Computational Investigation of Structural Interfaces of Protein Complexes with Short Linear Motifs

Abstract

Protein complexes with short linear motifs (SLiMs) are known to play important regulatory functions in eukaryotes. In this investigation, I have studied the structures deposited in PDB with SLiMs. The structures Were grouped into three broad categories of protein-protein, protein-peptide and the rest as others. Protein-peptide complexes Were found to be most highly represented. The interfaces Were evaluated for geometric features and conformational variables. It was observed that protein-protein and protein-peptide complexes show characteristic differences in residue pairings, which Were quantified by evaluating normalized contact residue pairing frequencies. Interface residues adopt characteristic canonical residue conformations in the Ramachandran space, with a pronounced preference for positive f conformations. It was observed that phosphorylated residues have an unusual propensity to adopt the unusual positive f conformations at the interface.

Protein–Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

“…The putative relationship between topological and/or chemical features of PP interfaces on the one hand and molecular function or biological process on the other is thus very complex and has not been rationalized to date. In fact, choosing a self-contained set of minimally correlated features as a subspace in which to successfully categorize PP complexes remains an ongoing challenge. − Because of its synthetic and predictive power, deep learning is currently gaining traction for the study of PP interfaces based on sequence, − structural data, or both. , However, the problem of selecting a feature space and efficiently encoding it for machine learning remains. A commonly used rationale to find trends in PP interfaces is to use a very large number of very diverse descriptors and let the learning algorithm pick the relevant ones.…”