Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting

Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting Tianwen Shao , Min Gan , Qi Yin , Jingyan Bai , Jiayi Yang , View ORCID Profile Shengjie Sun doi: https://doi.org/10.1101/2025.05.16.654435 Tianwen Shao 1 Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University , Changsha 410083, China 2 National Research Center for Geoanalysis and Key Laboratory of Eco-geochemistry, Ministry of Natural Resources , China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Min Gan 1 Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University , Changsha 410083, China 2 National Research Center for Geoanalysis and Key Laboratory of Eco-geochemistry, Ministry of Natural Resources , China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qi Yin 1 Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University , Changsha 410083, China 2 National Research Center for Geoanalysis and Key Laboratory of Eco-geochemistry, Ministry of Natural Resources , China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jingyan Bai 1 Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University , Changsha 410083, China 2 National Research Center for Geoanalysis and Key Laboratory of Eco-geochemistry, Ministry of Natural Resources , China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiayi Yang 1 Key Laboratory of Biometallurgy of Ministry of Education, School of Minerals Processing and Bioengineering, Central South University , Changsha 410083, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shengjie Sun 3 Department of Biomedical Informatic, School of Life Sciences, Central South University , Changsha 410083, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shengjie Sun For correspondence: shengjieswen{at}163.com Abstract Full Text Info/History Metrics Preview PDF Abstract Thrombocythemia and related disorders are primarily driven by persistent activation of the JAK-STAT signaling pathway, which is frequently associated with valine-to-phenylalanine (VF) point mutations in the JAK2 and TYK2 proteins. However, the precise mechanisms underlying the sustained downstream pathway activation induced by these mutations remain unclear. To investigate this, we employed molecular dynamics simulations and computational biophysical approaches to examine how VF mutations sustain JAK-STAT pathway activation. Our analyses, encompassing energetics, interaction forces, and surface electrostatic charge distributions, revealed that these mutations predominantly alter the spatial conformations and stability of the proteins by modulating inter-dimeric interaction forces and binding free energies. Specifically, the V617F mutation in JAK2 enhanced the stability of the JAK2 dimer and suppressed the activity of the JH2 pseudokinase domain. Similarly, the V678F mutation in TYK2 reducing the binding strength of the JH1 kinase domain and the binding free energy, increasing the binding affinity at the dimer interface. Both mutations ultimately resulted in heightened JH1 kinase domain activity and aberrant activation of downstream signaling pathways. Electrostatic analyses further demonstrated that JAK2V617F strengthened attractive forces within the dimer and TYK2V678F mutation mitigated repulsive forces. Decreased binding free energy confirmed enhanced dimer stability in JAK2V617F and TYK2V678F mutants, driven by distinct mechanisms: reinforced JH2 domain interactions in JAK2V617F and domain compaction with reoriented interfaces in TYK2V678F. These insights delineate the mutation-specific allosteric networks that underpin the pathogenesis of thrombocythemia. Furthermore, they establish a molecular foundation for designing isoform-selective inhibitors that target conformational vulnerabilities, thereby presenting novel therapeutic opportunities. Author Summary The VF mutations located in JAK2 and TYK2 stabilizes the JH2 pseudokinase domain and enhances dimer interfaces to disrupt autoinhibition in JAK2, while it induces structural reorganization of the dimer FERM/SH2 domains and JH1 kinase conformation to promote substrate accessibility in TYK2. We developed a novel multiscale analyses framework, integrating conformational analysis, electrostatic analysis, hydrogen bond and salt bridges, and free energy decomposition, to collaboratively elucidate the effects of VF mutations on the protein multimers. These findings highlights divergent allosteric networks underlying pathogenic signaling, offering a molecular mechanisms for designing isoform-selective inhibitors targeting conformational vulnerabilities in myeloproliferative disorders. Introduction Dysregulation of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway frequently leads to a variety of diseases, including autoimmune disorders such as rheumatoid arthritis, hematologic malignancies such as thrombocythemia and leukemia, and inflammation[ 1 ]. Mutations in JAK2 and TYK2 overactivate the JAK-STAT pathway, driving to myeloproliferative neoplasms (MPNs) such as essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (PMF). ET, a clonal myeloproliferative neoplasm, arises from the clonal expansion of bone marrow stem cells, leading to aberrant megakaryocyte proliferation and elevated platelet production, with clonal hematopoiesis (CH) in the elderly further enhancing the expansion of mutated leukocytes and platelets[ 2 ]. ET markedly elevates the risk of thrombosis, hemorrhage, myelofibrosis, and acute myeloid leukemia, presenting serious threats to patient health[ 3 ]. Clinically, more than half of patients with ET remain asymptomatic, with the condition frequently detected incidentally via routine blood tests; however, microvascular symptoms—including erythromelalgia, acroparesthesia, blurred vision, and headaches—manifest in approximately 29% of patients (add ref). At diagnosis, splenomegaly occurs in in 12%, arterial thrombosis in 14%, venous thrombosis in 8%, and major bleeding in 4.5%. In the U. S., the annual incidence of ET is 15.5 per 100,000 individuals, and about 90% of patients carry genetic variants that activate the JAK-STAT signaling pathway, including mutations in JAK2 (64%), CALR (23%), and MPL (4%)[ 4 ]. The JAK2V617F mutation, the most common somatic mutation in JAK2, drives MPNs progression and significantly increases the risk of atherothrombosis. Pardanani et al. further demonstrate that JAK2 mutations are found in nearly all patients with PV, with the V617F mutation accounting for approximately 95% of cases[ 5 ]. The JAK2V617F mutation is present in 50–60% of patients with ET and 55– 65% of those with PMF. Current treatments for JAK-STAT dysregulation, such as JAK inhibitors like tofacitinib and ruxolitinib, are hindered by off-target effects and lack curative potential[ 6 ]. A deeper understanding of the mechanisms underlying these mutations is therefore essential for developing targeted therapies that reduce adverse effects and advance precision medicine. The JAK-STAT signaling pathway is a vital intracellular cascade that regulates immune responses, cell proliferation, differentiation, and apoptosis. It consists of three main components: cytokine receptors, Janus kinases (JAKs), and signal transducers and activators of transcription (STATs)[ 7 ]. JAKs family, a group of tyrosine kinases critical for cytokine signaling, comprises four members: JAK1, JAK2, JAK3, and TYK2. Each JAK protein is characterized by distinct domains, including FERM, SH2, JH1, and JH2. The JH1 domain serves as the catalytically active kinase domain, driving tyrosine phosphorylation, whereas the JH2 pseudokinase domain regulates autoinhibition and other modulatory functions[ 8 ]. Activation of cytokine receptors activate and dimerize JAKs, leading to the phosphorylation of tyrosine residues on receptor tails and subsequent JAK dimerization. Darnell et al. shows that JAK dimerization generates docking sites for STATs, which are then recruited to receptor complexes through their SH2 domains and phosphorylated by the JH1 kinase domain[ 9 ]. Phosphorylated STATs (pSTATs) form homo or hetero dimers via SH2-phosphotyrosine interactions, translocate to the nucleus, and bind DNA regulatory elements to regulate chromatin accessibility and gene transcription. Interestingly, unphosphorylated STATs (U-STATs) are also present in the nucleus. Although specific STATs are generally activated by distinct cytokines, certain cytokines can activate nearly all STAT family members under particular conditions or in specific tissues. Nuclear STAT complexes coordinate transcriptional regulation by targeting chromatin, binding response elements, and recruiting coactivators to interact with RNA polymerase II (RNA Pol II), thus driving cellular responses[ 10 ]. The JAK2V617F mutation, a well-characterized gain-of-function (GOF) mutation, drives aberrant JAK-STAT signaling. Advances in sequencing technologies solved the mechanism of both germline and somatic GOF mutations in this pathway[ 11 ]. GOF mutations typically contribute to hyperinflammation or tumorigenesis. For example, Kralovics et al. showed that JAK2 GOF mutations are key drivers of MPN pathogenesis[ 12 ], while STAT1 GOF mutations are associated with chronic mucocutaneous candidiasis and autoimmunity, whereas STAT3 GOF mutations are linked to multisystem autoimmune disorders[ 13 ]. Somatic mutations are highly prevalent in hematologic malignancies, For instance, de Araujo et al. demonstrated that the STAT5B-N642H mutation drives T-cell leukemia[ 14 ]. These mutations impair signaling homeostasis by modifying kinase activity, STAT dimerization, or DNA-binding affinity. The JAK2V617F mutation, the most prevalent GOF mutation in the JH2 domain, promotes constitutive, cytokine-independent proliferation and persistent JAK-STAT pathway activation, as demonstrated by Hirahara et al.[ 15 ]. Amplified JAK2V617F signaling induces both qualitative and quantitative changes in hematopoietic stem cells (HSCs). These mutant HSCs overproduce cytokines and reactive oxygen species (ROS), reshape the bone marrow niche, provide clonal growth advantages, and contribute to genomic instability[ 6 ]. Under these conditions, ROS stabilize hypoxia-inducible factors (HIFs) in HSCs, leading to a pseudohypoxic state that shifts their metabolism from aerobic to anaerobic pathways. This metabolic reprogramming enhances HSC self-renewal via the downregulation of LKB1/STK11. Furthermore, mutant HSCs can egress from the bone marrow and colonize extramedullary sites, such as the spleen, where they initiate extramedullary hematopoiesis, potentially exacerbating inflammatory disorders[ 16 ]. Similarly, GOF mutations in TYK2, a JAK2 homolog (e.g., TYK2V678F), induce cytokine-independent autonomous proliferation. These mutations drive STAT1 activation and oncogenic transformation mediated by the BCL2 family[ 17 , 18 ]. The precise molecular mechanisms underlying the perturbation of JAK-STAT signaling by VF mutations remain elusive, hindering the development of mutation-specific therapies. Current JAK inhibitors lack specificity, broadly suppressing multiple JAK family members and resulting in significant off-target effects. This limitation is primarily attributed to the challenges faced by conventional biomedical techniques in dissecting intricate protein interaction networks. Traditional biomedical experiments have inherent limitations in studying protein-protein interactions in atom level. They often require lengthy experimental cycles, spanning months to years, and incur high costs. In contrast, computational approaches offer significant advantages. Molecular dynamics simulations can track molecular movements at femtosecond resolution, surpassing the spatiotemporal limits of experimental observation. Based on massive experimental works, now the deep learning algorithms, such as AlphaFold, predict millions of protein structures with over 90% accuracy, matching experimental standards. Computational methods developments and applications are highly reproducible and scalable. Molecular dynamics (MD) simulations have emerged as a powerful computational tool in biological research, offering unique insights into molecular-scale modeling and the analysis of mutation mechanisms. Unlike in vitro and in vivo experiments, MD simulations enable precise control over physicochemical parameters, including protein coordinates, charge states, solvation ions, membranes, temperature, and transmembrane potentials modulated by voltage and electrolyte gradients[ 19 – 21 ]. Additionally, it also provides atomic-resolution mechanistic insights, prediction of mutation effects, modeling of complex systems, and calculation of free energy changes. The flexibility of MD across various time scales, coupled with its cost-effectiveness and efficiency, makes it a crucial tool for guiding experimental research [ 22 – 24 ]. This study utilized molecular dynamics simulations to systematically explore the differential effects of JAK2V617F and TYK2V678F mutations on protein structure and function. Specifically, 80 ns all-atom simulations employing the CHARMM36m force field were conducted for both wild-type and mutant forms of JAK2 and TYK2. Comprehensive biophysical analyses—including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bond and salt bridge profiling, electrostatic potential mapping, and MM/PBSA-based free energy calculations—uncovered distinct mutation-specific mechanisms influencing JH2 autoinhibition, dimer stability, and JH1 kinase activity. Our study reveals that the JAK2V617F mutation strengthens dimer interface stability through enhanced electrostatic attraction and hydrogen bonding in the JH2 domain, while the TYK2V678F mutation induces a pincer-like opening of the JH1 domain via FERM and SH2 domain reorganization, highlighting their distinct roles in JAK-STAT pathway dysregulation. These insights pave the way for designing mutation-selective, conformation-specific inhibitors, offering a promising avenue to advance precision medicine for essential thrombocythemia with improved efficacy and reduced off-target effects. Materials and Methods The Proteins Modeling and Molecular Dynamics Simulation The initial sequences of JAK2 and TYK2 used for modeling were obtained from the UniProt[ 12 ] database (O60674 · JAK2_HUMAN and P29597 · TYK2_HUMAN), corresponding to the human tyrosine kinase JAK2 and TYK2 sequences. We obtained the sequence of the two proteins from the UniProt. The domain composition of JAK2 include(shown as residues ID): FERM domain (37-380), SH2 domain (401-482), JH2 pseudokinase domain (545-809), and JH1 kinase domain (849-1124). The domain composition of TYK2 include(shown as residues ID): FERM domain (26-431), SH2 domain (450-529), JH2 pseudokinase domain (589-875), and JH1 kinase domain (897-1176)[ 13 ]. These sequences were constructed using SWISS-MODEL[ 14 ] based on the full-length dimerized JAK structure (PDB: 7T6F)[ 4 ]. To investigate the impact of point mutations on dimer structure, Chimera 1.18 was employed to introduce the V617F mutation (valine to phenylalanine at position 617) in JAK2WT and the V678F mutation (valine to phenylalanine at position 678) in TYK2WT. The models were solvated with the TIP3P[ 15 ] water model and ionized with 150 mM KCl using CHARMM-GUI[ 16 ]. The CHARMM36m force field, known for its improved accuracy in modeling polypeptide backbones and proteins, was applied in MD simulations[ 17 ]. Periodic boundary conditions and the particle mesh Ewald method were implemented to handle long-range electrostatic interactions[ 18 ]. The system temperature was maintained at 310.15 K using a Langevin thermostat with a damping coefficient of 1/ps, while the pressure was set to 1 atm using a Nosé-Hoover Langevin piston barostat with a decay period of 25 fs. Simulations were preceded by 10,000 steps of energy minimization. Each simulation protocol comprised two phases: NPT equilibrium and NVT production. During the equilibrium phase, protein backbones were restrained, whereas all atoms were unrestrained in the production phase. Simulations were executed using NAMD3, with each MD run consisting of 0.5 ns NPT equilibration followed by 50 ns NVT production[ 19 ]. Through preliminary minimization simulations, we observed that after undergoing approximately 40 ns of molecular dynamics simulations, both the relative positions and intermolecular interactions among the four proteins stabilized. Therefore, we perform a 80 ns simulation with a time step of 2 fs. The simulation consists of 10,000,000 steps, and we save a frame every 12,500 steps, resulting in each frame representing 0.1 ns. Salt Bridges and hydrogen bond The formation of salt bridges and hydrogen bond is analyzed using Visual Molecular Dynamics (VMD)[ 20 ]. For salt bridges, the cutoff distance is set to 4 Å, while for hydrogen bond, the cutoff distance is 4 Å and the angle is 20°[ 21 , 22 ]. Electrostatic Analysis The electrostatic potential was calculated using the Delphi software[ 23 , 24 ], which solves the Poisson-Boltzmann equation Eq(1) [ 25 ] through the finite difference method to compute the electrostatic potential φ[ 26 ]. Subsequently, the electrostatic forces were further derived from the electrostatic potential and charges by DelphiForce[ 27 ]. Charges and radii were assigned using CHARMM36m’s pdb2pqr[ 28 ]. The dielectric constants for the protein and water were set to 2 and 80, respectively[ 29 ]. The calculation of electrostatic forces on TK was performed by DelphiForce[ 30 ]. Here, φ(r) and ρ(r) represent the electrostatic potential and permanent charge density, respectively, ε(r) is the dielectric constant, κ is the Debye-Hückel parameter, k B is the Boltzmann constant, and T is the temperature[ 21 ]. The salt concentration, probe radius, protein filling ratio, and resolution were set to 150 mM, 1.4 Å, 0.70, and 2 grids/Å, respectively. Hydrogen bond analysis was conducted based on the last 20 ns of molecular dynamics simulations using VMD. The distance and angle cutoffs for hydrogen bond analysis were set to 4 Å and 20°, respectively. Protein surface electrostatic charge visualization was performed using Chimera[ 31 ]. The pqr files corresponding to each protein pdb were generated using pdb2pqr, and the cube files were created using Delphi software. The surface charge distribution maps were rendered in Chimera, showing positively charged (red), negatively charged (blue), and neutral (white) surfaces. Electric field line diagrams were generated using VMD[ 20 ]. For electrostatic force calculations, the inter-chain distance of the protein was separated using separate2pdb_pqr, with a step size of 3 Å, and the range of 20 Å to 50 Å was selected. The electrostatic forces between the two chains were calculated at each step. MM/PBSA free binding energy To quantify the binding free energy between protein monomers, MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) analysis was performed using fifty structural snapshots extracted from the final 20 ns of the production molecular dynamics trajectory. Prior to analysis, solvent molecules and counterions were systematically removed from the system. The binding energy (ΔE bind ) for each energy component (electrostatic, van der Waals, polar solvation, and nonpolar solvation) was calculated according to Eq(2) ,where Edimer represents the energy of the dimeric complex and Emonomer corresponds to the energy of individual monomer units. The total binding free energy (ΔE total_bind ) was computed through the summation of its constituent energy components, as defined by Eq(3) ,where ΔE ebind , ΔE vbind , ΔE pbind , and ΔE npbind represent the Electrostatic component, van der Waals component, polar solvation component, and nonpolar solvation component contributions to the binding energy, respectively. Electrostatic component and polar solvation component energies computed using the DelPhi solver[ 23 ]. For these calculations, dielectric constants were assigned as ε = 2 for the protein and ε = 80 for the aqueous environment, with the proteins occupying 70% of the DelPhi calculation box volume[ 32 , 33 ]. Electrostatic parameters included a salt concentration of 0.15 mM and a grid resolution of 2 grids/Å. Van der Waals component were calculated through NAMD3 software following a single-step equilibration protocol[ 19 ]. Nonpolar solvation energy (E np ) was derived from solvent-accessible surface area (SASA) measurements obtained via NACCESS[ 34 ], with the energy relationship defined in Eq(4) , where α = 0.0054 and β = 0.92 kcal/mol. This multi-scale approach integrates implicit solvent models with molecular mechanics to provide a comprehensive thermodynamic profile of protein-protein interactions. Root Mean Square Fluctuation (RMSF) and Root Mean Square Deviation (RMSD) Based on the simulation, the RMSF of the α-carbons was calculated using VMD Eq(5) [ 20 ]. Here, i represents the residue index, T denotes the total simulation time (in this case, the number of frames), and r i (t j ) indicates the position of residue i at time t j . The r i ref is the reference position of residue i, computed as the time-averaged position[ 35 ]. The RMSD is used to measure the average distance between two protein structures, calculated by equation below Eq(6) [ 20 , 35 ]. Here, W = ∑ω i is the weighting factor, and N is the total number of atoms. r i (t) represents the position of atom i at time t after least-squares fitting the structure to the reference structure. The r i ref is the reference position of residue i, defined by the reference structure. Results and Discussion Difference in Stability Between Wild-Type and Mutant Proteins As demonstrated by RMSF analysis in Figs 1a and 1b , the V617F mutation in JAK2 and the V678F mutation in TYK2 induce domain-specific alterations in structural flexibility, disrupting autoinhibition and promoting pathological signaling. Within the JH2 pseudokinase domain, both mutations perturb critical ATP-binding residues (e.g., V629, R683, and K736 in JAK2; L699, R738, and F754 in TYK2), resulting in reduced ATP affinity and impairing JH2’s inhibitory regulation of the JH1 kinase domain. Simultaneously, the JH1 domains exhibit a paradoxical increase in activity despite global stability changes. In JAK2V617F, catalytic activity is redistributed, with diminished flexibility at residues L849 and A880 contrasted by enhanced flexibility at Q942 and D1004. Conversely, TYK2V678F adopts a rigidified JH1 conformation that sustains low-level activation. These structural shifts destabilize the binding interfaces for STAT proteins, thereby compromising normal signal regulation. Download figure Open in new tab Fig 1. The RMSF and RMSD analysis of four proteins (a)The RMSF comparison of JAK2WT and JAK2V617F. Blue represents JAK2WT, Red represents JAKV617F. (b)The RMSF comparison of TYK2WT and TYK2V678F.Blue represents TYK2WT, Red represents TYK2V678F. (c)The RMSD comparison of JAK2WT and JAK2V617F. Black represents JAK2V617F, Red represents JAK2WT. (d)The RMSD comparison of TYK2WT and TYK2V678F. Black represents TYK2V678F, Red represents TYK2WT. Molecular dynamics simulations reveal a distinct decoupling of stability and activity. As shown in Figs 1c and 1d , the mutant proteins display lower RMSD values compared to the wild type, with divergence initiating at approximately 24 ns for JAK2 and 20 ns for TYK2, indicating enhanced global conformational stability. However, RMSF analysis identifies localized hyperactivity within the JH1 domain. The stabilized JH2 domains reinforce dimerization interfaces, prolonging the persistence of pathological dimers. This establishes a self-reinforcing cycle: the rigidified JH2 domain fails to suppress JH1, while the hyperactive JH1 domain sustains pathway activation despite potentially reduced catalytic efficiency. Ultimately, these mutations establish a “locked switch” state, characterized by enhanced JH2-mediated dimer stability and constitutive JH1 activity, perpetuating JAK/STAT pathway hyperactivation. This dual mechanism—combining loss of autoinhibition with a signal-competent kinase conformation—accounts for the immune dysregulation observed in associated diseases. Targeting the reinforced JH2 dimer interface or the allosteric communication between JH1 and JH2 may provide therapeutic advantages over conventional ATP-competitive inhibitors. Current research efforts are focused on validating dimerization dynamics and evaluating conformation-selective inhibitors. Difference in Surface Charge, hydrogen bond, and Salt Bridges Between Wild-Type and Mutant Proteins After the molecular dynamics simulations, the dimers JAK2WT_Dimer, JAK2V617F_Dimer, TYK2WT_Dimer, and TYK2V678F_Dimer achieved structural convergence, with the final frame selected for detailed analysis (see Figures 2Ia-2IVf ). Comparisons of surface charge distributions ( Figs 2Ia-2Ic vs. Figs 2IIa-2IIc ; Figs 2IIIa-2IIIc vs. Figs 2IVa-2IVc ) indicate that, while the overall electrostatic profiles remain largely consistent, significant changes are observed at the binding sites of the JH2 pseudokinase domain. In JAK2V617F_Dimer, the JH2 interface displays denser electric field lines and a marked increase in positive charge density ( Fig 2If vs. Fig 2IIf ), which strengthens electrostatic interactions and establishes a neutralized interaction zone that enhances dimer stability. This structural adaptation extends the duration of JH2-mediated inhibition while maintaining prolonged JH1 activation, ultimately contributing to pathological JAK/STAT hyperactivation and the development of thrombocythemia. Download figure Open in new tab Fig 2. The electrostatic surface and the protein structures of four proteins (I)JAK2V617F: [a-c] Electrostatic Potential Surface Plot, [d-e] The structure and hydrogen bond of PK-PK, [f] Electric Field Lines Diagram of PK-PK. (II)JAK2WT: [a-c] Electrostatic Potential Surface Plot, [d-e] The structure and hydrogen bond of PK-PK, [f] Electric Field Lines Diagram of PK-PK. (III)TYK2V678F: [a-c] Electrostatic Potential Surface Plot, [d-e] The structure and hydrogen bond of PK-PK, [f] Electric Field Lines Diagram of PK-PK. (IV)TYK2WT: [a-c] Electrostatic Potential Surface Plot, [d-e] The structure and hydrogen bond of PK-PK, [f] Electric Field Lines Diagram of PK-PK. Similarly, TYK2V678F_Dimer exhibits expanded regions of positive and neutral charge ( Figs 2IIIa-2IIIc vs. Figs 2IVa-2IVc ) alongside a redistribution of electric fields ( Fig 2IIIf vs. Fig 2IVf ). Furthermore, interchain distances are notably reduced in the FERM, SH2, and pseudokinase (PK) domains, whereas the JH1 tyrosine kinase (TK) domains show increased separation, a configuration likely to promote JH1 hyperactivity. Reorganization of hydrogen bond further stabilizes the dimer interfaces. In JAK2V617F_Dimer, the hydrogen bond within the pseudokinase domain (PK-PK) shift from the R541-E592 interaction, which exhibits 55.45% occupancy in the wild type, to higher-occupancy interactions, namely K539-E592 (70.80%) and K539-E596 (52.97%) ( Fig 2Ie vs. Fig 2IIe ). This shift corresponds to strengthened binding, despite an increased distance between the V617 and V617 residues (from 15.924 Å to 18.452 Å; see Figs 3c and 3d ). In contrast, TYK2V678F_Dimer exhibits a reduction in hydrogen bond occupancy within the PK-PK interface, exemplified by the loss of the M535-F537 (57.22%) and R588-N622 (52.54%) interactions present in the wild type. However, this is offset by a reduced distance between the V678 and V678 residues (from 50.670 Å to 17.883 Å; see Figs 3e and 3f ), indicating that structural compaction, rather than hydrogen bonding, predominantly stabilizes the dimer. Download figure Open in new tab Fig 3. Occupancy of hydrogen bond and salt bridges and distances between mutated residues in dour protein dimers (a)The main hydrogen bond in four dimers and the comparison. Black represents JAK2V617F, Red represents JAK2WT, Green represents TYK2V678F, Blue represents TYK2WT. (b)The main Salt Bridges in four dimers and the comparison. Black represents JAK2V617F, Red represents JAK2WT, Green represents TYK2V678F, Blue represents TYK2WT. (c)The distance of F617-F617 in JAK2V617F. (d)The distance of V617-V617 in JAK2WT. (e)The distance of F678-F678 in TYK2V678F. (f)The distance of V678-V678 in TYK2WT. The two mutants enhance dimer stability through distinct mechanisms: JAK2V617F leverages electrostatic reinforcement and optimization of hydrogen bond, whereas TYK2V678F relies primarily on spatial compaction. These adaptations reinforce the JH2-mediated dimer interfaces, prolong JH1 activation, and sustain dysregulation of JAK/STAT signaling, aligning with their established roles in pathological conditions. We analyzed hydrogen bond in the pseudokinase domain (PK-PK) interaction regions of wild-type (WT) and mutant dimers of JAK2 and TYK2. This revealed dynamic changes, including the breaking and reforming of bonds, as well as shifts in bond occupancy (see S1 and S2 Tables ). In the wild-type JAK2 dimer (shown in red in Fig 3a ), most intermolecular hydrogen bond are located within the JH2 pseudokinase domain (residues 589–875), with additional bonds in residues 100–200, such as R235-E173 and R130-D519. However, in the JAK2V617F mutant dimer (shown in black in Fig 3a ), these bonds outside the JH2 domain are absent. Instead, there is a higher density and occupancy of hydrogen bond at the PK-PK interface. This increase suggests that the dimer becomes more stable, likely boosting JH1 kinase activity through stronger JH2-mediated interactions. On the other hand, the wild-type TYK2 dimer (shown in blue in Fig 3a ) has hydrogen bond only in the JH2 domain, specifically in residues 500–600. In contrast, the TYK2V678F mutant dimer (shown in green in Fig 3a ) forms new hydrogen bond in residues 100–300, such as R240-E174 and R249-E173, within the N-terminal FERM domain (residues 26–431). Although TYK2V678F has fewer total hydrogen bond than JAK2, it achieves greater overall stability through FERM-domain interactions. These interactions bring domains closer together and cause the JH1 kinase to adopt an open conformation, known as “clamp opening.” This open structure makes it easier for substrates to bind and be phosphorylated, keeping downstream signaling pathways active. Therefore, both of mutants enhance stability via hydrogen bonding, while the JAK2V617F mutation stabilizes the dimer through the JH2 domain, the TYK2V678F mutation stabilizes the dimer in the FERM domain By comparing the dynamics of salt bridges in JAK2 and TYK2 dimers, we identified how each mutation stabilizes the protein differently (see Fig 3b ). In the JAK2V617F mutant dimer (shown in black in Fig 3b ), two salt bridges, E592-K539 and E596-K539, are highly stable (both at 97% occupancy) and dominate the PK-PK interface (residues 589–875). In contrast, these salt bridges are less stable in the wild-type JAK2 dimer (shown in red in Fig 3b ), with occupancies of 65% and 18%, respectively (see S3 Table ). This increase in stability supports the idea that JAK2V617F strengthens dimer stability via the JH2 domain, leading to constant kinase activity. The high stability of salt bridges involving K539 suggests it acts as a key point for electrostatic interactions, possibly making the mutant dimer harder to break apart—a feature linked to myeloproliferative neoplasms. Meanwhile, the salt bridge between E543 and K728 is less stable in the mutant (occupancy drops from 42% to 30%), likely due to structural stress caused by the mutation. In the TYK2V678F mutant dimer (shown in green in Fig 3b ), there is a notable change in salt bridge locations. In the wild-type TYK2 dimer (shown in blue in Fig 3b ), the main salt bridges at the PK-PK interface, E596-K539 (59% occupancy) and E592-K539 (38%), disappear in the mutant. Instead, new ionic interactions form in the FERM domain (residues 26–431) (see S4 Table ). This shift aligns with structural studies showing that tightening of the FERM domain causes the JH1 kinase to open like a clamp. Even though the PK-PK salt bridges are lost, TYK2V678F has more salt bridges overall, supported by charge-assisted hydrogen bond that provide stability through long-range electrostatic forces. This FERM-centered network brings different parts of the protein closer together, similar to how engineered proteins are designed to assemble through ionic interactions. These results highlight distinct stabilization strategies: the JAK2V617F mutation strengthens existing salt bridges at the PK-PK interface to keep the kinase active, while the TYK2V678F mutation uses the flexibility of the FERM domain to create new ionic interactions elsewhere. Both approaches make it easier for the JH1 kinase to phosphorylate substrates, though they differ in their spatial and electrostatic mechanisms. These findings suggest that developing drugs targeting the unique salt bridge patterns in each mutant—such as focusing on K539 in JAK2V617F—could help stop the abnormal signaling caused by these mutations. Difference in Electrostatic Force and Energy Between Wild-Type and Mutant Proteins Analysis of electrostatic forces between protein dimer chains, quantified through real-time force-distance profiles with a 3Å step size (see Fig 4a ), revealed distinct patterns in JAK2 and TYK2 dimers. In JAK2, the JAK2V617F mutant exhibited markedly higher electrostatic forces than its wild-type counterpart (JAK2WT) within the 20–32Å separation range, with initial forces approximately three times stronger (shown in Black and Red in Fig 4a ). This pronounced electrostatic attraction correlates with enhanced inter-chain binding, likely stabilizing the dimer and prolonging downstream signaling pathway activation. Download figure Open in new tab Fig 4. The force and energy analysis of proteins (a)The Electrostatic force of different distances in four dimers. Black represents JAK2V617F, Red represents JAK2WT, Green represents TYK2V678F, Blue represents TYK2WT. (b)The Energy analysis including Electrostatic component, Van der Waals component, Polar solvation component, Nonpolar solvation component and Binding free energy between monomers. Black represents JAK2V617F, Red represents JAK2WT, Green represents TYK2V678F, Blue represents TYK2WT. In contrast, the TYK2V678F mutant displayed negligible electrostatic forces until the force-distance curves converged at approximately 41Å, differing sharply from the predominant repulsive forces observed in TYK2WT across the 23–41Å range (shown in Green and Blue in Fig 4a ). This reduction in repulsion in TYK2V678F stabilizes the dimer by maintaining a low-energy state during conformational transitions, thereby lowering energy barriers. Consequently, this low-energy state enhances accessibility of the JH1 kinase domain, facilitating unimpeded substrate binding and increased phosphorylation activity. Thus, while both mutants sustain pathway activation, they employ divergent electrostatic strategies. The JAK2V617F mutation stabilizes the dimer by enhancing electrostatic interactions, whereas the TYK2V678F mutation stabilizes the dimer by reducing the overall conformational energy. We conducted MM/PBSA binding free energy calculations for four protein dimers, analyzing both the total binding free energy and its individual components: electrostatic, van der Waals, polar, and non-polar (see Fig 4b ). The stability of these dimers correlates with their energy values, where negative values enhance stability, and positive values diminish it. The electrostatic interaction for JAK2V617F is calculated at −180.20 (see Table S5 ), compared to −179.36 for JAK2WT (see Table S6 ). This slightly lower value for JAK2V617F suggests marginally enhanced stability relative to its wild-type counterpart. In contrast, TYK2V678F exhibits a substantially lower electrostatic interaction value of −577.43 (see Table S7 ) compared to −78.31 for TYK2WT (see Table S8 ), indicating a significant increase in stability for the mutant form. Although van der Waals forces between the protein monomers are relatively weak, a clear trend emerges. Specifically, JAK2V617F demonstrates a van der Waals force of −61.48 (see Table S5 ), which is more negative than that of JAK2WT at −43.53 (see Table S6 ). Similarly, TYK2V678F demonstrates a value of −86.29 (see Table S7 ), compared to −49.61 for TYK2WT (see Table S8 ). These findings indicate that the mutant forms possess stronger van der Waals interactions than their wild-type counterparts, thereby contributing to their enhanced stability. Polar interactions across all four proteins yield positive values, signifying a destabilizing effect. For JAK2V617F and JAK2WT, these values are identical, suggesting that the mutation does not influence stability through polar interactions. Conversely, TYK2V678F exhibits a markedly higher polar interaction value of 637.6 (see Table S7 ) compared to 98 for TYK2WT (see Table S8 ). This substantial increase implies that the mutation in TYK2 may compromise stability, particularly in polar environments, potentially leading to structural instability. Nevertheless, the overall binding free energy of TYK2V678F remains lower than that of TYK2WT, indicating greater overall stability. This apparent paradox may result from mutation-induced structural changes that, while increasing polar interactions and potentially exposing the protein structure, also enhance active site accessibility, thereby facilitating substrate binding and downstream pathway activation. Non-polar interactions, though of the smallest magnitude among the energy components, contribute positively to stability due to their negative values. JAK2V617F demonstrates a non-polar interaction value of −15.29 (see Table S5 ), more negative than JAK2WT’s −11.43 (see Table S6 ). Similarly, TYK2V678F demonstrates a value of −21.86 (see Table S7 ), compared to −12.22 for TYK2WT (see Table S8 ). These results suggest that the mutant forms exhibit stronger non-polar interactions than their wild-type counterparts, further reinforcing their increased stability. The binding free energy, integrating the aforementioned energy components, was evaluated during the final 20 ns of the simulation (see Figure S1 ), where energies fluctuated within a stable range, indicating system equilibrium. JAK2V617F exhibits a binding free energy of −53.79 (see Table S5 ), lower than JAK2WT’s −31.53 (see Table S6 ), suggesting greater stability for the mutant form. Likewise, TYK2V678F exhibits a binding free energy of −47.99 (see Table S7 ), compared to −12.22 for TYK2WT (see Table S8 ), further confirming enhanced stability. Collectively, these findings demonstrate that both mutant forms possess greater stability than their wild-type counterparts, potentially resulting in prolonged dimerization and aberrant activation of the JAK-STAT pathway, which may contribute to immune cell dysfunction. Discussion and Conclusion In the case of JAK2V617F, increased structural stability, as evidenced by reduced RMSF and RMSD values, along with enhanced interactions between the dimers, characterized by higher occupancy of hydrogen bonds and salt bridges, accompanied by a decrease in the overall energy of the protein dimer, reflected in a reduced binding free energy, lead to a more rigid JH2-mediated dimerization.This rigidification paradoxically enhances the autoinhibitory function of JH2, thereby alleviating the suppression of the JH1 kinase domain and augmenting its phosphorylation activity. The resultant prolonged stability of the dimer perpetuates pathogenic JAK/STAT signaling, which in turn drives immune dysregulation and thrombocythemia. In the case of TYK2V678F, although global stabilization is similarly observed with lower RMSF and RMSD values, the primary effects are concentrated in the N-terminal FERM-SH2-PK regions. Here, an increase in hydrogen bond occupancy and charge density leads to reduced inter-domain distances, effectively compacting the N-terminal structure. Concurrently, this compaction induces the C-terminal JH1 kinase domain to adopt an open, “clamp-like” conformation, which facilitates substrate binding and phosphorylation. In contrast to JAK2V617F, the stability in TYK2V678F is achieved through the attenuation of electrostatic repulsion and the integration of N-terminal domains, rather than simply enhancing stability via strengthened dimer interactions. Both mutations result in dimeric states that are resistant to dissociation, thereby perpetuating hyperactive JAK/STAT signaling. Provide a concise summary in a single sentence: JAK2V617F enhances the release of JH2-mediated autoinhibition, whereas TYK2V678F reconfigures the domain architecture to optimize accessibility to the JH1 kinase domain. By employing molecular dynamics simulations and analyzing RMSF, RMSD, hydrogen bonding, salt bridges, surface electrostatic charges, electric field forces, and binding free energy in proteins harboring the JAK2V617F and TYK2V678F mutations, we elucidate the molecular mechanisms by which these mutations drive thrombocythemia[ 36 ]. These insights lay a robust foundation for the development of targeted therapeutics specifically designed to counteract the pathogenic effects of these mutations, thereby offering promising avenues for the effective treatment or potential eradication of thrombocythemia. Furthermore, the integrated molecular dynamics research framework developed in this work can be readily extended to explore other mutations potentially involved in disorders, thus broadening our understanding of its molecular etiology. In the future, the integration of advanced computational techniques with experimental validation will continue to play a pivotal role in elucidating the mechanisms underlying JAK family dysregulation and its associated diseases. In conclusion, JAK2 and TYK2 are pivotal in cytokine signaling and are implicated in a range of cancers and hematological disorders. Their mutant forms JAK2V617F and TYK2V678F are the primary drivers of thrombocythemia. This study utilizes molecular dynamics simulations and computational biophysical methods to comprehensively analyze the pathogenic mechanisms underlying these two mutations. Molecular dynamics simulations have emerged as a potent tool for elucidating the structural and functional consequences of mutations in these kinases. The insights garnered from such studies are poised to significantly advance the development of targeted therapeutic interventions and deepen our comprehension of the molecular mechanisms that underpin these diseases. Supporting information Download figure Open in new tab S1 Fig. The line chart of Binding Free Energy analysis The details of Binding Free Energy between the monomers in last 5ns simulation. Black represents JAK2V617F, Red represents JAK2WT, Green represents TYK2V678F, Blue represents TYK2WT. S1 Table.The details of Interchain hydrogen bond of JAK2WT and JAK2V617F. The analysis incorporates hydrogen bond occupancy rates for individual donor-acceptor pairs and identifies corresponding interacting residue pairs. S2 Table.The details of Interchain hydrogen bond of TYK2WT and TYK2V678F. The analysis incorporates hydrogen bond occupancy rates for individual donor-acceptor pairs and identifies corresponding interacting residue pairs. S3 Table.The details of Interchain Salt Bridges of JAK2WT and JAK2V617F. The analysis incorporates salt bridges occupancy rates for individual donor-acceptor pairs and identifies corresponding interacting residue pairs. S4 Table.The details of Interchain Salt Bridges of TYK2WT and TYK2V678F. The analysis incorporates salt bridges occupancy rates for individual donor-acceptor pairs and identifies corresponding interacting residue pairs. S5 Table.The details of Energy analysis in JAK2V617F. The table comprises electrostatic component, van der Waals component, polar component, and non-polar component and binding free energy, with their respective mean values and standard deviations systematically quantified. S6 Table.The details of Energy analysis in JAK2WT. The table comprises electrostatic component, van der Waals component, polar component, and non-polar component and binding free energy, with their respective mean values and standard deviations systematically quantified. S7 Table.The details of Energy analysis in TYK2V678F. The table comprises electrostatic component, van der Waals component, polar component, and non-polar component and binding free energy, with their respective mean values and standard deviations systematically quantified. S8 Table.The details of Energy analysis in TYK2WT. The table comprises electrostatic component, van der Waals component, polar component, and non-polar component and binding free energy, with their respective mean values and standard deviations systematically quantified. Acknowledgments This project is supported by the grants from the National Natural Science Foundation of China (82404104) and the Natural Science Foundation of Hunan Province (2024JJ6492). References 1. ↵ Sun S , Rodriguez G , Xie Y , Guo W , Hernandez AEL , Sanchez JE , et al. Phosphorylation of tyrosine 841 plays a significant role in JAK3 activation . Life . 2023 ; 13 ( 4 ): 981 . OpenUrl CrossRef PubMed 2. ↵ Rodriguez Moncivais OJ , Chavez SA , Estrada Jimenez VH , Sun S , Li L , Kirken RA , et al. Structural Analysis of Janus Tyrosine Kinase Variants in Hematological Malignancies: Implications for Drug Development and Opportunities for Novel Therapeutic Strategies . International Journal of Molecular Sciences . 2023 ; 24 ( 19 ). doi: 10.3390/ijms241914573 . OpenUrl CrossRef 3. ↵ Stetka J , Usart M , Kubovcakova L , Rai S , Nageswara Rao T , Sutter J , et al. Iron is a modifier of the phenotypes of JAK2-mutant myeloproliferative neoplasms . Blood . 2023 . doi: 10.1182/blood.2022017976 . OpenUrl CrossRef 4. ↵ Glassman CR , Tsutsumi N , Saxton RA , Lupardus PJ , Jude KM , Garcia KC . Structure of a Janus kinase cytokine receptor complex reveals the basis for dimeric activation . Science . 2022 ; 376 ( 6589 ): 163 - 9 . OpenUrl CrossRef PubMed 5. ↵ Shide K , Shimoda K , Kamezaki K , Kakumitsu H , Kumano T , Numata A , et al. Tyk2 mutation homologous to V617F Jak2 is not found in essential thrombocythaemia, although it induces constitutive signaling and growth factor independence . Leukemia Research . 2007 ; 31 ( 8 ): 1077 – 84 . doi: 10.1016/j.leukres.2006.08.018 . OpenUrl CrossRef PubMed 6. ↵ Wöss K , Simonović N , Strobl B , Macho-Maschler S , Müller M . TYK2: An Upstream Kinase of STATs in Cancer . Cancers . 2019 ; 11 ( 11 ). doi: 10.3390/cancers11111728 . OpenUrl CrossRef PubMed 7. ↵ Tuckerman ME , Martyna GJ . Understanding modern molecular dynamics: Techniques and applications . ACS Publications ; 2000 . p. 159 – 78 . 8. ↵ Ul Haq F , Abro A , Raza S , Liedl KR , Azam SS . Molecular dynamics simulation studies of novel β-lactamase inhibitor . Journal of Molecular Graphics and Modelling . 2017 ; 74 : 143 – 52 . doi: 10.1016/j.jmgm.2017.03.002 . OpenUrl CrossRef PubMed 9. ↵ Lee TS , Ma W , Zhang X , Giles F , Kantarjian H , Albitar M . Mechanisms of constitutive activation of Janus kinase 2-V617F revealed at the atomic level through molecular dynamics simulations . Cancer . 2009 ; 115 ( 8 ): 1692 – 700 . doi: 10.1002/cncr.24183 . OpenUrl CrossRef PubMed 10. ↵ Sargsyan K , Grauffel C , Lim C . How Molecular Size Impacts RMSD Applications in Molecular Dynamics Simulations . Journal of Chemical Theory and Computation . 2017 ; 13 ( 4 ): 1518 – 24 . doi: 10.1021/acs.jctc.7b00028 . OpenUrl CrossRef PubMed 11. ↵ Sun S , Rodriguez G , Zhao G , Sanchez JE , Guo W , Du D , et al. A novel approach to study multi-domain motions in JAK1’s activation mechanism based on energy landscape . Briefings in Bioinformatics . 2024 ; 25 ( 2 ). doi: 10.1093/bib/bbae079 . OpenUrl CrossRef 12. ↵ Consortium U . UniProt: a worldwide hub of protein knowledge . Nucleic acids research . 2019 ; 47 ( D1 ): D506 – D15 . OpenUrl CrossRef PubMed 13. ↵ Rodriguez G , Martinez GS , Negrete OD , Sun S , Guo W , Xie Y , et al. JAK3 Y841 Autophosphorylation Is Critical for STAT5B Activation, Kinase Domain Stability and Dimer Formation . International Journal of Molecular Sciences . 2023 ; 24 ( 15 ). doi: 10.3390/ijms241511928 . OpenUrl CrossRef 14. ↵ Guex N , Peitsch MC . SWISS-MODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling . Electrophoresis . 2005 ; 18 ( 15 ): 2714 – 23 . doi: 10.1002/elps.1150181505 . OpenUrl CrossRef 15. ↵ Price DJ , Brooks III CL . A modified TIP3P water potential for simulation with Ewald summation . The Journal of chemical physics . 2004 ; 121 ( 20 ): 10096 – 103 . OpenUrl CrossRef PubMed 16. ↵ Jo S , Kim T , Iyer VG , Im W . CHARMM-GUI: A web-based graphical user interface for CHARMM . Journal of Computational Chemistry . 2008 ; 29 ( 11 ): 1859 – 65 . doi: 10.1002/jcc.20945 . OpenUrl CrossRef PubMed 17. ↵ Huang J , Rauscher S , Nawrocki G , Ran T , Feig M , de Groot BL , et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins . Nature Methods . 2016 ; 14 ( 1 ): 71 – 3 . doi: 10.1038/nmeth.4067 . OpenUrl CrossRef PubMed 18. ↵ York DM , Darden TA , Pedersen LG . The effect of long-range electrostatic interactions in simulations of macromolecular crystals: A comparison of the Ewald and truncated list methods . The Journal of Chemical Physics . 1993 ; 99 ( 10 ): 8345 – 8 . doi: 10.1063/1.465608 . OpenUrl CrossRef 19. ↵ Phillips JC , Braun R , Wang W , Gumbart J , Tajkhorshid E , Villa E , et al. Scalable molecular dynamics with NAMD . Journal of Computational Chemistry . 2005 ; 26 ( 16 ): 1781 – 802 . doi: 10.1002/jcc.20289 . OpenUrl CrossRef PubMed Web of Science 20. ↵ Humphrey W , Dalke A , Schulten K . VMD: visual molecular dynamics . Journal of molecular graphics . 1996 ; 14 ( 1 ): 33 – 8 . OpenUrl CrossRef PubMed Web of Science 21. ↵ Sun S , Karki C , Gao BZ , Li L . Molecular mechanisms of cardiac actomyosin transforming from rigor state to post-rigor state . The Journal of Chemical Physics . 2022 ; 156 ( 3 ). doi: 10.1063/5.0078166 . OpenUrl CrossRef 22. ↵ Chen C , Zhang N . The effect of geometrical criteria on hydrogen bond analysis in aqueous glycerol solutions . Journal of Molecular Imaging & Dynamics . 2011 . 23. ↵ Li L , Li C , Sarkar S , Zhang J , Witham S , Zhang Z , et al. DelPhi: a comprehensive suite for DelPhi software and associated resources . BMC biophysics . 2012 ; 5 : 1 – 11 . OpenUrl CrossRef PubMed 24. ↵ Sun S , Xu H , Xie Y , Sanchez JE , Guo W , Liu D , et al. HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules . Computational and Structural Biotechnology Journal . 2023 ; 21 : 1383 – 9 . doi: 10.1016/j.csbj.2023.02.013 . OpenUrl CrossRef 25. ↵ Sun S , Lopez JA , Xie Y , Guo W , Liu D , Li L . HIT web server: A hybrid method to improve electrostatic calculations for biomolecules . Computational and Structural Biotechnology Journal . 2022 ; 20 : 1580 – 3 . doi: 10.1016/j.csbj.2022.03.022 . OpenUrl CrossRef 26. ↵ Sun S , Karki C , Xie Y , Xian Y , Guo W , Gao BZ , et al. Hybrid method for representing ions in implicit solvation calculations . Computational and Structural Biotechnology Journal . 2021 ; 19 : 801 – 11 . doi: 10.1016/j.csbj.2021.01.020 . OpenUrl CrossRef 27. ↵ Li L , Chakravorty A , Alexov E . DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding . Journal of Computational Chemistry . 2017 ; 38 ( 9 ): 584 – 93 . doi: 10.1002/jcc.24715 . OpenUrl CrossRef PubMed 28. ↵ Dolinsky TJ , Nielsen JE , McCammon JA , Baker NA . PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations . Nucleic Acids Research . 2004 ; 32 ( Web Server ): W665 - W7 . doi: 10.1093/nar/gkh381 . OpenUrl CrossRef PubMed Web of Science 29. ↵ Sun S , Poudel P , Alexov E , Li L . Electrostatics in Computational Biophysics and Its Implications for Disease Effects . International Journal of Molecular Sciences . 2022 ; 23 ( 18 ). doi: 10.3390/ijms231810347 . OpenUrl CrossRef 30. ↵ White KA , Ruiz DG , Szpiech ZA , Strauli NB , Hernandez RD , Jacobson MP , et al. Cancer-associated arginine-to-histidine mutations confer a gain in pH sensing to mutant proteins . Science Signaling . 2017 ; 10 ( 495 ). doi: 10.1126/scisignal.aam9931 . OpenUrl Abstract / FREE Full Text 31. ↵ Pettersen EF , Goddard TD , Huang CC , Couch GS , Greenblatt DM , Meng EC , et al. UCSF Chimera—A visualization system for exploratory research and analysis . Journal of Computational Chemistry . 2004 ; 25 ( 13 ): 1605 – 12 . doi: 10.1002/jcc.20084 . OpenUrl CrossRef PubMed Web of Science 32. ↵ Li C , Li L , Zhang J , Alexov E . Highly efficient and exact method for parallelization of grid-based algorithms and its implementation in DelPhi . Journal of Computational Chemistry . 2012 ; 33 ( 24 ): 1960 – 6 . doi: 10.1002/jcc.23033 . OpenUrl CrossRef PubMed 33. ↵ Li L , Li C , Zhang Z , Alexov E . On the Dielectric “Constant” of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi . Journal of Chemical Theory and Computation . 2013 ; 9 ( 4 ): 2126 – 36 . doi: 10.1021/ct400065j . OpenUrl CrossRef PubMed 34. ↵ Hubbard SJ , Thornton J. NACCESS: program for calculating accessibilities . Department of Biochemistry and Molecular Biology, University College of London . 1992 . 35. ↵ Van Der Spoel D , Lindahl E , Hess B , Groenhof G , Mark AE , Berendsen HJC . GROMACS: Fast, flexible, and free . Journal of Computational Chemistry . 2005 ; 26 ( 16 ): 1701 – 18 . doi: 10.1002/jcc.20291 . OpenUrl CrossRef PubMed Web of Science 36. ↵ Sun S. Developing and Applying Computational Methods on Biomolecules: The University of Texas at El Paso ; 2023 . View the discussion thread. Back to top Previous Next Posted May 16, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting Tianwen Shao , Min Gan , Qi Yin , Jingyan Bai , Jiayi Yang , Shengjie Sun bioRxiv 2025.05.16.654435; doi: https://doi.org/10.1101/2025.05.16.654435 Share This Article: Copy Citation Tools Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting Tianwen Shao , Min Gan , Qi Yin , Jingyan Bai , Jiayi Yang , Shengjie Sun bioRxiv 2025.05.16.654435; doi: https://doi.org/10.1101/2025.05.16.654435 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Biophysics Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13895) Bioinformatics (41951) Biophysics (21456) Cancer Biology (18594) Cell Biology (25520) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22510) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88622) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)