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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

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Enhancing Statistical Multiple Sequence Alignment and Tree Inference Using Structural Information.

Joseph L Herman¹

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. herman@hms.harvard.edu.

Methods in Molecular Biology (Clifton, N.J.)

|October 10, 2018

Summary

This summary is machine-generated.

Protein structure conservation aids homology detection in divergent sequences. A new model integrates structural data into evolutionary analyses, improving alignment and phylogenetic tree construction.

Keywords:

Alignment uncertainty Bayesian hierarchical models Globins MCMC Molecular phylogenetics Parallel tempering Protein structure RMSD Statistical alignment Structural alignment

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Area of Science:

Computational Biology
Evolutionary Biology
Structural Bioinformatics

Background:

Detecting homology in highly divergent protein sequences is challenging due to limited sequence information.
Protein structure is often conserved even when sequences diverge significantly, offering a potential avenue for homology detection.
Existing structurally informed alignment methods often overlook underlying mutational processes.

Purpose of the Study:

To develop and present a novel stochastic model for structural evolution on phylogenetic trees.
To integrate structural information into sequence-based evolutionary models for improved phylogenetic inference.
To introduce the StructAlign plugin for the StatAlign package, enabling joint estimation of alignments and trees with structural data.

Main Methods:

Development of a stochastic model of structural evolution.
Implementation of the model as the StructAlign plugin for the StatAlign package.
Joint estimation of protein sequence alignments and phylogenetic trees incorporating structural information.

Main Results:

StructAlign facilitates the inclusion of structural information to inform the joint estimation of alignments and trees.
The plugin can infer branch-specific rates of structural evolution, revealing significant variation across phylogenetic trees.
Analysis of globin data shows higher structural conservation within clades compared to between functionally divergent proteins.

Conclusions:

Integrating structural information into evolutionary models significantly enhances the accuracy of alignment and tree construction for divergent sequences.
Structural evolution rates vary considerably across phylogenetic branches, with higher divergence rates observed between functionally distinct proteins.
Allowing for variable rates of structural divergence improves model fit to empirical structural data (RMSD values).