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.
Highlights
Anfinsen’s dogma of molecular biology postulates that the native structure and function of proteins are uniquely determined by its amino acid sequence. [1] As such there is a lot of fundamental interest in analysing the sequences of proteins
The total number of such influences exerted by the amino acid i is defined as its impact factor
Among all the proteins we studied, even though there were a few changes in the master sequences when the data set was randomly halved, there were no changes in the amino acid interaction networks, except in the case of Phosphoglycerate kinase (PGK)
Summary
Anfinsen’s dogma of molecular biology postulates that the native structure and function of proteins are uniquely determined by its amino acid sequence. [1] As such there is a lot of fundamental interest in analysing the sequences of proteins. Anfinsen’s dogma of molecular biology postulates that the native structure and function of proteins are uniquely determined by its amino acid sequence. [1] As such there is a lot of fundamental interest in analysing the sequences of proteins. Sequence data of protein from multiple species helps in understanding evolutionary patterns and that from a cohort helps with the drug resistance patterns. Multiple Sequence Alignment (MSA) of protein sequences obtained from across species or a cohort is usually the starting point for many such analyses. The simplest analysis one can perform using MSA is evaluating the level of conservation of the individual amino acids. Based on similarity and homology of sequences curated from different species, protein sequences are classified into families which are likely to share structural and functional similarities. The interest in the functional information contained in the sequence analysis is only enhanced by the
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