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Related Experiment Video

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Homology modeling in a dynamical world.

Alexander Miguel Monzon¹, Diego Javier Zea², Cristina Marino-Buslje²

¹Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, CONICET, B1876BXD, Bernal, Argentina.

Protein Science : a Publication of the Protein Society

|August 18, 2017

Summary

Conformational diversity in proteins reduces the correlation between sequence and structure, impacting template-based modeling. Structure-function relationships and order/disorder information can improve modeling accuracy for dynamic proteins.

Keywords:

conformational diversity homology modeling protein dynamics protein sequence protein structure

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Area of Science:

Structural Biology
Computational Biology
Bioinformatics

Background:

Template-based modeling (TBM) relies on the sequence-structure correlation in homologous proteins.
Native state conformational diversity can decouple sequence and structural divergence.

Purpose of the Study:

To investigate the impact of conformational diversity on the sequence-structure divergence relationship.
To assess how conformational diversity affects template-based modeling (TBM).

Main Methods:

Analysis of conformational diversity across protein families.
Evaluation of the correlation between sequence and structural divergence under varying conformational diversity.
Exploration of structure-function relationships and protein order/disorder as predictive factors for TBM.

Main Results:

Conformational diversity can be substantial, comparable to maximum structural divergence.
Increased conformational diversity significantly weakens the sequence-structure correlation, introducing noise.
Structure-function information and order/disorder status can mitigate noise and improve TBM for dynamic proteins.

Conclusions:

Conformational diversity poses a challenge to TBM accuracy, especially for highly dynamic proteins.
Leveraging structure-function relationships and order/disorder information enhances TBM performance by resolving noise caused by conformational flexibility.