title: Insights into the sequence parameters for halophilic adaptation.
abstract: The sequence parameters for halophilic adaptation are still not fully understood. To understand the molecular basis of protein hypersaline adaptation, a detailed analysis is carried out, and investigated the likely association of protein sequence attributes to halophilic adaptation. A two-stage strategy is implemented, where in the first stage a supervised machine learning classifier is build, giving an overall accuracy of 86 % on stratified tenfold cross validation and 90 % on blind testing set, which are better than the previously reported results. The second stage consists of statistical analysis of sequence features and possible extraction of halophilic molecular signatures. The results of this study showed that, halophilic proteins are characterized by lower average charge, lower K content, and lower S content. A statistically significant preference/avoidance list of sequence parameters is also reported giving insights into the molecular basis of halophilic adaptation. D, Q, E, H, P, T, V are significantly preferred while N, C, I, K, M, F, S are significantly avoided. Among amino acid physicochemical groups, small, polar, charged, acidic and hydrophilic groups are preferred over other groups. The halophilic proteins also showed a preference for higher average flexibility, higher average polarity and avoidance for higher average positive charge, average bulkiness and average hydrophobicity. Some interesting trends observed in dipeptide counts are also reported. Further a systematic statistical comparison is undertaken for gaining insights into the sequence feature distribution in different residue structural states. The current analysis may facilitate the understanding of the mechanism of halophilic adaptation clearer, which can be further used for rational design of halophilic proteins.
authors.affiliation: MMV, Banaras Hindu University, Varanasi, 221005, India. abhigyannath01@gmail.com.
authors.forename: Abhigyan
authors.initials: A
authors.lastname: Nath
mesh_terms: D000222:Adaptation, Physiological; D000595:Amino Acid Sequence; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D007700:Kinetics; D000069550:Machine Learning; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012965:Sodium Chloride / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins; D012965:Sodium Chloride
keywords: Amino acid preference/avoidance; Halophilic adaptation; Molecular signatures; Physicochemical properties; Protein surface