Unveiling Conserved Allosteric Hot Spots in Protein Domains from Sequences

Abstract

The amino acid sequence determines the structure, function, and dynamics of a protein. In recent years, enormous progress has been made in translating sequence information into 3D structural information using artificial intelligence. However, because of the underlying methodology, it is an immense computational challenge to extract this information from the ever-increasing number of sequences. In the present study, we show that it is possible to create 2D contact maps from sequences, for which only a few exemplary structures are available on a laptop without the need for GPUs. This is achieved by using a pattern-matching approach. The resulting contact maps largely reflect the interactions in the 3D structures and contain information about its function and dynamics. This approach was used to explore the evolutionarily conserved allosteric mechanisms and identify the source- sink (driver-driven) relationships by using an established method that combines Schreibers concept of entropy transfer with a simple Gaussian network model. The validity of our method was tested on the DHFR, PDZ, SH3, and S100 domains, with our predictions consistently aligning with the experimental findings.