Protein folding, design and functional networks study using coevolution analysis

Abstract

Evolutionary information contained in the amino acid sequences of proteins species biological function and fold, but exactly what information in the contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding and functional networks analysis. In this study, we developed a web-server, named Sibe, which is used to capture positionally conserved couplings between pairwise amino acids and help protein folding and design, in which the pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several `blocks', which could contribute to driving protein folding and design. [1, 2] Also, we investigated Gα protein, which has direct interaction with GPCR, from the evolutionary view using statistical coupling analysis (SCA). SCA captured co-evolving residues among various species and organized them into functional subunits, so-called sectors. We rearranged the positions of these sectors to get more clearly distinguish networks and mapped on the structure of 2AR-G protein complex. Through these processes, we found five sectors that compose structure-to-functional networks and they are more clear than original SCA results on residue level. Furthermore, this result is consistent with previously known evolutionary determinants of Gα, and the sectors have own characteristics of receptor selectivity or conducting main functions of Gα.