Amino acid impact factor

Abstract

Amino acid mutations in proteins are random and those mutations which are beneficial or neutral survive during the course of evolution. Conservation or co-evolution analyses are performed on the multiple sequence alignment of homologous proteins to understand how important different amino acids or groups of them are. However, these traditional analyses do not explore the directed influence of amino acid mutations, such as compensatory effects. In this work we develop a method to capture the directed evolutionary impact of one amino acid on all other amino acids, and provide a visual network representation for it. The method developed for these directed networks of inter- and intra-protein evolutionary interactions can also be used for noting the differences in amino acid evolution between the control and experimental groups. The analysis is illustrated with a few examples, where the method identifies several directed interactions of functionally critical amino acids. The impact of an amino acid is quantified as the number of amino acids that are influenced as a consequence of its mutation, and it is intended to summarize the compensatory mutations in large evolutionary sequence data sets as well as to rationally identify targets for mutagenesis when their functional significance can not be assessed using structure or conservation.

Deep2Full: Evaluating strategies for selecting the minimal mutational experiments for optimal computational predictions of deep mutational scan outcomes

“…This group is supposed to reflect the importance of an amino acid in an undirected coevolutionary network-(11) Average co-evolution score of each amino acid (12) Degree centrality, the number of nodes to which a node is connected (13) Betweenness centrality, quantifying the importance of a node in connecting other pairs of residues (14) Closeness centrality, the inverse of the sum of distances to all other nodes (15) Eigenvector centrality, which considers not just the number of connections a node has, but also the connectivity of the immediately connected nodes. Directed network information is also included in the model-(16) Impact factor [47], the number of compensatory mutations required for mutations at a residue position is calculated based on conditional probabilities (17) Dependency factor, which is the counterpart of impact factor is the number of residues which are likely to influence a mutation at a given position. Details about calculation of these parameters are described in the following sub-sections.…”

“…The impact and dependency are supposed to summarize how many simultaneous mutations are forced or forced-upon by a mutation. [47] Average commute time…”

Disentangling the contribution of each descriptive characteristic of every single mutation to its functional effects

“…Impact and I8. Dependence defined as asymmetric co-evolutionary parameters [25] AI model. Using the 18 descriptive parameters and the experimental measurements for each of the mutations, the AI analyses were performed using Python.…”

Amino acid interacting network in the receptor-binding domain of SARS-CoV-2 spike protein

“…Conservation of AA sequence is considered to hold the key for understanding many of the biological and evolution process. 2 A highly conserved sequence is one that has remained relatively unchanged far up in the phylogenetic tree, and hence way back into geological time scale. Traditional analysis of conservation is based on the comparative study of the alignment of AA sequence of different proteins from closely related species or at different times.…”

“…There have been some recent efforts to go beyond this liner sequence model. 2 However, most such analysis depend on statistical methods and probability theory such as multiple sequence alignment (MSA), 2 statistical coupling analysis (SCA), 3 direct coupling analysis (DCA) 4 etc. They all based on the homologous sequence, and sequence alone, of the AAs in the protein.…”