Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

ARIP: A Tool for Precise Interatomic Contact Area and Volume Calculation in Proteins

The interplay patterns of amino acid residues are pivotal in determining the tertiary structure and flexibility of proteins, which in turn are intricately linked to their functionality and interactions with other molecules. Here, we introduce ARIP, a novel tool designed to identify contact residues within proteins. ARIP employs a modified version of the dr_sasa algorithm and an atomic overlap weighted algorithm to directly calculate the contact area and volume between atoms based on their van der Waals radius. It also allows for the selection of solvent radii, recognizing that not every atom in proteins can interact with water molecules. The solvent parameters were derived from the analysis of approximately 5000 protein and nucleic acid structures with water molecules determined using X-ray crystallography. One advantage of the modified algorithm is its capability to analyze multiple models within a single PDB file, making it suitable for molecular dynamic capture. The contact volume is symmetrically distributed between the interacting atoms, providing more informative results than contact area for the analysis of intra- and intermolecular interactions and the development of scoring functions. Furthermore, ARIP has been applied to four distinct cases: capturing key residue-residue contacts in NMR structures of P4HB, protein-drug binding of CYP17A1, protein-DNA binding of SPI1, and molecular dynamic simulations of BRD4.

Gene expression profile analysis unravelled the systems level association of renal cell carcinoma with diabetic nephropathy and Matrix-metalloproteinase-9 as a potential therapeutic target

Type 2 diabetes (T2D) and cancer share many common risk factors. However, the potential biological link that connects the two at the molecular level is still unclear. The experimental evidence suggests that several genes and their pathways may be involved in developing cancerous conditions associated with diabetes. In this study, we identified the protein-protein interaction (PPI) networks and the hub protein(s) that interlink T2D and cancer using genome-scale differential gene expression profiles. Further, the PPI network of AMP-activated protein kinase (AMPK) in cancer was analyzed to explore novel insights into the molecular association between the two conditions. The densely connected regions were analyzed by constructing the backbone and subnetworks with key nodes and shortest pathways, respectively. The PPI network studies identified Matrix-metalloproteinase-9 (MMP-9) as a hub protein playing a vital role in glomerulonephritis tubular diseases and some genetic kidney diseases. MMP-9 was also associated with different growth factors, like tumor necrosis factor (TNF-α), transforming growth factor 1 (TGF-1), and pathways like chemokine signaling, NOD-like receptor signaling, etc. Further, the molecular docking and molecular dynamic simulation studies supported the druggability of MMP-9, suggesting it as a potential therapeutic target in treating renal cell carcinoma linked with diabetic kidney disease.Communicated by Ramaswamy H. Sarma.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Curcumin inhibits spike protein of new SARS-CoV-2 variant of concern (VOC) Omicron, an in silico study

BACKGROUND

Omicron (B.1.1.529), a variant of SARS-CoV-2 is currently spreading globally as a dominant strain. Due to multiple mutations at its Spike protein, including 15 amino acid substitutions at the receptor binding domain (RBD), Omicron is a variant of concern (VOC) and capable of escaping vaccine generated immunity. So far, no specific treatment regime is suggested for this VOC.

METHODS

The three-dimensional structure of the Spike RBD domain of Omicron variant was constructed by incorporating 15 amino acid substitutions to the Native Spike (S) structure and structural changes were compared that of the Native S. Seven phytochemicals namely Allicin, Capsaicin, Cinnamaldehyde, Curcumin, Gingerol, Piperine, and Zingeberene were docked with Omicron S protein and Omicron S-hACE2 complex. Further, molecular dynamic simulation was performed between Crcumin and Omicron S protein to evaluate the structural stability of the complex in the physiological environment and compared with that of the control drug Chloroquine.

RESULTS

Curcumin, among seven phytochemicals, was found to have the most substantial inhibitory potential with Omicron S protein. Further, it was found that curcumin could disrupt the Omicron S-hACE2 complex. The molecular dynamic simulation demonstrated that Curcumin could form a stable structure with Omicron S in the physiological environment.

CONCLUSION

To conclude, Curcumin can be considered as a potential therapeutic agent against the highly infectious Omicron variant of SARS-CoV-2.

Molecular Characterization of Infectious Bronchitis Virus Strain HH06 Isolated in a Poultry Farm in Northeastern China

Spike (S) glycoprotein is an important virulent factor for coronaviruses (CoVs), and variants of CoVs have been characterized based on S gene analysis. We present phylogenetic relationship of an isolated infectious bronchitis virus (IBV) strain with reference to the available genome and protein sequences based on network, multiple sequence, selection pressure, and evolutionary fingerprinting analysis in People's Republic of China. One hundred and elven strains of CoVs i.e., Alphacoronaviruses (Alpha-CoVs; n = 12), Betacoronaviruses (Beta-CoVs; n = 37), Gammacoronaviruses (Gamma-CoVs; n = 46), and Deltacoronaviruses (Delta-CoVs; n = 16) were selected for this purpose. Phylogenetically, SARS-CoV-2 and SARS-CoVs clustered together with Bat-CoVs and MERS-CoV of Beta-CoVs (C). The IBV HH06 of Avian-CoVs was closely related to Duck-CoV and partridge S14, LDT3 (teal and chicken host). Beluga whale-CoV (SW1) and Bottlenose dolphin-CoVs of mammalian origin branched distantly from other animal origin viruses, however, making group with Avian-CoVs altogether into Gamma-CoVs. The motif analysis indicated well-conserved domains on S protein, which were similar within the same phylogenetic class and but variable at different domains of different origins. Recombination network tree indicated SARS-CoV-2, SARS-CoV, and Bat-CoVs, although branched differently, shared common clades. The MERS-CoVs of camel and human origin spread branched into a different clade, however, was closely associated closely with SARS-CoV-2, SARS-CoV, and Bat-CoVs. Whereas, HCoV-OC43 has human origin and branched together with bovine CoVs with but significant distant from other CoVs like SARS CoV-2 and SARS-CoV of human origin. These findings explain that CoVs' constant genetic recombination and evolutionary process that might maintain them as a potential veterinary and human epidemic threat.

Receptor based virtual screening of potential novel inhibitors of tigar [TP53 (tumour protein 53)-induced glycolysis and apoptosis regulator

TP53 (tumor protein 53)-induced glycolysis and apoptosis regulator (TIGAR) belongs to the phosphatases family of proteins that modulates the level of reactive oxygen species in tumor cells. This protein plays a vital role as a negative regulator of glycolysis, thus lowering ROS levels in the cells, which helps the cancerous cells to resist programmed cell death. Besides, TIGAR also mediates the DNA damage repair in cancer cells by increasing tumor cell survival. In the current study, we have screened natural products that compete with the substrate to bind to the active site of TIGAR. Extra precision and MMGBSA scoring function were used to screen the lead molecules. Five compounds were considered as lead molecules with 2-(2-(3,4-dihydroxy phenyl)-3,5-dihydroxy-8-(4-hydroxyphenyl)-4-oxo-4H-furo[2,3-h]chromen-9-yl) acetic acid(DDFA) as a top lead with a docking score of -9.428, and -53.16 MMGBSA, bind to the positively charged amino acids present in the active site. Further, the molecular dynamics simulation studies indicated the structural stability attained by TIGAR protein upon the binding of DDFA, suggesting it to be a potent inhibitor of TIGAR, and could be employed as an anticancer drug during combinational therapy.