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by claude@2026-06, 2026-06-24
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The paper introduces evo3D, an R package that provides a structure-informed framework for molecular evolution by extracting spatial haplotypes—multiple sequence alignment subsets defined by 3D protein structural context—so users can compute a broader range of downstream statistics than prior structure-informed methods. The authors implement fixed-count and fixed-distance spatial windows, residue and codon analysis modes, and extend the approach to multimers, interfaces, and multiple structural models via a unified wrapper, run_evo3d(), and demonstrate scalability using viral structural assemblies. As examples, they perform an epitope-level diversity scan of the Hepatitis C virus E1/E2 complex and show that evo3D can identify conserved spatial neighbourhoods missed by linear sliding-window approaches, while a caveat is that the paper focuses on demonstrating the framework through specific use cases rather than validating across a wide range of evolutionary questions. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
At the molecular level, selection pressures often act on protein structural features, yet most evolutionary analyses remain confined to linear sequences. Early structure-informed approaches improved interpretability by mapping single-site metrics onto protein structures, and later methods introduced 3D sliding windows to capture spatially clustered signals missed by linear window approaches. These frameworks, however, are restricted to predefined statistics and narrowly defined 3D window types, limiting the scope of questions that can be addressed. We developed an R package, evo3D , as a new framework for structure-informed evolutionary analysis that supports a wide range of downstream statistics and scales from simple to complex structures. evo3D extracts structure-informed multiple sequence alignment subsets (spatial haplotypes), making the structure-informed unit of analysis directly available to users. The framework supports fixed-count and fixed-distance spatial windows, introduces residue and codon analysis modes, and extends to multimers, interfaces, and multiple structural models through a single wrapper, run_evo3d() . We demonstrate evo3D ’s utility by performing an epitope-level diversity scan of Hepatitis C virus E1/E2 complex, identifying conserved spatial neighbourhoods missed by linear sliding windows, and by evaluating evo3D ’s scalability on the octameric Chikungunya virus E1/E2 assembly. Importantly, evo3D formalises the core components of structure-informed analysis of molecular evolution and removes technical barriers. As a result, the framework streamlines the evaluation of evolutionary patterns directly within 3D structural contexts, and we anticipate its wide application in molecular evolution studies. The package is available at github.com/bbroyle/evo3D.
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Abstract
At the molecular level, selection pressures often act on protein structural features, yet most evolutionary analyses remain confined to linear sequences. Early structure-informed approaches improved interpretability by mapping single-site metrics onto protein structures, and later methods introduced 3D sliding windows to capture spatially clustered signals missed by linear window approaches. These frameworks, however, are restricted to predefined statistics and narrowly defined 3D window types, limiting the scope of questions that can be addressed. We developed an R package, evo3D, as a new framework for structure-informed evolutionary analysis that supports a wide range of downstream statistics and scales from simple to complex structures. evo3D extracts structure-informed multiple sequence alignment subsets (spatial haplotypes), making the structure-informed unit of analysis directly available to users. The framework supports fixed-count and fixed-distance spatial windows, introduces residue and codon analysis modes, and extends to multimers, interfaces, and multiple structural models through a single wrapper, run_evo3d(). We demonstrate evo3D’s utility by performing an epitope-level diversity scan of Hepatitis C virus E1/E2 complex, identifying conserved spatial neighbourhoods missed by linear sliding windows, and by evaluating evo3D’s scalability on the octameric Chikungunya virus E1/E2 assembly. Importantly, evo3D formalises the core components of structure-informed analysis of molecular evolution and removes technical barriers. As a result, the framework streamlines the evaluation of evolutionary patterns directly within 3D structural contexts, and we anticipate its wide application in molecular evolution studies. The package is available at github.com/bbroyle/evo3D.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
we have revised our title to “evo3D R package: a spatial haplotype framework for structure-informed analysis of molecular evolution” to better inform the reader of the package’s general purpose. We improved clarity and reduced potential confusion regarding evo3D’s applications and scope. We better situated evo3D within the existing methodological landscape, and to discuss between-species MSA analyses more explicitly, including the addition of a summary of innovations. Several sections have been expanded to clarify scope, including discussion of applications beyond the presented use cases and situations in which evo3D may not apply.
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