Abstract
Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable.
Highlights
The revolutions in biotechnology of the past two decades opened an unprecedented ability to analyze and organize biological information
The number of hot residues is usually smaller than the number of contact or first-layer residues
This paper addresses the physical interactions of proteins, which is an important issue in molecular biology and biophysics
Summary
The revolutions in biotechnology of the past two decades opened an unprecedented ability to analyze and organize biological information. More than 90 million protein sequences have been deciphered so far, and that number grows at an enormous rate [4], but the sequencing data alone is not sufficient to fully grasp the biological process on the molecular level. The capacity to generate and adequately connect structural data, i.e., protein, DNA, and RNA structures, to biological processes is diminutive in comparison to the sequencing yield or even to diagnostic abilities. The past decade did not rectify this issue, as explained in [7]. New approaches, such as the one described in this manuscript, or the analysis of ligand binding behavior within a framework of chemico-biological space [8], may be a way toward a much better compound filtering during preclinical trials and toward a more efficient drug design
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
