Shared information between residues is sufficient to detect pair-wise epistasis in a protein
In a comment on our manuscript "Strong selection significantly increases epistatic interactions in the long-term evolution of a protein", Dr. Crona challenges our assertion that shared entropy (that is, information) between two residues implies epistasis between those residues, by constructing an explicit example of three loci (say A, B, and C), where A and B are epistatically linked (leading to shared entropy between A and B), and A and C also depend epistatically (leading to shared entropy between A and C), so that loci B and C are correlated (share entropy).
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Gra…
Feature EngineeringImage-based ground distance detection for crop-residue-covered soil
Conservation agriculture features a soil surface covered with crop residues, which brings benefits of improving soil health and saving water. However, one significant challenge in conservation agriculture lies in precise…
Deciphering general characteristics of residues constituting allosteric communication paths
Considering all the PDB annotated allosteric proteins (from ASD - AlloSteric Database) belonging to four different classes (kinases, nuclear receptors, peptidases and transcription factors), this work has attempted to de…
Identifying critical residues of a protein using meaningfully-thresholded Random Geometric Graphs
Identification of critical residues of a protein is actively pursued, since such residues are essential for protein function. We present three ways of recognising critical residues of an example protein, the evolution of…
Rethinking Minimal Sufficient Representation in Contrastive Learning
Contrastive learning between different views of the data achieves outstanding success in the field of self-supervised representation learning and the learned representations are useful in broad downstream tasks. Since al…
Contrastive LearningRepresentation Learning