Biomedical subjects

Sridhar Govindarajan

Publications and source records attributed to Sridhar Govindarajan.

5 recordsLinked to original sources

PubMed2003

Evolutionary potential of (beta/alpha)8-barrels: functional promiscuity produced by single substitutions in the enolase superfamily.

The members of the mechanistically diverse, (beta/alpha)(8)-barrel fold-containing enolase superfamily evolved from a common progenitor but catalyze different reactions using a conserved partial reaction. The molecular pathway for natural divergent evolution of function in the superfamily is unknown. We have identified single-site mutants of the (beta/alpha)(8)-barrel domains in both the l-Ala-d/l-Glu epimerase from Escherichia coli (AEE) and the muconate lactonizing enzyme II from Pseudomonas sp. P51 (MLE II) that catalyze the o-succinylbenzoate synthase (OSBS) reaction as well as the wild-type reaction. These enzymes are members of the MLE subgroup of the superfamily, share conserved lysines on opposite sides of their active sites, but catalyze acid- and base-mediated reactions with different mechanisms. A comparison of the structures of AEE and the OSBS from E. coli was used to design the D297G mutant of AEE; the E323G mutant of MLE II was isolated from directed evolution experiments. Although neither wild-type enzyme catalyzes the OSBS reaction, both mutants complement an E. coli OSBS auxotroph and have measurable levels of OSBS activity. The analogous mutations in the D297G mutant of AEE and the E323G mutant of MLE II are each located at the end of the eighth beta-strand of the (beta/alpha)(8)-barrel and alter the ability of AEE and MLE II to bind the substrate of the OSBS reaction. The substitutions relax the substrate specificity, thereby allowing catalysis of the mechanistically diverse OSBS reaction with the assistance of the active site lysines. The generation of functionally promiscuous and mechanistically diverse enzymes via single-amino acid substitutions likely mimics the natural divergent evolution of enzymatic activities and also highlights the utility of the (beta/alpha)(8)-barrel as a scaffold for new function.

Amino Acid Sequence↗

PubMed2003

Systematic variation of amino acid substitutions for stringent assessment of pairwise covariation.

During protein evolution, amino acids change due to a combination of functional constraints and genetic drift. Proteins frequently contain pairs of amino acids that appear to change together (covariation). Analysis of covariation from naturally occurring sets of orthologs cannot distinguish between residue pairs retained by functional requirements of the protein and those pairs existing due to changes along a common evolutionary path. Here, we have separated the two types of covariation by independently recombining every naturally occurring amino acid variant within a set of 15 subtilisin orthologs. Our analysis shows that in this family of subtilisin orthologs, almost all possible pairwise combinations of amino acids can coexist. This suggests that amino acid covariation found in the subtilisin orthologs is almost entirely due to common ancestral origin of the changes rather than functional constraints. We conclude that naturally occurring sequence diversity can be used to identify positions that can vary independently without destroying protein function.

Amino Acid Sequence↗

PubMed2003

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design.

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.

Algorithms↗

PubMed2003

Optimizing the search algorithm for protein engineering by directed evolution.

An in silico protein model based on the Kauffman NK-landscape, where N is the number of variable positions in a protein and K is the degree of coupling between variable positions, was used to compare alternative search strategies for directed evolution. A simple genetic algorithm (GA) was used to model the performance of a standard DNA shuffling protocol. The search effectiveness of the GA was compared to that of a statistical approach called the protein sequence activity relationship (ProSAR) algorithm, which consists of two steps: model building and library design. A number of parameters were investigated and found to be important for the comparison, including the value of K, the screening size, the system noise and the number of replicates. The statistical model was found to accurately predict the measured activities for small values of the coupling between amino acids, K <or= 1. The ProSAR strategy was found to perform well for small to moderate values of coupling, 0 <or= K <or= 3, and was found to be robust to noise. Some implications for protein engineering are discussed.

Algorithms↗

PubMed2002

Synthetic shuffling expands functional protein diversity by allowing amino acids to recombine independently.

We describe synthetic shuffling, an evolutionary protein engineering technology in which every amino acid from a set of parents is allowed to recombine independently of every other amino acid. With the use of degenerate oligonucleotides, synthetic shuffling provides a direct route from database sequence information to functional libraries. Physical starting genes are unnecessary, and additional design criteria such as optimal codon usage or known beneficial mutations can also be incorporated. We performed synthetic shuffling of 15 subtilisin genes and obtained active and highly chimeric enzymes with desirable combinations of properties that we did not obtain by other directed-evolution methods.

Amino Acid Sequence↗