PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory

A comparative study on structural proteins of viruses that belong to the identical family

Recent studies have focused on the similarity between SARS Cov-2 and various viruses from the Coronaviridae family (such as MERS Cov,  SARS Cov and Bat Cov RaTG13) to uncover the mystery of SARS Cov-2. Specifically, some studies identified that the SARS Cov-2 is closely related to Bat Cov RaTG13 (a SARS-related coronavirus found in bats) rather than the other viruses in that family. These studies are mainly focusing on the biological techniques to show the similarity between the SARS Cov-2 and other viruses. Examining proteins is not easy for common researchers unless for biologists. To rectify this flaw, we have to convert the protein to one of the known formats, which are easy to understand. Consequently, this study uses viral structural proteins to analyse the relationship between SARS Cov-2 and the rest of the coronavirus with the help of mathematical and statistical parameters and explores the various graph representations of MERS Cov,  SARS Cov,  Bat Cov RaTG13 and SARS Cov-2 structural proteins, such as zig-zag curve, Protein Contact Map ( PCM ) and Chaos Game Representation ( CGR ). Though these graph interpretations are visually similar, a slight variation between the graphs reflects their structural and functional differences. Thus, we use an elegant parameter known as the fractal dimension to observe their minor changes. According to the nature of the graph, we employ different types of fractal dimensions, namely mass dimension and box dimension. Furthermore, we perform the similarity tests with normalized cross-correlation and cosine similarity to assess the comparability of the PCM and CGR graphs. The acquired C C n values are near the sequence identity between SARS Cov-2 and MERS Cov,  SARS Cov,  Bat Cov RaTG13.

Role of a high centrality residue in protein dynamics and thermal stability

Centralities determined from Residue Interaction Networks (RIN) in proteins have been used to predict aspects of their structure and dynamics. Here, we correlate the Eigenvector Centrality (Ec) with the rate constant for thermal denaturation (kden) of the HisF protein from Thermotoga maritima based on 12 single alanine substitution mutants. The molecular basis for this correlation was further explored by studying a mutant containing a replacement of a high Ec residue, Y182A, which displayed increased kden at 80 °C. The crystallographic structure of this mutant showed few changes, mostly in two flexible loops. The 1H-15N -HSQC showed only subtle changes of cross peak positions for residues located near the mutation site and scattered throughout the structure. However, the comparison of the RIN showed that Y182 is the vertex of a set of high centrality residues that spreads throughout the HisF structure, which is lacking in the mutant. Cross-correlation displacements of Cα calculated from a molecular dynamics simulation at different temperatures showed that the Y182A mutation reduced the correlated movements in the HisF structure above 70 °C. 1H-15N NMR chemical shift covariance using temperature as perturbation were consistent with these results. In conclusion the increase in temperature drives the structure of the mutant HisF-Y182A into a less connected state, richer in non-concerted motions, located predominantly in the C-terminal half of the protein where Y182 is placed. Conversely, wild-type HisF responds to increased temperature as a single unit. Hence the replacement of a high Ec residue alters the distribution of thermal energy through HisF structure.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.