Superfamily of alpha-beta hydrolases is one of the largest groups of structurally related enzymes with diverse catalytic functions. Bioinformatic analysis was used to study how lipase and amidase catalytic activities are implemented into the same structural framework. Subfamily-specific positions--conserved within lipases and peptidases but different between them--that were supposed to be responsible for functional discrimination have been identified. Mutations at subfamily-specific positions were used to introduce amidase activity into Candida antarctica lipase B (CALB). Molecular modeling was implemented to evaluate influence of selected residues on binding and catalytic conversion of amide substrate by corresponding library of mutants. In silico screening was applied to select reactive enzyme-substrate complexes that satisfy knowledge-based criteria of amidase catalytic activity. Selected CALB variants with substitutions at subfamily-specific positions Gly39, Thr103, Trp104, and Leu278 were produced and showed significant improvement of experimentally measured amidase activity. Based on these results, we suggest that value of subfamily-specific positions should be further explored in order to develop a systematic tool to study structure-function relationship in enzymes and to use this information for rational enzyme engineering.

Understanding the role of specific amino acid residues in the molecular mechanism of a protein's function is one of the most challenging problems in modern biology. A systematic bioinformatic analysis of protein families and superfamilies can help in the study of structure–function relationships and in the design of improved variants of enzymes/proteins, but represents a methodological challenge. The pyridoxal‐5′‐phosphate ( PLP )‐dependent enzymes are catalytically diverse and include the aspartate aminotransferase superfamily which implements a common structural framework known as type fold I. In this work, the recently developed bioinformatic online methods Mustguseal and Zebra were used to collect and study a large representative set of the aspartate aminotransferase superfamily with high structural, but low sequence similarity to l ‐threonine aldolase from Aeromonas jandaei ( LTA aj), in order to identify conserved positions that provide general properties in the superfamily, and to reveal family‐specific positions (FSPs) responsible for functional diversity. The roles of the identified residues in the catalytic mechanism and reaction specificity of LTA aj were then studied by experimental site‐directed mutagenesis and molecular modelling. It was shown that FSPs determine reaction specificity by coordinating the PLP cofactor in the enzyme's active centre, thus influencing its activation and the tautomeric equilibrium of the intermediates, which can be used as hotspots to modulate the protein's functional properties. Mutagenesis at the selected FSPs in LTA aj led to a reduction in a native catalytic activity and increased the rate of promiscuous reactions. The results provide insight into the structural basis of catalytic promiscuity of the PLP ‐dependent enzymes and demonstrate the potential of bioinformatic analysis in studying structure–function relationship in protein superfamilies.

Protein stability provides advantageous development of novel properties and can be crucial in affording tolerance to mutations that introduce functionally preferential phenotypes. Consequently, understanding the determining factors for protein stability is important for the study of structure-function relationship and design of novel protein functions. Thermal stability has been extensively studied in connection with practical application of biocatalysts. However, little work has been done to explore the mechanism of pH-dependent inactivation. In this study, bioinformatic analysis of the Ntn-hydrolase superfamily was performed to identify functionally important subfamily-specific positions in protein structures. Furthermore, the involvement of these positions in pH-induced inactivation was studied. The conformational mobility of penicillin acylase in Escherichia coli was analyzed through molecular modeling in neutral and alkaline conditions. Two functionally important subfamily-specific residues, Gluβ482 and Aspβ484, were found. Ionization of these residues at alkaline pH promoted the collapse of a buried network of stabilizing interactions that consequently disrupted the functional protein conformation. The subfamily-specific position Aspβ484 was selected as a hotspot for mutation to engineer enzyme variant tolerant to alkaline medium. The corresponding Dβ484N mutant was produced and showed 9-fold increase in stability at alkaline conditions. Bioinformatic analysis of subfamily-specific positions can be further explored to study mechanisms of protein inactivation and to design more stable variants for the engineering of homologous Ntn-hydrolases with improved catalytic properties.

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The ability of proteins and enzymes to maintain a functionally active conformation under adverse environmental conditions is an important feature of biocatalysts, vaccines, and biopharmaceutical proteins. From an evolutionary perspective, robust stability of proteins improves their biological fitness and allows for further optimization. Viewed from an industrial perspective, enzyme stability is crucial for the practical application of enzymes under the required reaction conditions. In this review, we analyze bioinformatic-driven strategies that are used to predict structural changes that can be applied to wild type proteins in order to produce more stable variants. The most commonly employed techniques can be classified into stochastic approaches, empirical or systematic rational design strategies, and design of chimeric proteins. We conclude that bioinformatic analysis can be efficiently used to study large protein superfamilies systematically as well as to predict particular structural changes which increase enzyme stability. Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes. Further development of systematic bioinformatic procedures is needed to organize and analyze sequences and structures of proteins within large superfamilies and to link them to function, as well as to provide knowledge-based predictions for experimental evaluation.