Atomic-Level Insights into the Molecular Recognition of Anticancer Naphthoquinones by Human Serum Albumin: The Role of Apolar Side Chains in Binding Stability

Atomic-Level Insights into the Molecular Recognition of Anticancer Naphthoquinones by Human Serum Albumin: The Role of Apolar Side Chains in Binding Stability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Atomic-Level Insights into the Molecular Recognition of Anticancer Naphthoquinones by Human Serum Albumin: The Role of Apolar Side Chains in Binding Stability Flavio Kock, Erick Cirilo, Jesús Valdiviezo, Tiago Venancio This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8683311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Naphthoquinone derivatives, specifically 01 (lawsone; 2-hydroxynaphthalene-1,4-dione), 02 (lapachol; 2-hydroxy-3-(3-methylbut-2-enyl)naphthalene-1,4-dione), and 03 (2-hydroxy-3-styrylnaphthalene-1,4-dione), represent a versatile class of natural and synthetic molecules. These compounds hold significant potential as ligands for the development of novel anticancer metal complexes. In this study, we investigated the intermolecular interactions between these ligands and Human Serum Albumin (HSA), the primary protein responsible for drug transport in the human bloodstream. To achieve this, a synergistic approach was employed, combining NMR binding-target techniques, such as Saturation Transfer Difference (STD-NMR) and 2D-NOESY, with computational tools including molecular docking, molecular dynamics (MD) simulations, and binding affinity predictions. These methods allowed for a detailed characterization of the binding interface at the atomic level, defined as Group Epitope Mapping (GEM), while also enabling the estimation of the dissociation constants (K D​ ) for the resulting adducts. The results demonstrate that the presence of an unsaturated lateral chain significantly contributes to the stabilization of the supramolecular arrangement. Specifically, the experimental K D​ values, 7.00 mM for 01, 1.40 mM for 02, and 1.13 mM for 03, indicate that increasing the size and apolarity of the substituent leads to more efficient HSA interaction. Furthermore, 2D-NOESY experiments suggest that these naphthoquinones are spatially directed toward peripheral aliphatic domains (alanine, leucine, and valine). In agreement with these findings, the calculated docking scores follow a consistent trend (01 < 02 < 03), with derivative 03 approaching the binding affinity of the reference ligand, warfarin. Moreover, GNINA-based affinity predictions and CNN_VS scores further corroborate that derivatives bearing apolar lateral chains exhibit superior interaction profiles. Taken together, these unprecedented results establish that incorporating apolar substituents is a robust strategy for enhancing HSA binding. Ultimately, this study provides a valuable guideline for the rational design of naphthoquinone derivatives with optimized drug-delivery properties. Human Serum Albumin (HSA) Naphthoquinones STD-NMR Molecular Dynamics Molecular Recognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The pursuit of bioactive molecules for oncology remains a cornerstone in the design of next-generation drug candidates with enhanced efficacy and selectivity[ 1 , 2 ]. Among the various scaffolds under investigation, quinolones[ 3 – 5 ], flavones[ 6 , 7 ], and naphthoquinones[ 8 ] have emerged as particularly promising. In this regard, a highly effective strategy involves the synthesis of metal complexes using these molecules as ligands, a coordination approach that frequently yields derivatives with superior pharmacological profiles compared to the free ligands[ 9 – 11 ]. Within this framework, naphthoquinones such as lawsone (derivative 01, 2-hydroxy-1,4-naphthoquinone) have gained significant scientific attention[ 12 , 13 ]. Commonly isolated from Lawsonia alba , lawsone and its derivatives are currently being evaluated for their potential as chemotherapeutic agents[ 14 ]. Similarly, lapachol (derivative 02, 2-hydroxy-3-(3-methylbut-2-enyl)naphthalene-1,4-dione), typically sourced from the Bignoniaceae family, exhibits a broad spectrum of biological activities, including antiviral, antimicrobial, and anticancer properties[ 15 – 18 ]. Building upon these natural scaffolds, the synthetic derivative 2-hydroxy-3-styrylnaphthalene-1,4-dione (derivative 03)[ 19 ] is proposed here as a comparative model. The objective of including derivative 03 is to provide a proof-of-concept regarding the role of aliphatic and aromatic lateral chains in modulating intermolecular interactions with Human Serum Albumin (HSA). As the most abundant protein in human plasma, HSA plays a pivotal role in drug delivery by transporting metabolites and exogenous substances throughout the organism[ 20 – 22 ]. Because a detailed atomic-level understanding of ligand-HSA interactions is essential for the rational design of more potent drugs, this study investigates the binding mechanisms of derivatives 01–03 using a combination of NMR-based binding assays and computational modeling. Specifically, Saturation Transfer Difference (STD-NMR) was employed as a robust tool to distinguish bound from free ligands with high precision[ 23 , 24 ]. A major advantage of this technique is that it requires low protein concentrations without the need for isotopic labeling or prior structural knowledge, thus allowing for a "ligand-centered" analysis of the binding event[ 25 ]. Furthermore, STD-NMR facilitates the generation of Group Epitope Maps (GEM) and the estimation of dissociation constants (K D ​), providing quantitative chemical insights that are often inaccessible via other analytical methods under similar conditions[ 23 , 24 ]. To complement the epitope mapping, 2D-NOESY was utilized to accurately discriminate spatial contacts between the naphthoquinones and specific amino acid residues within the HSA structure[ 26 ]. By identifying these hydrogen-hydrogen proximities, it becomes possible to pinpoint the specific residues that contribute most to the stability of the complex[ 27 ]. In parallel with these experimental techniques, molecular docking was used to provide energetic insights into the binding mechanisms[ 20 , 27 – 29 ]. Indeed, the integration of NMR and docking is a well-established protocol in the literature for scrutinizing drug candidates for diseases such as Alzheimer’s[ 30 , 31 ] and cancer[ 8 ]. For instance, previous studies by Tanoli et al.[ 27 ] and Ermakova et al.[ 32 ] have successfully elucidated complex interaction mechanisms and identified specific binding sites within HSA, demonstrating the high relevance of this dual approach. To further bridge the gap between static models and biological reality, molecular dynamics (MD) simulations were performed to evaluate the conformational stability and temporal evolution of the protein–ligand complexes[ 33 , 34 ]. Moreover, predictive affinity models, such as GNINA, were integrated to enhance the interpretation of experimental data by providing quantitative scoring and binding probabilities. Collectively, these predictive tools validate the trends observed in NMR and docking, offering a comprehensive view of the interaction forces governing the supramolecular arrangement. In summary, this work utilizes STD-NMR to determine the binding epitopes and K D ​ values of derivatives 01–03, while 2D-NOESY measurements provide a reliable assessment of their spatial orientation within HSA. Finally, computational simulations delineate the stabilizing forces of the final adducts, establishing a robust guideline for the synthesis of naphthoquinone-based anticancer agents with optimized pharmacokinetic properties. 2. Experimental 2.1. Sample preparation Human Serum Albumin (HSA) was purchased from Sigma-Aldrich (Brazil) and used without further purification. To prepare the protein, a 50 µM stock solution was created by dissolving the lyophilized HSA in 75 mM deuterated phosphate buffer (99.9% D, Cambridge Isotope Laboratories, Inc.) adjusted to pH 7.2. This solution was subsequently aliquoted and stored at -20°C. Regarding the naphthoquinone derivatives, Derivative 01 was obtained commercially from Sigma-Aldrich, whereas Derivative 02 was isolated following the procedure described by Oliveira et al[ 35 ]. In contrast, Derivative 03 was synthesized according to the methodology reported by Demidoff et al[ 19 ]. Finally, all ligand stock solutions were prepared in a solvent system of 5:95% (v/v) DMSO-d 6 and deuterated phosphate buffer. 2.2. NMR Spectroscopy and STD-NMR Parameters All 1 H NMR and saturation transfer difference (STD-NMR) experiments were conducted in 5 mm NMR tubes (Norrell, Inc.) with a total volume of 500 µL. Initial experiments and saturation time-dependent studies were performed at a protein-to-ligand molar ratio of approximately 1:100. For Group Epitope Mapping (GEM), final concentrations were fixed at 30 µM for HSA and 5 mM for each derivative. In concentration-dependent studies, the HSA concentration was maintained at 30 µM while ligand concentrations were varied from 1 to 5 mM. To ensure statistical reproducibility, seven independent STD-NMR experiments were recorded for each derivative. Spectra were acquired on a Bruker Avance III 600 MHz spectrometer equipped with a TXI cryoprobe and z-axis gradients. Data processing was performed using Bruker Topspin 3.6 pl. 7. 2.3. STD-NMR Data Analysis Selective protein saturation was achieved using a train of 50 ms Gaussian-shaped pulses (1% truncation, 45–55dB attenuation) separated by 2 ms delays. On- and off-resonance frequencies were set to -300 Hz and 18,000 Hz, respectively. Saturation times (t sat ) ranged from 0.5 to 10 s. The STD amplification factor (A STD ​) was calculated according to Eq. 1 [ 25 ]: $$\:{A}_{STD}=\:\frac{{I}_{0}-{I}_{STD}}{{I}_{0}}\:\times\:\:\frac{\left[L\right]}{\left[P\right]}\:$$ 1 where I 0​ is the intensity of the off-resonance signal, I STD​ is the intensity of the STD signal, and [L]/[P]​ represents the ligand-to-protein ratio. For GEM, the signal with the highest integral was normalized to 100%. Dissociation constants (K D ​) were determined by fitting the concentration-dependent data to a Michaelis-Menten-like growth curve (Eq. 2 )[ 25 ] using Origin 8.0: $$\:{A}_{STD}=\:\frac{{\alpha\:}_{STD}\left[L\right]}{{K}_{D}+\left[L\right]}\:$$ 2 2.4. 2D-NOESY Experiments Samples for 2D-NOESY were prepared in a deuterated buffer (pH 7.0) at the highest concentrations of both ligand and protein to ensure sufficient signal-to-noise. Parameters included a mixing time of 500 ms, a spectral window of 9615.38 Hz, 44 transients, and 1024 points. A relaxation delay of 3.0 s and a pre-scan delay of 10 µs were employed. Data were processed using a squared cosine window function, zero-filling, and Fourier transformation to yield 1K × 1K matrices. 2.5. Computational Methods 2.5.1. Molecular Docking The crystal structure of HSA bound to R-(+)-warfarin (PDB ID: 1H9Z)[ 36 ] served as the receptor. Prior to docking, crystallographic ligands and water molecules were removed, polar hydrogens were added, and Gasteiger charges were assigned. Ligand geometries were generated from SMILES and optimized using the ANI-2x3 machine-learning potential. Simulations were performed using Uni-Dock with an exhaustiveness of 512. The grid box (15×15×15 Å) was centered at coordinates (32.79, 13.53, 9.61) Å, corresponding to the known active site. 2.5.2. Molecular Dynamics The best docking pose obtained from Uni-Dock was selected for molecular dynamics simulations. The complex protein-ligand was solvated in a TIP3P[ 37 ] water box (Amber ff19SB[ 38 , 39 ] for the protein and GAFF2[ 39 , 40 ] for the ligand) and neutralized to a 0.15 M ionic strength. After energy minimization performed in 25 000 steps, the system was gradually heated from 0 to 300 K over 500 ps under constant volume (NVT) conditions using a Langevin thermostat with a collision frequency of 2 ps − 1 and a positional restraint of 50 kJ.mol − 1 .Å −2 applied to the protein and ligand heavy atoms to maintain structural stability during heating, while solvent and ions were allowed to move freely. Subsequently, an NPT equilibration of 500 ps at 300 K and 1 atm was carried out using isotropic position scaling and a barostat coupling constant of 5 ps with a positional restraint of 50 kJ.mol − 1 .Å −2 . The solvent molecules and ions were kept unrestrained, allowing the system density to equilibrate under constant-pressure conditions. The complexes were subjected to MD simulations using OpenMM[ 41 ] with a 2 fs integration timestep. The simulation was run for a total of 55 ns, with coordinates saved every 5 ps. The first 5 ns were discarded from the analysis and considered as an additional equilibration period to ensure full stabilization of the protein–ligand system and proper relaxation of noncovalent interactions prior to data collection. The MD trajectory was subsequently evaluated through docking analysis with the GNINA[ 42 ] scoring framework. For each frame, GNINA affinity scores and convolutional neural network (CNN)–based scores were obtained. The CNN GNINA scoring function provides two primary metrics: CNNscore, a binary pose-quality classifier representing the probability that the predicted pose is within 2 Å RMSD of the true binding mode, and CNNaffinity, a predicted binding affinity expressed as a pK value. A third metric, the CNN_VS score, defined as the product of CNNscore and CNNaffinity, integrates pose confidence and affinity into a single ranking criterion[ 43 ]. This CNN_VS score was used to evaluate the ligand poses generated from the MD trajectory, as it has previously demonstrated strong retrospective virtual screening performance[ 43 , 44 ]. 2.5.3. Binding Affinity Prediction The predictive model Boltz-2 implemented in Rowan Scientific online platform[ 45 ] was used to estimate the affinity values (IC 50 ) for the protein–ligand complexes formed between HSA and derivatives 01, 02, and 03. A pocket restriction was applied, limiting ligand placement within 4 Å of residue ARG257, which interacts with all three derivatives according to molecular docking simulations. However, reproducible affinity values could not be obtained for warfarin, since its chirality could not be properly encoded in SMILES format. Only predictions with affinity probabilities greater than 0.5 were considered for analysis. 3. Results and Discussion Initial assessments of the interactions between the naphthoquinone derivatives and HSA were conducted using 1 H NMR spectroscopy. Figures 1 a–c illustrate the spectra of the ligands in the presence (red) and absence (blue) of the protein. The addition of HSA resulted in marked line broadening, a loss of J-coupling definition and splitting patterns, significant chemical shift displacements, and baseline distortions. These observations strongly suggest a significant intermolecular interaction between the naphthoquinones and the protein in the liquid state. Similar spectral patterns were observed by Milagre et al. during the study of cephalosporin and penicillin interactions with HSA [ 46 ]. Broadening of NMR signals is widely recognized as primary evidence for the association between small molecules and biological targets. This phenomenon is typically attributed to a decrease in the effective transverse relaxation time (T 2 ∗ ​) resulting from an increase in the rotational correlation time (τ c​ ) upon binding. Mathematically , T 2 ∗ ​is inversely proportional to the linewidth at half-height (Δν 1/2 ​) ( Eq. 3 ): $$\:{\varDelta\:\nu\:}_{\frac{1}{2}}\propto\:\frac{1}{\pi\:{T}_{2}^{*}}$$ 3 While monitoring Δν 1/2 ​ as a function of HSA concentration has been used to study drugs like phenytoin and naproxen[ 47 ], conclusions based solely on line broadening can be premature, as these changes may also arise from non-specific viscosity increases. Consequently, more robust techniques such as STD-NMR and 2D-NOESY were employed to validate the binding. Thus, to obtain the most favourable interaction site, the use of STD-NMR is essential. This approach is supported on NOE (Nuclear Overhauser Effect) transference from HSA to naphthoquinones candidates [ 25 , 46 , 48 ]. This experiment consists of the application of a radiofrequency pulse on a spectral region that contains only the signals attributed to HSA ; this experiment is denominated on-resonance . The magnetization received by the HSA molecule is transferred to the entire protein, by a process well-described as spin diffusion, which occurs through the bonds on the macromolecule (HSA, in this case) until to naphthoquinone derivative under scrutiny. Therefore, the hydrogen belonging to the naphthoquinone closer to HSA, will receive the most intense magnetization, and consequently, the amplitude for the observed signals will be higher. Additionally, an off-resonance (I off ) spectrum is acquired. In this experiment, there is the irradiation of a radiofrequency pulse in a region that does not have signals attributed to HSA. Finally, the saturation degrees are calculated, determining the individual signal intensities in the STD spectrum (I off ). This relative amount is calculated using Eq. 1 and expressed as a percentual of the most intense saturation transfer observed for a set of protons (as indicated by the colored highlights in the Fig. 2 ), and as consequence, the proximity of these protons to the HSA surface. The Group Epitope Mapping (GEM) analysis reveals distinct binding modes for each derivative. For derivative 01, the interaction with HSA is dominated almost exclusively by the aromatic system, with these protons exhibiting a high saturation transfer (average AF ≈ 98.4%). Interestingly, the single proton positioned outside the aromatic scaffold (H1) also contributes significantly, showing a relative interaction of approximately 35.3%. In contrast, the binding epitope of derivative 02 shifts toward the aliphatic region; here, the methyl protons (H1 and H2) are positioned closer to the HSA surface (AF ≈ 81.4%) than the aromatic protons. Furthermore, the methylidene proton (H3) displays a similar influence on the binding stability (AF ≈ 56.8%), whereas the methylene group (H4) appears to be oriented away from the protein surface (AF ≈ 31.5%), indicating a minor contribution to the overall stabilization. Finally, for derivative 03, the combination of an aliphatic branch and an additional aromatic interaction site provides optimal conditions for molecular recognition, resulting in an exceptionally efficient interface where all protons exhibit AF values exceeding 92.0%. Collectively, these results suggest that an increased degree of unsaturation and hydrophobicity in the side chain is highly beneficial for a more effective and robust interaction with HSA. To validate the hypothesis that a high degree of unsaturation enhances binding efficiency, concentration-dependent STD-NMR experiments were performed to determine the dissociation constants (K D​ ) for each ligand (Fig. 3 ). These results confirm that the presence of an unsaturated branch external to the aromatic core provides a more effective interaction site, thereby imparting higher stability to the resulting supramolecular adduct. The average dissociation constants obtained for derivative 01 (K D ​ ≈ 7.00 mM), derivative 02 (K D ≈ 1.40 mM), and derivative 03 (K D ​≈ 1.13 mM) follow a consistent trend. Specifically, these values demonstrate that the incorporation of an apolar group is essential for strengthening the interaction with HSA. Consequently, this structural modification appears to be a key factor in optimizing the pharmacokinetic profiles and drug-delivery potential of these naphthoquinone derivatives. Additionally, Fig. 4 displays the 2D-NOESY correlation maps for all derivatives in the presence of HSA. These spectra reveal significant cross-peaks in the region corresponding to correlations between the aromatic signals of the naphthoquinones and the aliphatic signals of the HSA protein. These findings confirm that all investigated naphthoquinones exhibit a high degree of spatial proximity toward apolar amino acid motifs. Furthermore, these results are in excellent agreement with literature reports, which indicate that the most efficient binding sites are the hydrophobic domains of HSA. Specifically, this behavior is consistent with binding at Site I (Subdomain IIA), a pocket characterized by a high density of leucine, isoleucine, and alanine residues, which provide a favorable environment for the stabilization of hydrophobic ligands[ 28 , 29 , 49 ]. Furthermore, molecular docking simulations were performed to elucidate and corroborate the nature of the intermolecular interactions between the naphthoquinone derivatives and HSA. In this computational approach, the warfarin molecule was employed as a structural benchmark, given its chemical similarity to the naphthoquinone scaffold. Warfarin is well-characterized by its high affinity for HSA (K D​ ≈ 3 µM) and its preferential binding to Sudlow’s Site I (Subdomain IIA). This specific region is characterized by a high density of both aliphatic and aromatic amino acid residues, providing an ideal hydrophobic pocket that mirrors the environment suggested by our experimental NMR data. Consequently, utilizing warfarin as a reference ligand allows for a direct comparison of the binding orientations and stabilization energies of the new derivatives within the same protein domain[ 50 , 51 ]. First, it was verified that the best docking pose obtained for R-warfarin (Fig. 5 a) closely matched the crystallographic conformation observed in the 1H9Z structure (RMSD = 0.757 Å). This result confirms that the docking protocol reliably reproduced the experimental binding mode. The best docking poses (Figs. 5 a, 6 a, 6 c, 6 e) revealed binding scores of − 6.934 kcal/mol for derivative 01, − 7.741 kcal/mol for derivative 02, − 8.196 kcal/mol for derivative 03, and − 8.760 kcal/mol for warfarin. Warfarin displayed hydrogen bond interactions with residue ARG220 and a π–π stacking interaction with TRP212, consistent with its known high affinity for this binding site. Derivative 01 (D01) formed hydrogen bonds with residue ARG255; however, its smaller molecular size and the large cavity of the binding site lead to a less favorable stabilization, as reflected in its lower docking score. This observation is in good agreement with the experimental group epitope mapping data (Fig. 2 ), where the H1 proton of derivative 01 shows a low relative proximity, consistent with its orientation toward a highly polar amino acid environment in the docking pose (Fig. 6 a). Derivative 02 (D02), located further from residues ARG220 and ARG255, interacts mainly through hydrophobic contacts with LEU217 and LEU236, which explains its moderate binding affinity. Accordingly, the side-chain protons of derivative 02 exhibit higher comparative proximity percentages due to their closer contact with nonpolar residues, whereas the aromatic protons display intermediate proximity values (around 50%), reflecting their partial exposure to polar groups within the binding site. In contrast, derivative 03 (D03) exhibits hydrogen bonding with ARG220 and a π–π stacking interaction with TRP212, positioning it as the derivative with the most favorable binding score. In this case, the docking pose reveals hydrophobic interactions comparable to those observed for derivative 02, together with a greater exposure of the naphthoquinone aromatic ring to nonpolar residues, which accounts for the near-100% comparative proximity values observed in this region. Furthermore, the high proximity values associated with the terminal phenyl group are consistent with the π–π stacking interaction with TRP212. The docking scores correlate well with the experimental K D values obtained from NMR studies, namely 7.00 mM for derivative 01, 1.40 mM for derivative 02, and 1.13 mM for derivative 03. Figure 7a shows the RMSD profiles of the four studied compounds during 50 ns of molecular dynamics simulation. Overall, Derivative 01 exhibited the most pronounced fluctuations; this behavior can be attributed to its smaller molecular size, which provides greater conformational freedom within the HSA binding pocket, as previously discussed. When analyzing the average RMSD values in relation to the affinities toward HSA, a clear trend is observed. The derivatives showed progressively higher RMSD averages (Fig. 7a, 4.013 Å (Derivative 01), 2.301 Å (Derivative 02), and 0.925 Å (Derivative 03), which inversely correlate with their experimentally determined dissociation constants of 7.00 mM, 1.40 mM, and 1.13 mM, respectively. This inverse relationship suggests that lower RMSD values are associated with increased structural stability of the protein–ligand complex. Nevertheless, this interpretation should be considered with caution, as RMSD is also influenced by the intrinsic flexibility of the ligand. In this regard, warfarin, which possesses a largely saturated and flexible side chain, exhibits higher conformational variability compared to the more rigid scaffold of derivative 03, potentially leading to larger RMSD fluctuations that do not necessarily reflect weaker binding. Therefore, the observed RMSD trend likely reflects a combination of enhanced van der Waals and π–π interactions together with differences in ligand rigidity, rather than RMSD serving as a standalone descriptor of binding affinity. Figure 7 . (a) Root-mean-square deviation (RMSD) profiles of warfarin and the three naphthoquinone derivatives bound to HSA over 50 ns of molecular dynamics simulation. (b) MD evaluation of warfarin and the naphthoquinone derivatives bound to HSA by CNN_VS scores. Figure 7b summarizes the post-docking and molecular dynamics–based evaluation of the binding behavior of warfarin and the naphthoquinone derivatives. Figure S2 presents the GNINA binding scores (kcal/mol), which highlight the reduced affinity of derivative 01. Based solely on these scores, derivative 03 would appear to display a higher affinity for HSA than warfarin itself; however, this trend is not fully consistent with the experimental binding data. In contrast, evaluation of the CNN_VS scores yields a ranking that is more consistent with the experimentally determined dissociation constants (K D ), supporting the use of this metric as a more reliable indicator of binding performance in this system.[ 43 , 44 ] Overall, the combined analysis of RMSD behavior, GNINA scores, CNN-based ranking, and experimental NMR-derived K D values provides a coherent and balanced interpretation of ligand–HSA interactions. The IC 50 values obtained from Boltz-2 simulations with pocket restraints for the naphthoquinone derivatives are consistent with the results from docking and molecular dynamics studies, as shown in Table 2 . This agreement supports the reliability of the computational workflow, indicating that derivatives with stronger predicted affinities (lower IC 50 values) also displayed more stable binding modes and lower RMSD values during molecular dynamics simulations. Such consistency reinforces the hypothesis that the lateral chain plays a critical role in stabilizing the ligand–protein interactions through van der Waals and π–π contacts. Table 2 Predicted inhibitory activities (IC 50 ) and affinity probabilities for the studied naphthoquinone derivatives against HSA. Ligand IC 50 (µM) St. Dev. (µM) Affinity probability Derivative 01 21.55 3.42 0.558 Derivative 02 5.74 1.06 0.625 Derivative 03 1.78 0.17 0.679 4. Conclusions Herein we have used STD-NMR, 2D-NOESY , molecular docking and molecular dynamics to understand the biological interaction between naphthoquinones derivatives and HSA aiming the further design of potential anticancer drugs with improved drug delivery features. Given the results, some conclusions can be supplied: (i) The interaction between naphthoquinones and HSA occurs through weak spatial contacts by Van der Waals forces; (ii) the presence of an unsaturated chain outside the aromatic cage turns the intermolecular interaction more favorable, and (iii) 2D-NOESY and molecular docking results demonstrates that the addressed naphthoquinones have more attraction for sites rich in aliphatic residues within HSA arrangement, putting light on the reasons about its interactions towards this protein, which is a relevant biological feature for the design of more effective anticancer drug delivery candidates. Furthermore, molecular dynamics simulations demonstrated that the side chain plays a key role in maintaining stable contacts with residues such as ARG220 and TRP212, particularly in the case of the derivative 03, which exhibited the lowest average RMSD (0.925 Å) and the highest binding affinity (K D ≈ 1.13 mM). Taken together, the combined experimental and computational results consistently identify derivative 03 as the most promising ligand, highlighting the importance of side-chain architecture in promoting persistent hydrophobic contacts and π–π stacking interactions with TRP212. These features contribute to enhanced complex stability, are in agreement with GNINA-based docking ranking metrics and Boltz-2 IC₅₀ predictions, and underscore the relevance of structural rigidity and aromatic lateral groups in optimizing HSA binding at the warfarin site. Overall, this integrated approach provides a robust framework for guiding the future design of naphthoquinone-based anticancer drug delivery systems. Declarations Acknowledgements The authors of this manuscript are gratefully acknowledging the financial support from FAPESP (2018/16040-5 and 2018/09145-5) and by the Vicerrectorado de Investigacion (VRI) at the Pontificia Universidad Catolica del Peru (PUCP) through grant DRI-2025-1290 and DRI-2025-1287. EC and JV are thankful to IA-PUCP for the computational resources provided. Author contributions F.K. and J.V. contributed equally to the study’s conceptualization and served as lead contributors for the formal analysis and investigation. F.K. led the writing of the original draft and contributed equally to its review and editing. J.V. served as lead for the review and editing process. E.C. contributed equally to conceptualization and formal analysis, while T.V.provided equal contributions to the formal analysis and original draft preparation. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. 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Angew Chem Int Ed Engl 38(19990614):1784–1788. https://doi.org/10.1002/(SICI)1521-3773 . )38:12%253C1784::AID-ANIE1784%253E3.0.CO;2-Q Viegas A, Manso J, Nobrega FL, Cabrita EJ (2011) Saturation-Transfer Difference (STD) NMR: A Simple and Fast Method for Ligand Screening and Characterization of Protein Binding. J Chem Educ 88:990–994. https://doi.org/10.1021/ed101169t Claridge TDW (2016) Chap. 9 - Correlations Through Space: The Nuclear Overhauser Effect. In: Claridge TDW (ed) High-Resolution NMR Techniques in Organic Chemistry (Third Edition). Elsevier, Boston, pp 315–380 Tanoli NU, Tanoli SAK, Ferreira AG et al (2015) Evaluation of binding competition and group epitopes of acetylcholinesterase inhibitors by STD NMR, Tr-NOESY, DOSY and molecular docking: an old approach but new findings. MedChemComm 6:1882–1890. https://doi.org/10.1039/C5MD00231A Ma R, Pan H, Shen T et al (2017) Interaction of Flavonoids from Woodwardia unigemmata with Bovine Serum Albumin (BSA): Application of Spectroscopic Techniques and Molecular Modeling Methods. Molecules 22. https://doi.org/10.3390/molecules22081317 Takehara K, Yuki K, Shirasawa M et al (2009) Binding Properties of Hydrophobic Molecules to Human Serum Albumin Studied by Fluorescence Titration. Anal Sci 25:115–120. https://doi.org/10.2116/analsci.25.115 Campora M, Canale C, Gatta E et al (2021) Multitarget Biological Profiling of New Naphthoquinone and Anthraquinone-Based Derivatives for the Treatment of Alzheimer’s Disease. ACS Chem Neurosci 12:447–461. https://doi.org/10.1021/acschemneuro.0c00624 Viswanathan GK, Paul A, Gazit E, Segal D (2019) Naphthoquinone Tryptophan Hybrids: A Promising Small Molecule Scaffold for Mitigating Aggregation of Amyloidogenic Proteins and Peptides. Front Cell Dev Biol 7. https://doi.org/10.3389/fcell.2019.00242 Ermakova EA, Danilova AG, Khairutdinov BI (2020) Interaction of ceftriaxone and rutin with human serum albumin. WaterLOGSY-NMR and molecular docking study. J Mol Struct 1203:127444. https://doi.org/10.1016/j.molstruc.2019.127444 Ashraf GM, Gupta DD, Alam MZ et al (2022) Unravelling Binding of Human Serum Albumin with Galantamine: Spectroscopic, Calorimetric, and Computational Approaches. ACS Omega 7:34370–34377. https://doi.org/10.1021/acsomega.2c04004 Yeggoni DP, Gokara M, Manidhar DM et al (2014) Binding and molecular dynamics studies of 7-hydroxycoumarin derivatives with human serum albumin and its pharmacological importance. Mol Pharm 11:1117–1131. https://doi.org/10.1021/mp500051f Oliveira KM, Liany L-D, Corrêa RS et al (2017) Selective Ru(II)/lawsone complexes inhibiting tumor cell growth by apoptosis. J Inorg Biochem 176:66–76. https://doi.org/10.1016/j.jinorgbio.2017.08.019 Petitpas I, Bhattacharya AA, Twine S et al (2001) Crystal structure analysis of warfarin binding to human serum albumin: anatomy of drug site I. J Biol Chem 276:22804–22809. https://doi.org/10.1074/jbc.M100575200 Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. https://doi.org/10.1063/1.445869 Tian C, Kasavajhala K, Belfon KAA et al (2020) ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J Chem Theory Comput 16:528–552. https://doi.org/10.1021/acs.jctc.9b00591 Case DA, Aktulga HM, Belfon K et al (2023) AmberTools. J Chem Inf Model 63:6183–6191. https://doi.org/10.1021/acs.jcim.3c01153 He X, Man VH, Yang W et al (2020) A fast and high-quality charge model for the next generation general AMBER force field. J Chem Phys 153:114502. https://doi.org/10.1063/5.0019056 Eastman P, Swails J, Chodera JD et al (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Comput Biol 13:e1005659. https://doi.org/10.1371/journal.pcbi.1005659 McNutt AT, Francoeur P, Aggarwal R et al (2021) GNINA 1.0: molecular docking with deep learning. J Cheminformatics 13:43. https://doi.org/10.1186/s13321-021-00522-2 Dunn I, Pirhadi S, Wang Y et al (2024) CACHE Challenge #1: Docking with GNINA Is All You Need. J Chem Inf Model 64:9388–9396. https://doi.org/10.1021/acs.jcim.4c01429 Sunseri J, Koes DR (2021) Virtual Screening with Gnina 1.0. Molecules 26:7369. https://doi.org/10.3390/molecules26237369 Rowan Scientific (2025) https://www.rowansci.com Milagre CDF, Cabeça LF, Martins LG, Marsaioli AJ (2011) STD NMR spectroscopy: a case study of fosfomycin binding interactions in living bacterial cells. J Braz Chem Soc 22:286–291. https://doi.org/10.1590/S0103-50532011000200014 Shortridge MD, Hage DS, Harbison GS, Powers R (2008) Estimating Protein–Ligand Binding Affinity Using High-Throughput Screening by NMR. J Comb Chem 10:948–958. https://doi.org/10.1021/cc800122m Monaco S, Tailford LE, Juge N, Angulo J (2017) Differential Epitope Mapping by STD NMR Spectroscopy To Reveal the Nature of Protein–Ligand Contacts. Angew Chem 129:15491–15495. https://doi.org/10.1002/ange.201707682 Russell BA, Kubiak-Ossowska K, Mulheran PA et al (2015) Locating the nucleation sites for protein encapsulated gold nanoclusters: a molecular dynamics and fluorescence study. Phys Chem Chem Phys 17:21935–21941. https://doi.org/10.1039/C5CP02380G Ma R, Pan H, Shen T et al (2017) Interaction of Flavonoids from Woodwardia unigemmata with Bovine Serum Albumin (BSA): Application of Spectroscopic Techniques and Molecular Modeling Methods. Molecules 22. https://doi.org/10.3390/molecules22081317 Petitpas I, Bhattacharya AA, Twine S et al (2001) Crystal Structure Analysis of Warfarin Binding to Human Serum Albumin: ANATOMY OF DRUG SITE I*. J Biol Chem 276:22804–22809. https://doi.org/10.1074/jbc.M100575200 Additional Declarations No competing interests reported. Supplementary Files JournalofComputerAidedMolecularDesignFinalSupportingInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8683311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584163389,"identity":"502bc487-960d-4d82-ad90-9b398ceb3a87","order_by":0,"name":"Flavio Kock","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9nYgkcDAYMDAwMb4ACpogFcLz2GEFmaYUiK0QFSxsUkQp4WZ+ZnEgwoGY372Y2nVhW12eQzszdskGHfY4NHCZiaRcIbBTLIn7djtmW3JxQw8x8okGM+k4dRiz8xgbJDYxmBjcIO97TZv24HEBokcMwnGtsN4bGH/DNdSDNYi/wak5T8eLTyGD4BazAxusB1jhtjCA9JyAJ+WwgcJZySMgX5Jlp5xLrmYjSet2CLxTDJuLeztGw7+qLAx7Gc/Zvi5oMwuj5/98MYbH3fY4dQCBZAYYQbiBDYQK7GBkA4oAGsBsxiJ1TIKRsEoGAUjAQAAlPVIgrjaM+IAAAAASUVORK5CYII=","orcid":"","institution":"Pontifical Catholic University of Peru","correspondingAuthor":true,"prefix":"","firstName":"Flavio","middleName":"","lastName":"Kock","suffix":""},{"id":584163391,"identity":"48700851-99c3-4e77-a2e9-e13bcdd39659","order_by":1,"name":"Erick Cirilo","email":"","orcid":"","institution":"Pontifical Catholic University of Peru","correspondingAuthor":false,"prefix":"","firstName":"Erick","middleName":"","lastName":"Cirilo","suffix":""},{"id":584163393,"identity":"eb8968cf-34a7-4c11-8081-1da7bd51cc66","order_by":2,"name":"Jesús Valdiviezo","email":"","orcid":"","institution":"Pontifical Catholic University of Peru","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Valdiviezo","suffix":""},{"id":584163394,"identity":"9ae84a5c-e1ff-467a-89b1-4b46c64464a1","order_by":3,"name":"Tiago Venancio","email":"","orcid":"","institution":"Federal University of São Carlos","correspondingAuthor":false,"prefix":"","firstName":"Tiago","middleName":"","lastName":"Venancio","suffix":""}],"badges":[],"createdAt":"2026-01-24 02:53:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8683311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8683311/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101797988,"identity":"4895f970-4b93-4f05-82fd-70de13fa188c","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":502170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eH-NMR spectra for all naphthoquinones (derivative 1 (a), derivative 02 (b) and derivative 03 (c)), in presence (red) and absence (blue) of HSA. The numbers in black correspond to the\u0026nbsp; identification for all the hydrogens within the chemical structure.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/b242ec79f9a5b4e52c35c8d0.png"},{"id":101797989,"identity":"5c6780da-d412-4927-ba75-dc095347758d","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1027055,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular structures and Group Epitope Mapping (GEM) of naphthoquinone derivatives 01 (a), 02 (b), and 03 (c) interacting with HSA. Values represent the relative saturation percentages (%) determined at a saturation time (t\u003csub\u003esat\u003cbr\u003e\n\u003c/sub\u003e) of 6.0 s, indicating the proximity of specific proton chemical environments to the protein surface.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/06ea735aae480b016dbd244f.png"},{"id":101880898,"identity":"01f246a7-f1e3-46b4-b268-ec51b6392254","added_by":"auto","created_at":"2026-02-04 15:07:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1043549,"visible":true,"origin":"","legend":"\u003cp\u003eSTD-NMR titration experiments for the derivative 01 (a), derivative 02 (b) and derivative 03 (c) used for the calculations of average dissociation constants (K\u003csub\u003eD\u003c/sub\u003e) values for its respective interaction towards HSA.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/127e00821042fdb602d9b34d.png"},{"id":101797992,"identity":"aee52613-bf16-4de7-ac49-5cf73f774df7","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":749090,"visible":true,"origin":"","legend":"\u003cp\u003eIntermolecular 2D-NOESY correlation maps for derivatives 01 (a), 02 (b), and 03 (c) in the presence of HSA. The cross-peaks illustrate the spatial proximity between the naphthoquinone protons and the hydrophobic amino acid side chains, confirming a binding preference for the apolar domains within the HSA scaffold.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/aa07dd5e4e84463beddcba92.png"},{"id":102745221,"identity":"d11de9fe-7937-456e-80b7-4c20d4e871af","added_by":"auto","created_at":"2026-02-16 08:44:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1034949,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Comparison between the best docking pose of R-warfarin (cyan) and its crystallographic conformation from the 1H9Z structure (purple). (b) Interaction diagram, green indicates aliphatic interactions, blue basic residues, cyan polar residues, and purple aromatic interactions\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/5d4f9b902e1170ef8efd9b6b.png"},{"id":101797990,"identity":"681c9710-1c84-4516-a1b9-93f17867b84c","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2839632,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking poses of the naphthoquinone derivatives bound to HSA. (a) Derivative 01, (c) derivative 02, and (e) derivative 03. The corresponding protein–ligand interaction maps are shown in (b), (d), and (f), respectively. In these interaction diagrams, green indicates aliphatic interactions, blue basic residues, cyan polar residues, and purple aromatic interactions.\u003c/p\u003e","description":"","filename":"61.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/9c750162b268b8d1f91acebe.png"},{"id":101797993,"identity":"b68bde0d-8e2b-41f4-a8f3-6a2d05e625cc","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":567132,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Root-mean-square deviation (RMSD) profiles of warfarin and the three naphthoquinone derivatives bound to HSA over 50 ns of molecular dynamics simulation. (b) MD evaluation of warfarin and the naphthoquinone derivatives bound to HSA by CNN_VS scores.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/0161b5c16c2a74569fb8917d.png"},{"id":104780169,"identity":"ece470d9-6a94-49d5-bf11-c7b67c8be199","added_by":"auto","created_at":"2026-03-17 07:51:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9269887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/903f21db-44c3-46d6-9ac8-57a5c7fb3e23.pdf"},{"id":101797994,"identity":"86619b25-94fc-41e3-9dd3-0a2e7e4b7449","added_by":"auto","created_at":"2026-02-03 17:16:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4255846,"visible":true,"origin":"","legend":"","description":"","filename":"JournalofComputerAidedMolecularDesignFinalSupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8683311/v1/de2d2fe0da92d1a16c346d37.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atomic-Level Insights into the Molecular Recognition of Anticancer Naphthoquinones by Human Serum Albumin: The Role of Apolar Side Chains in Binding Stability","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe pursuit of bioactive molecules for oncology remains a cornerstone in the design of next-generation drug candidates with enhanced efficacy and selectivity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among the various scaffolds under investigation, quinolones[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], flavones[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and naphthoquinones[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have emerged as particularly promising. In this regard, a highly effective strategy involves the synthesis of metal complexes using these molecules as ligands, a coordination approach that frequently yields derivatives with superior pharmacological profiles compared to the free ligands[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin this framework, naphthoquinones such as lawsone (derivative 01, 2-hydroxy-1,4-naphthoquinone) have gained significant scientific attention[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Commonly isolated from \u003cem\u003eLawsonia alba\u003c/em\u003e, lawsone and its derivatives are currently being evaluated for their potential as chemotherapeutic agents[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, lapachol (derivative 02, 2-hydroxy-3-(3-methylbut-2-enyl)naphthalene-1,4-dione), typically sourced from the \u003cem\u003eBignoniaceae\u003c/em\u003e family, exhibits a broad spectrum of biological activities, including antiviral, antimicrobial, and anticancer properties[\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Building upon these natural scaffolds, the synthetic derivative 2-hydroxy-3-styrylnaphthalene-1,4-dione (derivative 03)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] is proposed here as a comparative model. The objective of including derivative 03 is to provide a proof-of-concept regarding the role of aliphatic and aromatic lateral chains in modulating intermolecular interactions with Human Serum Albumin (HSA). As the most abundant protein in human plasma, HSA plays a pivotal role in drug delivery by transporting metabolites and exogenous substances throughout the organism[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause a detailed atomic-level understanding of ligand-HSA interactions is essential for the rational design of more potent drugs, this study investigates the binding mechanisms of derivatives 01\u0026ndash;03 using a combination of NMR-based binding assays and computational modeling. Specifically, Saturation Transfer Difference (STD-NMR) was employed as a robust tool to distinguish bound from free ligands with high precision[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A major advantage of this technique is that it requires low protein concentrations without the need for isotopic labeling or prior structural knowledge, thus allowing for a \"ligand-centered\" analysis of the binding event[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, STD-NMR facilitates the generation of Group Epitope Maps (GEM) and the estimation of dissociation constants (K\u003csub\u003eD\u003c/sub\u003e​), providing quantitative chemical insights that are often inaccessible via other analytical methods under similar conditions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo complement the epitope mapping, 2D-NOESY was utilized to accurately discriminate spatial contacts between the naphthoquinones and specific amino acid residues within the HSA structure[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By identifying these hydrogen-hydrogen proximities, it becomes possible to pinpoint the specific residues that contribute most to the stability of the complex[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In parallel with these experimental techniques, molecular docking was used to provide energetic insights into the binding mechanisms[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Indeed, the integration of NMR and docking is a well-established protocol in the literature for scrutinizing drug candidates for diseases such as Alzheimer\u0026rsquo;s[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and cancer[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, previous studies by Tanoli et al.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Ermakova et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] have successfully elucidated complex interaction mechanisms and identified specific binding sites within HSA, demonstrating the high relevance of this dual approach.\u003c/p\u003e \u003cp\u003eTo further bridge the gap between static models and biological reality, molecular dynamics (MD) simulations were performed to evaluate the conformational stability and temporal evolution of the protein\u0026ndash;ligand complexes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, predictive affinity models, such as GNINA, were integrated to enhance the interpretation of experimental data by providing quantitative scoring and binding probabilities. Collectively, these predictive tools validate the trends observed in NMR and docking, offering a comprehensive view of the interaction forces governing the supramolecular arrangement.\u003c/p\u003e \u003cp\u003eIn summary, this work utilizes STD-NMR to determine the binding epitopes and K\u003csub\u003eD\u003c/sub\u003e​ values of derivatives 01\u0026ndash;03, while 2D-NOESY measurements provide a reliable assessment of their spatial orientation within HSA. Finally, computational simulations delineate the stabilizing forces of the final adducts, establishing a robust guideline for the synthesis of naphthoquinone-based anticancer agents with optimized pharmacokinetic properties.\u003c/p\u003e"},{"header":"2. Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSample preparation\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eHuman Serum Albumin (HSA) was purchased from Sigma-Aldrich (Brazil) and used without further purification. To prepare the protein, a 50 \u0026micro;M stock solution was created by dissolving the lyophilized HSA in 75 mM deuterated phosphate buffer (99.9% D, Cambridge Isotope Laboratories, Inc.) adjusted to pH 7.2. This solution was subsequently aliquoted and stored at -20\u0026deg;C. Regarding the naphthoquinone derivatives, Derivative 01 was obtained commercially from Sigma-Aldrich, whereas Derivative 02 was isolated following the procedure described by Oliveira et al[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In contrast, Derivative 03 was synthesized according to the methodology reported by Demidoff et al[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally, all ligand stock solutions were prepared in a solvent system of 5:95% (v/v) DMSO-d\u003csub\u003e6\u003c/sub\u003e and deuterated phosphate buffer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. NMR Spectroscopy and STD-NMR Parameters\u003c/h2\u003e \u003cp\u003eAll \u003csup\u003e1\u003c/sup\u003eH NMR and saturation transfer difference (STD-NMR) experiments were conducted in 5 mm NMR tubes (Norrell, Inc.) with a total volume of 500 \u0026micro;L. Initial experiments and saturation time-dependent studies were performed at a protein-to-ligand molar ratio of approximately 1:100. For Group Epitope Mapping (GEM), final concentrations were fixed at 30 \u0026micro;M for HSA and 5 mM for each derivative. In concentration-dependent studies, the HSA concentration was maintained at 30 \u0026micro;M while ligand concentrations were varied from 1 to 5 mM. To ensure statistical reproducibility, seven independent STD-NMR experiments were recorded for each derivative. Spectra were acquired on a Bruker Avance III 600 MHz spectrometer equipped with a TXI cryoprobe and z-axis gradients. Data processing was performed using Bruker Topspin 3.6 pl. 7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. STD-NMR Data Analysis\u003c/h2\u003e \u003cp\u003eSelective protein saturation was achieved using a train of 50 ms Gaussian-shaped pulses (1% truncation, 45\u0026ndash;55dB attenuation) separated by 2 ms delays. On- and off-resonance frequencies were set to -300 Hz and 18,000 Hz, respectively. Saturation times (t\u003csub\u003esat\u003c/sub\u003e) ranged from 0.5 to 10 s. The STD amplification factor (A\u003csub\u003eSTD\u003c/sub\u003e​) was calculated according to Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{A}_{STD}=\\:\\frac{{I}_{0}-{I}_{STD}}{{I}_{0}}\\:\\times\\:\\:\\frac{\\left[L\\right]}{\\left[P\\right]}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere I\u003csub\u003e0​\u003c/sub\u003e is the intensity of the off-resonance signal, I\u003csub\u003eSTD​\u003c/sub\u003e is the intensity of the STD signal, and [L]/[P]​ represents the ligand-to-protein ratio. For GEM, the signal with the highest integral was normalized to 100%. Dissociation constants (K\u003csub\u003eD\u003c/sub\u003e​) were determined by fitting the concentration-dependent data to a Michaelis-Menten-like growth curve (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] using Origin 8.0:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{A}_{STD}=\\:\\frac{{\\alpha\\:}_{STD}\\left[L\\right]}{{K}_{D}+\\left[L\\right]}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2D-NOESY Experiments\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eSamples for 2D-NOESY were prepared in a deuterated buffer (pH 7.0) at the highest concentrations of both ligand and protein to ensure sufficient signal-to-noise. Parameters included a mixing time of 500 ms, a spectral window of 9615.38 Hz, 44 transients, and 1024 points. A relaxation delay of 3.0 s and a pre-scan delay of 10 \u0026micro;s were employed. Data were processed using a squared cosine window function, zero-filling, and Fourier transformation to yield 1K \u0026times; 1K matrices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Computational Methods\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMolecular\u003c/span\u003e Docking\u003c/h2\u003e \u003cp\u003eThe crystal structure of HSA bound to R-(+)-warfarin (PDB ID: 1H9Z)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] served as the receptor. Prior to docking, crystallographic ligands and water molecules were removed, polar hydrogens were added, and Gasteiger charges were assigned. Ligand geometries were generated from SMILES and optimized using the ANI-2x3 machine-learning potential. Simulations were performed using Uni-Dock with an exhaustiveness of 512. The grid box (15\u0026times;15\u0026times;15 \u0026Aring;) was centered at coordinates (32.79, 13.53, 9.61) \u0026Aring;, corresponding to the known active site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Molecular Dynamics\u003c/h2\u003e \u003cp\u003eThe best docking pose obtained from Uni-Dock was selected for molecular dynamics simulations. The complex protein-ligand was solvated in a TIP3P[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] water box (Amber ff19SB[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] for the protein and GAFF2[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] for the ligand) and neutralized to a 0.15 M ionic strength. After energy minimization performed in 25 000 steps, the system was gradually heated from 0 to 300 K over 500 ps under constant volume (NVT) conditions using a Langevin thermostat with a collision frequency of 2 ps\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a positional restraint of 50 kJ.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u0026Aring;\u003csup\u003e\u0026minus;2\u003c/sup\u003e applied to the protein and ligand heavy atoms to maintain structural stability during heating, while solvent and ions were allowed to move freely. Subsequently, an NPT equilibration of 500 ps at 300 K and 1 atm was carried out using isotropic position scaling and a barostat coupling constant of 5 ps with a positional restraint of 50 kJ.mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u0026Aring;\u003csup\u003e\u0026minus;2\u003c/sup\u003e. The solvent molecules and ions were kept unrestrained, allowing the system density to equilibrate under constant-pressure conditions.\u003c/p\u003e \u003cp\u003eThe complexes were subjected to MD simulations using OpenMM[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] with a 2 fs integration timestep. The simulation was run for a total of 55 ns, with coordinates saved every 5 ps. The first 5 ns were discarded from the analysis and considered as an additional equilibration period to ensure full stabilization of the protein\u0026ndash;ligand system and proper relaxation of noncovalent interactions prior to data collection. The MD trajectory was subsequently evaluated through docking analysis with the GNINA[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] scoring framework. For each frame, GNINA affinity scores and convolutional neural network (CNN)\u0026ndash;based scores were obtained. The CNN GNINA scoring function provides two primary metrics: CNNscore, a binary pose-quality classifier representing the probability that the predicted pose is within 2 \u0026Aring; RMSD of the true binding mode, and CNNaffinity, a predicted binding affinity expressed as a pK value. A third metric, the CNN_VS score, defined as the product of CNNscore and CNNaffinity, integrates pose confidence and affinity into a single ranking criterion[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This CNN_VS score was used to evaluate the ligand poses generated from the MD trajectory, as it has previously demonstrated strong retrospective virtual screening performance[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3. Binding Affinity Prediction\u003c/h2\u003e \u003cp\u003eThe predictive model Boltz-2 implemented in \u003cem\u003eRowan Scientific\u003c/em\u003e online platform[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] was used to estimate the affinity values (IC\u003csub\u003e50\u003c/sub\u003e) for the protein\u0026ndash;ligand complexes formed between HSA and derivatives 01, 02, and 03. A pocket restriction was applied, limiting ligand placement within 4 \u0026Aring; of residue ARG257, which interacts with all three derivatives according to molecular docking simulations. However, reproducible affinity values could not be obtained for warfarin, since its chirality could not be properly encoded in SMILES format. Only predictions with affinity probabilities greater than 0.5 were considered for analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eInitial assessments of the interactions between the naphthoquinone derivatives and HSA were conducted using \u003csup\u003e1\u003c/sup\u003eH NMR spectroscopy. Figures\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;c illustrate the spectra of the ligands in the presence (red) and absence (blue) of the protein. The addition of HSA resulted in marked line broadening, a loss of J-coupling definition and splitting patterns, significant chemical shift displacements, and baseline distortions. These observations strongly suggest a significant intermolecular interaction between the naphthoquinones and the protein in the liquid state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSimilar spectral patterns were observed by Milagre et al. during the study of cephalosporin and penicillin interactions with HSA\u003c/span\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBroadening of NMR signals is widely recognized as primary evidence for the association between small molecules and biological targets. This phenomenon is typically attributed to a decrease in the effective transverse relaxation time (T\u003c/span\u003e\u003csub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sub\u003e\u003csup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026lowast;\u003c/span\u003e\u003c/sup\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e​) resulting from an increase in the rotational correlation time (τ\u003c/span\u003e\u003csub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ec​\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e) upon binding. Mathematically\u003c/span\u003e, T\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026lowast;\u003c/sup\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e​is inversely proportional to the linewidth at half-height (Δν\u003c/span\u003e\u003csub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1/2\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e​) (\u003c/span\u003eEq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\varDelta\\:\\nu\\:}_{\\frac{1}{2}}\\propto\\:\\frac{1}{\\pi\\:{T}_{2}^{*}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhile monitoring Δν\u003csub\u003e1/2\u003c/sub\u003e​ as a function of HSA concentration has been used to study drugs like phenytoin and naproxen[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], conclusions based solely on line broadening can be premature, as these changes may also arise from non-specific viscosity increases. Consequently, more robust techniques such as STD-NMR and 2D-NOESY were employed to validate the binding.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThus, to obtain the most favourable interaction site, the use of STD-NMR is essential. This approach is supported on NOE (Nuclear Overhauser Effect)\u003c/span\u003e transference \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003efrom HSA to naphthoquinones candidates\u003c/span\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis experiment consists\u003c/span\u003e of the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eapplication of a radiofrequency pulse on a spectral region that\u003c/span\u003e contains \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eonly the signals attributed to HSA\u003c/span\u003e; this \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eexperiment is denominated\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eon-resonance\u003c/span\u003e. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe magnetization received by the HSA molecule is\u003c/span\u003e transferred \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eto the entire protein, by a process well-described as spin diffusion, which occurs\u003c/span\u003e through \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethe bonds on the macromolecule (HSA, in this case) until to naphthoquinone derivative under scrutiny. Therefore, the hydrogen\u003c/span\u003e belonging \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eto the naphthoquinone closer to HSA, will receive the most intense magnetization, and\u003c/span\u003e consequently, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethe amplitude for the observed signals will be higher. Additionally, an\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eoff-resonance\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e(I\u003c/span\u003e\u003csub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eoff\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e)\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003espectrum\u003c/span\u003e is acquired. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn this experiment, there is the irradiation of a radiofrequency pulse in a region that\u003c/span\u003e does not \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ehave signals attributed to HSA. Finally, the saturation degrees are calculated, determining the individual signal intensities in the STD spectrum (I\u003c/span\u003e\u003csub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eoff\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). This relative amount is calculated using\u003c/span\u003e Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand expressed as a percentual of the most intense saturation transfer observed for a set of protons (as indicated by the colored highlights in the\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e), and as consequence, the proximity of these protons\u003c/span\u003e to the HSA \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003esurface.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Group Epitope Mapping (GEM) analysis reveals distinct binding modes for each derivative. For derivative 01, the interaction with HSA is dominated almost exclusively by the aromatic system, with these protons exhibiting a high saturation transfer (average AF\u0026thinsp;\u0026asymp;\u0026thinsp;98.4%). Interestingly, the single proton positioned outside the aromatic scaffold (H1) also contributes significantly, showing a relative interaction of approximately 35.3%.\u003c/p\u003e \u003cp\u003eIn contrast, the binding epitope of derivative 02 shifts toward the aliphatic region; here, the methyl protons (H1 and H2) are positioned closer to the HSA surface (AF\u0026thinsp;\u0026asymp;\u0026thinsp;81.4%) than the aromatic protons. Furthermore, the methylidene proton (H3) displays a similar influence on the binding stability (AF\u0026thinsp;\u0026asymp;\u0026thinsp;56.8%), whereas the methylene group (H4) appears to be oriented away from the protein surface (AF\u0026thinsp;\u0026asymp;\u0026thinsp;31.5%), indicating a minor contribution to the overall stabilization.\u003c/p\u003e \u003cp\u003eFinally, for derivative 03, the combination of an aliphatic branch and an additional aromatic interaction site provides optimal conditions for molecular recognition, resulting in an exceptionally efficient interface where all protons exhibit AF values exceeding 92.0%. Collectively, these results suggest that an increased degree of unsaturation and hydrophobicity in the side chain is highly beneficial for a more effective and robust interaction with HSA.\u003c/p\u003e \u003cp\u003eTo validate the hypothesis that a high degree of unsaturation enhances binding efficiency, concentration-dependent STD-NMR experiments were performed to determine the dissociation constants (K\u003csub\u003eD​\u003c/sub\u003e) for each ligand (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results confirm that the presence of an unsaturated branch external to the aromatic core provides a more effective interaction site, thereby imparting higher stability to the resulting supramolecular adduct.\u003c/p\u003e \u003cp\u003eThe average dissociation constants obtained for derivative 01 (K\u003csub\u003eD\u003c/sub\u003e ​ \u0026asymp; 7.00 mM), derivative 02 (K\u003csub\u003eD\u003c/sub\u003e \u0026asymp; 1.40 mM), and derivative 03 (K\u003csub\u003eD\u003c/sub\u003e ​\u0026asymp; 1.13 mM) follow a consistent trend. Specifically, these values demonstrate that the incorporation of an apolar group is essential for strengthening the interaction with HSA. Consequently, this structural modification appears to be a key factor in optimizing the pharmacokinetic profiles and drug-delivery potential of these naphthoquinone derivatives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the 2D-NOESY correlation maps for all derivatives in the presence of HSA. These spectra reveal significant cross-peaks in the region corresponding to correlations between the aromatic signals of the naphthoquinones and the aliphatic signals of the HSA protein. These findings confirm that all investigated naphthoquinones exhibit a high degree of spatial proximity toward apolar amino acid motifs. Furthermore, these results are in excellent agreement with literature reports, which indicate that the most efficient binding sites are the hydrophobic domains of HSA. Specifically, this behavior is consistent with binding at Site I (Subdomain IIA), a pocket characterized by a high density of leucine, isoleucine, and alanine residues, which provide a favorable environment for the stabilization of hydrophobic ligands[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, molecular docking simulations were performed to elucidate and corroborate the nature of the intermolecular interactions between the naphthoquinone derivatives and HSA. In this computational approach, the warfarin molecule was employed as a structural benchmark, given its chemical similarity to the naphthoquinone scaffold. Warfarin is well-characterized by its high affinity for HSA (K\u003csub\u003eD​\u003c/sub\u003e \u0026asymp; 3 \u0026micro;M) and its preferential binding to Sudlow\u0026rsquo;s Site I (Subdomain IIA). This specific region is characterized by a high density of both aliphatic and aromatic amino acid residues, providing an ideal hydrophobic pocket that mirrors the environment suggested by our experimental NMR data. Consequently, utilizing warfarin as a reference ligand allows for a direct comparison of the binding orientations and stabilization energies of the new derivatives within the same protein domain[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst, it was verified that the best docking pose obtained for R-warfarin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) closely matched the crystallographic conformation observed in the 1H9Z structure (RMSD\u0026thinsp;=\u0026thinsp;0.757 \u0026Aring;). This result confirms that the docking protocol reliably reproduced the experimental binding mode.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe best docking poses (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) revealed binding scores of \u0026minus;\u0026thinsp;6.934 kcal/mol for derivative 01, \u0026minus;\u0026thinsp;7.741 kcal/mol for derivative 02, \u0026minus;\u0026thinsp;8.196 kcal/mol for derivative 03, and \u0026minus;\u0026thinsp;8.760 kcal/mol for warfarin. Warfarin displayed hydrogen bond interactions with residue ARG220 and a π\u0026ndash;π stacking interaction with TRP212, consistent with its known high affinity for this binding site. Derivative 01 (D01) formed hydrogen bonds with residue ARG255; however, its smaller molecular size and the large cavity of the binding site lead to a less favorable stabilization, as reflected in its lower docking score. This observation is in good agreement with the experimental group epitope mapping data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), where the H1 proton of derivative 01 shows a low relative proximity, consistent with its orientation toward a highly polar amino acid environment in the docking pose (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Derivative 02 (D02), located further from residues ARG220 and ARG255, interacts mainly through hydrophobic contacts with LEU217 and LEU236, which explains its moderate binding affinity. Accordingly, the side-chain protons of derivative 02 exhibit higher comparative proximity percentages due to their closer contact with nonpolar residues, whereas the aromatic protons display intermediate proximity values (around 50%), reflecting their partial exposure to polar groups within the binding site. In contrast, derivative 03 (D03) exhibits hydrogen bonding with ARG220 and a π\u0026ndash;π stacking interaction with TRP212, positioning it as the derivative with the most favorable binding score. In this case, the docking pose reveals hydrophobic interactions comparable to those observed for derivative 02, together with a greater exposure of the naphthoquinone aromatic ring to nonpolar residues, which accounts for the near-100% comparative proximity values observed in this region. Furthermore, the high proximity values associated with the terminal phenyl group are consistent with the π\u0026ndash;π stacking interaction with TRP212. The docking scores correlate well with the experimental K\u003csub\u003eD\u003c/sub\u003e values obtained from NMR studies, namely 7.00 mM for derivative 01, 1.40 mM for derivative 02, and 1.13 mM for derivative 03.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 7a shows the RMSD profiles of the four studied compounds during 50 ns of molecular dynamics simulation. Overall, Derivative 01 exhibited the most pronounced fluctuations; this behavior can be attributed to its smaller molecular size, which provides greater conformational freedom within the HSA binding pocket, as previously discussed.\u003c/p\u003e \u003cp\u003eWhen analyzing the average RMSD values in relation to the affinities toward HSA, a clear trend is observed. The derivatives showed progressively higher RMSD averages (Fig.\u0026nbsp;7a, 4.013 \u0026Aring; (Derivative 01), 2.301 \u0026Aring; (Derivative 02), and 0.925 \u0026Aring; (Derivative 03), which inversely correlate with their experimentally determined dissociation constants of 7.00 mM, 1.40 mM, and 1.13 mM, respectively. This inverse relationship suggests that lower RMSD values are associated with increased structural stability of the protein\u0026ndash;ligand complex. Nevertheless, this interpretation should be considered with caution, as RMSD is also influenced by the intrinsic flexibility of the ligand. In this regard, warfarin, which possesses a largely saturated and flexible side chain, exhibits higher conformational variability compared to the more rigid scaffold of derivative 03, potentially leading to larger RMSD fluctuations that do not necessarily reflect weaker binding. Therefore, the observed RMSD trend likely reflects a combination of enhanced van der Waals and π\u0026ndash;π interactions together with differences in ligand rigidity, rather than RMSD serving as a standalone descriptor of binding affinity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7\u003c/b\u003e. (a) Root-mean-square deviation (RMSD) profiles of warfarin and the three naphthoquinone derivatives bound to HSA over 50 ns of molecular dynamics simulation. (b) MD evaluation of warfarin and the naphthoquinone derivatives bound to HSA by CNN_VS scores.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 7b\u003c/b\u003e summarizes the post-docking and molecular dynamics\u0026ndash;based evaluation of the binding behavior of warfarin and the naphthoquinone derivatives. \u003cb\u003eFigure S2\u003c/b\u003e presents the GNINA binding scores (kcal/mol), which highlight the reduced affinity of derivative 01. Based solely on these scores, derivative 03 would appear to display a higher affinity for HSA than warfarin itself; however, this trend is not fully consistent with the experimental binding data. In contrast, evaluation of the CNN_VS scores yields a ranking that is more consistent with the experimentally determined dissociation constants (K\u003csub\u003eD\u003c/sub\u003e), supporting the use of this metric as a more reliable indicator of binding performance in this system.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] Overall, the combined analysis of RMSD behavior, GNINA scores, CNN-based ranking, and experimental NMR-derived K\u003csub\u003eD\u003c/sub\u003e values provides a coherent and balanced interpretation of ligand\u0026ndash;HSA interactions.\u003c/p\u003e \u003cp\u003eThe IC\u003csub\u003e50\u003c/sub\u003e values obtained from Boltz-2 simulations with pocket restraints for the naphthoquinone derivatives are consistent with the results from docking and molecular dynamics studies, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This agreement supports the reliability of the computational workflow, indicating that derivatives with stronger predicted affinities (lower IC\u003csub\u003e50\u003c/sub\u003e values) also displayed more stable binding modes and lower RMSD values during molecular dynamics simulations. Such consistency reinforces the hypothesis that the lateral chain plays a critical role in stabilizing the ligand\u0026ndash;protein interactions through van der Waals and π\u0026ndash;π contacts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted inhibitory activities (IC\u003csub\u003e50\u003c/sub\u003e) and affinity probabilities for the studied naphthoquinone derivatives against HSA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC\u003csub\u003e50\u003c/sub\u003e (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt. Dev. (\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAffinity probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerivative 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerivative 02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerivative 03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHerein we have used STD-NMR, 2D-NOESY\u003c/span\u003e, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emolecular docking\u003c/span\u003e and molecular dynamics \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eto understand the biological interaction between naphthoquinones derivatives and HSA aiming the further design of potential anticancer drugs with improved drug delivery features. Given the results, some conclusions can be supplied: (i) The interaction between naphthoquinones and HSA occurs through weak spatial contacts by Van der Waals forces; (ii) the presence of an unsaturated chain outside the aromatic cage turns the intermolecular interaction more favorable, and (iii) 2D-NOESY and molecular docking results demonstrates that the addressed naphthoquinones have more attraction for sites rich in aliphatic residues within HSA arrangement, putting light on the reasons about its interactions towards this protein, which is a relevant biological feature for the design of more effective anticancer drug delivery candidates.\u003c/span\u003e Furthermore, molecular dynamics simulations demonstrated that the side chain plays a key role in maintaining stable contacts with residues such as ARG220 and TRP212, particularly in the case of the derivative 03, which exhibited the lowest average RMSD (0.925 \u0026Aring;) and the highest binding affinity (K\u003csub\u003eD\u003c/sub\u003e \u0026asymp; 1.13 mM).\u003c/p\u003e \u003cp\u003eTaken together, the combined experimental and computational results consistently identify derivative 03 as the most promising ligand, highlighting the importance of side-chain architecture in promoting persistent hydrophobic contacts and π\u0026ndash;π stacking interactions with TRP212. These features contribute to enhanced complex stability, are in agreement with GNINA-based docking ranking metrics and Boltz-2 IC₅₀ predictions, and underscore the relevance of structural rigidity and aromatic lateral groups in optimizing HSA binding at the warfarin site. Overall, this integrated approach provides a robust framework for guiding the future design of naphthoquinone-based anticancer drug delivery systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript are gratefully acknowledging the financial support from FAPESP (2018/16040-5 and 2018/09145-5) and by the Vicerrectorado de Investigacion (VRI) at the Pontificia Universidad Catolica del Peru (PUCP) through grant DRI-2025-1290 and DRI-2025-1287. EC and JV are thankful to IA-PUCP for the computational resources provided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.K. and J.V. contributed equally to the study\u0026rsquo;s conceptualization and served as lead contributors for the formal analysis and investigation. F.K. led the writing of the original draft and contributed equally to its review and editing. J.V. served as lead for the review and editing process. E.C. contributed equally to conceptualization and formal analysis, while T.V.provided equal contributions to the formal analysis and original draft preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Karmalawy A, Eissa AE, Ashour MA N, et al (2025) Medicinal chemistry perspectives on anticancer drug design based on clinical applications (2015\u0026ndash;2025). 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Molecules 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules22081317\u003c/span\u003e\u003cspan address=\"10.3390/molecules22081317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetitpas I, Bhattacharya AA, Twine S et al (2001) Crystal Structure Analysis of Warfarin Binding to Human Serum Albumin: ANATOMY OF DRUG SITE I*. J Biol Chem 276:22804\u0026ndash;22809. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M100575200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M100575200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human Serum Albumin (HSA), Naphthoquinones, STD-NMR, Molecular Dynamics, Molecular Recognition","lastPublishedDoi":"10.21203/rs.3.rs-8683311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8683311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNaphthoquinone derivatives, specifically 01 (lawsone; 2-hydroxynaphthalene-1,4-dione), 02 (lapachol; 2-hydroxy-3-(3-methylbut-2-enyl)naphthalene-1,4-dione), and 03 (2-hydroxy-3-styrylnaphthalene-1,4-dione), represent a versatile class of natural and synthetic molecules. These compounds hold significant potential as ligands for the development of novel anticancer metal complexes. In this study, we investigated the intermolecular interactions between these ligands and Human Serum Albumin (HSA), the primary protein responsible for drug transport in the human bloodstream. To achieve this, a synergistic approach was employed, combining NMR binding-target techniques, such as Saturation Transfer Difference (STD-NMR) and 2D-NOESY, with computational tools including molecular docking, molecular dynamics (MD) simulations, and binding affinity predictions. These methods allowed for a detailed characterization of the binding interface at the atomic level, defined as Group Epitope Mapping (GEM), while also enabling the estimation of the dissociation constants (K\u003csub\u003eD​\u003c/sub\u003e) for the resulting adducts. The results demonstrate that the presence of an unsaturated lateral chain significantly contributes to the stabilization of the supramolecular arrangement. Specifically, the experimental K\u003csub\u003eD​\u003c/sub\u003e values, 7.00 mM for 01, 1.40 mM for 02, and 1.13 mM for 03, indicate that increasing the size and apolarity of the substituent leads to more efficient HSA interaction. Furthermore, 2D-NOESY experiments suggest that these naphthoquinones are spatially directed toward peripheral aliphatic domains (alanine, leucine, and valine). In agreement with these findings, the calculated docking scores follow a consistent trend (01\u0026thinsp;\u0026lt;\u0026thinsp;02\u0026thinsp;\u0026lt;\u0026thinsp;03), with derivative 03 approaching the binding affinity of the reference ligand, warfarin. Moreover, GNINA-based affinity predictions and CNN_VS scores further corroborate that derivatives bearing apolar lateral chains exhibit superior interaction profiles. Taken together, these unprecedented results establish that incorporating apolar substituents is a robust strategy for enhancing HSA binding. Ultimately, this study provides a valuable guideline for the rational design of naphthoquinone derivatives with optimized drug-delivery properties.\u003c/p\u003e","manuscriptTitle":"Atomic-Level Insights into the Molecular Recognition of Anticancer Naphthoquinones by Human Serum Albumin: The Role of Apolar Side Chains in Binding Stability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 17:16:51","doi":"10.21203/rs.3.rs-8683311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6625b001-fd99-451e-a280-6e1f3c88a6ce","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T18:54:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 17:16:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8683311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8683311","identity":"rs-8683311","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}