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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

Sreetama Das¹, Pratiti Bhadra¹, Suryanarayanarao Ramakumar¹

¹Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India.

Journal of Proteome Research

|June 22, 2017

Summary

Incorporating both static cis-peptide information and protein dynamics enhances protein function prediction. Combining these methods improves accuracy by reducing errors and detecting true functional matches in automated annotation.

Keywords:

Gene Ontology autocorrelation vector cis-peptide fragment coarse-grained force field fragment-based method function annotation geometric clustering molecular dynamics simulation sequence-structure patterns validation

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Biochemistry
Structural Biology
Bioinformatics

Background:

Cis-peptide bonds, though rare, are conserved and crucial for protein function.
Existing methods use static cis-peptide information for homology-independent protein function annotation.
Protein dynamics are integral to molecular activity, including cis-trans isomerization.

Purpose of the Study:

To improve protein molecular function prediction by integrating static cis-peptide and dynamic information.
To address limitations of using cis-peptide information alone, especially with cis-trans isomerization.
To overcome false positives generated by dynamics information alone.

Main Methods:

Incorporated both static cis-peptide data and protein dynamics information.
Developed a combined approach for protein function annotation.
Evaluated the performance against methods using only static or dynamic information.

Main Results:

Cis-peptide information alone fails in cases of cis-trans isomerization or incorrect bond assignment.
Dynamics information alone can lead to false positives due to similar secondary structures and dynamics.
The combined method significantly reduces errors and enhances the detection of true functional matches.

Conclusions:

A combined approach of static cis-peptide and dynamics information is superior for protein function annotation.
This integrated strategy overcomes limitations of individual methods.
The combined approach offers a promising avenue for advancing automated protein function prediction methodologies.