Heterodimer binding scaffolds recognition via the analysis of kinetically hot residues
Ognjen Peri\v{s}i\'c

TL;DR
This paper introduces an algorithm using the Gaussian Network Model to identify binding patches and scaffolds in heterodimer proteins by analyzing kinetically hot residues, showing promising results especially for chains with high length ratios.
Contribution
The paper presents a novel GNM-based method for recognizing heterodimer binding scaffolds through kinetically hot residues, with adjustable mode selection for improved accuracy.
Findings
The GNM approach performs well on heterodimers with high chain length ratios.
In some cases, GNM predictions are comparable or better than statistical potentials.
Interacting chains often exhibit rigid or flexible complementary behaviors.
Abstract
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer protein chains using the Gaussian Network Model (GNM). The recognition is based on the (self)adjustable identification of kinetically hot residues, i.e., residues with highest contributions to the weighted sum of fastest modes per chain extracted via GNM, and their connection to possible binding scaffolds. The algorithm adjusts the number of modes used in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and first layer residues). This approach produces very good results when applied to chains forming heterodimers, especially with dimers with high chain length ratios. The protocol's ability to recognize…
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Taxonomy
TopicsProtein Structure and Dynamics · Computational Drug Discovery Methods · Enzyme Structure and Function
