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Computational enzyme design has made great strides over the last five years. Traditional methods of enzyme design require synthesis and evaluation of many mutations. Computational enzyme design has emerged as a powerful tool to predict how specific mutations modify a protein’s activity, stability, and/or selectivity. Such computational approaches can evaluate many mutations and reduce the load of in vitro work by identifying mutations likely to accomplish design objectives. Computational approaches can explore mutational spaces inaccessible in traditional mutagenesis. Computational methods reduce cost and time compared with experimental approaches. We review the efficacy and key differences of computational enzyme design methods as published in recent studies. The included articles used computational methods to design enzymes, were published no earlier than 2015, met design objectives, and verified results in vitro.

"Protein Side-chain Packing" has an ever-increasing application in the field of bio-informatics, dating from the early methods of homology modeling to protein design and to the protein docking. However, this problem is computationally known to be NP-hard.

In this regard, we have developed a novel approach to solve this problem using the notion of a maximum edge-weight clique. Our approach is based on efficient reduction of protein side-chain packing problem to a graph and then solving the reduced graph to find the maximum clique by applying an efficient clique finding algorithm developed by our co-authors. Since our approach is based on deterministic algorithms in contrast to the various existing algorithms based on heuristic approaches, our algorithm guarantees of finding an optimal solution.

We have tested this approach to predict the side-chain conformations of a set of proteins and have compared the results with other existing methods. We have found that our results are favorably comparable or better than the results produced by the existing methods. As our test set contains a protein of 494 residues, we have obtained considerable improvement in terms of size of the proteins and in terms of the efficiency and the accuracy of prediction.

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand’s binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

Gene targeting to selected chromosomal loci is greatly stimulated when free DNA ends are created that initiate double-strand break repair. Gene therapy reagents can be developed by engineering DNA endonucleases that cleave genomes at desired target sequences. Homing endonucleases are naturally occurring rare-cutting enzymes that have well understood DNA binding and DNA cleavage properties. Rational design methods as well as directed evolution strategies that involve genetic selections and screens using combinatorial libraries generate homing endonucleases with altered sequence specificities. Molecular switches are being introduced into these enzymes to regulate their activity. This article reviews the progress that has been made in constructing homing endonucleases for gene therapy and genome engineering, and discusses the challenges that remain.

The inverse protein folding problem is that of designing an amino acid sequence which has a prescribed native protein fold. This problem arises in drug design where a particular structure is necessary to ensure proper protein-protein interactions. The input to the inverse protein folding problem is a shape and the goal is to design a protein sequence with a unique native fold that closely approximates the input shape. Gupta et al.¹ introduced a design in the 2D HP model of Dill that can be used to approximate any given (2D) shape. They conjectured that the protein sequences of their design are stable but only proved the stability for an infinite class of very basic structures. The HP model divides amino acids to two groups: hydrophobic (H) and polar (P), and considers only hydrophobic interactions between neighboring H amino in the energy formula. Another significant force acting during the protein folding are sulfide (SS) bridges between two cysteine amino acids. In this paper, we will enrich the HP model by adding cysteines as the third group of amino acids. A cysteine monomer acts as an H amino acid, but in addition two neighboring cysteines can form a bridge to further reduce the energy of the fold. We call our model the HPC model. We consider a subclass of linear structures designed in Gupta et al.¹ which is rich enough to approximate (although more coarsely) any given structure. We refine the structures for the HPC model by setting approximately a half of H amino acids to cysteine ones. We conjecture that these structures are stable under the HPC model and prove it under an additional assumption that non-cysteine amino acids act as cysteine ones, i.e., they tend to form their own bridges to reduce the energy. In the proof we will make an efficient use of a computational tool 2DHPSolver which significantly speeds up the progress in the technical part of the proof. This is a preliminary work, and we believe that the same techniques can be used to prove this result without the artificial assumption about non-cysteine H monomers.