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Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma (KRAS) harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the KRAS 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of KRAS mutations.

Pyrazinamidase (PZase) is a member of Fe-dependent amidohydrolases that activates pyrazinamide (PZA) into active pyrazinoic acid (POA). PZA, a nicotinamide analogue, is an essential first-line drug...

G-protein-coupled receptor 142 (GPR142) belongs to rhodopsin family. GPR142 and GPR119, both Gq-coupled receptors, are expressed in pancreatic β cells of pancreas; their activation eventually leads to triggering of insulin secretion. In this paper, through a systems and synthetic biology approach, the effect of a common hit compound has been investigated in GPR142 and GPR119 pathways. This hit that has the potential to be developed as a lead for nanodrug was obtained through high-throughput virtual screening. The hit compound was further docked with nanoparticles (GOLD, SPION, and CeO2). The probable effect of this potential hit on insulin secretion in type 2 diabetes and its dynamic behavior was explored. Kinetic simulation was performed for cross-validation of its role in both the pathways. This study opens up a probable avenue in therapy of type 2 diabetes through regulation of GPR142 and GPR119 receptors. The biological circuit constructed may further have an application as a modulator to control the up- and downregulation of the biochemical pathway and can be implemented as sensors or nanochips for therapy.

Incidents of breast cancer (BC) are on the rise on a daily basis and have proven to be the most prevelant cause of death for women in both developed and developing countries. Among total BC cases diagnosed after menopause, 70% of cases are Estrogen Receptor (ER) positive (ER-positive or ER+). Mutations in the LBD (ligand-binding domain) of the ER have recently been reported to be the major cause of resistance to potent antagonists. In this study, the experimentally reported mutations K303R, E380Q, V392I, S463P, V524E, P535H, P536H, Y537C, Y537N, Y537S, and D538G were analyzed, and the most significant mutations were shortlisted based on multiple analyses. Initial analyses, such as mCSM stability, occluded depth analysis, mCSM-binding affinity, and FoldX energy changes shortlisted only six mutations as being highly resistant. Finally, simulations of force field-based molecular dynamics (MD on wild type (WT) ERα) on six mERα variants (E380Q, S463P, Y537S, Y537C, Y537N, and D538G) were carried out to justify mechanism of the resistance. It was observed that these mutations increased the flexibility of the H12. A bonding analysis suggested that previously reported important residue His524 lost bonding upon mutation. Other parameters, such as PCA (principal component analysis), DCCM (dynamics cross-correlation), and FEL (free energy landscape), verified that the shortlisted mutations affect the H12 helix, which opens up the co-activator binding conformation. These results provide deep insight into the mechanism of relative resistance posed to fulvestrant due to mutations in breast cancer. This study will facilitate further understanding of the important aspects of designing specific and more effective drugs.

Helicobacter pylori (H. pylori) is one of the most extensively studied Gram-negative bacteria due to its implication in gastric cancer. The oncogenicity of H. pylori is associated with cytotoxin-associated gene A (CagA), which is injected into epithelial cells lining the stomach. Both the C- and N-termini of CagA are involved in the interaction with several host proteins, thereby disrupting vital cellular functions, such as cell adhesion, cell cycle, intracellular signal transduction, and cytoskeletal structure. The N-terminus of CagA interacts with the tumor-suppressing protein, apoptosis-stimulating protein of p53 (ASPP2), subsequently disrupting the apoptotic function of tumor suppressor gene p53. Here, we present the in-depth molecular dynamic mechanism of the CagA–ASPP2 interaction and highlight hot-spot residues through in silico mutagenesis. Our findings are in agreement with previous studies and further suggest other residues that are crucial for the CagA–ASPP2 interaction. Furthermore, the ASPP2-binding pocket possesses potential druggability and could be engaged by decoy peptides, identified through a machine-learning system and suggested in this study. The binding affinities of these peptides with CagA were monitored through extensive computational procedures and reported herein. While CagA is crucial for the oncogenicity of H. pylori, our designed peptides possess the potential to inhibit CagA and restore the tumor suppressor function of ASPP2.

A drug may be metabolized by multiple cytochrome P450 (CYP450) isoforms. Predicting the metabolic fate of drugs is very important to prevent drug-drug interactions in the development of novel pharmaceuticals. Prediction of CYP450 enzyme-substrate selectivity is formulized as a multilabel learning task in this study. First, we compared the performance of feature combinations based on four different categories of features, which are physiochemical property descriptors, mol2vec descriptors, extended connectivity fingerprints, and molecular access system key fingerprints on modeling. After identifying the best combination of features, we applied seven different multilabel models, which are multilabel

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

Half of the world population is infected by the Gram-negative bacterium Helicobacter pylori (H. pylori). It colonizes in the stomach and is associated with severe gastric pathologies including gastric cancer and peptic ulceration. The most virulent factor of H. pylori is the cytotoxin-associated gene A (CagA) that is injected into the host cell. CagA interacts with several host proteins and alters their function, thereby causing several diseases. The most well-known target of CagA is the tumor suppressor protein ASPP2. The subdomain I at the N-terminus of CagA interacts with the proline-rich motif of ASPP2. Here, in this study, we carried out alanine scanning mutagenesis and an extensive molecular dynamics simulation summing up to 3.8 μs to find out hot spot residues and discovered some new protein-protein interaction (PPI)-modulating molecules. Our findings are in line with previous biochemical studies and further suggested new residues that are crucial for binding. The alanine scanning showed that mutation of Y207 and T211 residues to alanine decreased the binding affinity. Likewise, dynamics simulation and molecular mechanics with generalized Born surface area (MMGBSA) analysis also showed the importance of these two residues at the interface. A four-feature pharmacophore model was developed based on these two residues, and top 10 molecules were filtered from ZINC, NCI, and ChEMBL databases. The good binding affinity of the CHEMBL17319 and CHEMBL1183979 molecules shows the reliability of our adopted protocol for binding hot spot residues. We believe that our study provides a new insight for using CagA as the therapeutic target for gastric cancer treatment and provides a platform for a future experimental study.

The DAP12-NKG2C activating immunoreceptor complex is one of the multisubunit transmembrane protein complexes in which ligand-binding receptor chains assemble with dimeric signal-transducing modules through non-covalent associations in their transmembrane (TM) domains. In this work, both coarse grained and atomistic molecular dynamic simulation methods were applied to investigate the self-assembly dynamics of the transmembrane domains of the DAP12-NKG2C activating immunoreceptor complex. Through simulating the dynamics of DAP12-NKG2C TM heterotrimer and point mutations, we demonstrated that a five-polar-residue motif including: 2 Asps and 2 Thrs in DAP12 dimer, as well as 1 Lys in NKG2C TM plays an important role in the assembly structure of the DAP12-NKG2C TM heterotrimer. Furthermore, we provided clear evidences to exclude the possibility that another NKG2C could stably associate with the DAP12-NKG2C heterotrimer. Based on the simulation results, we proposed a revised model for the self-assembly of DAP12-NKG2C activating immunoreceptor complex, along with a plausible explanation for the association of only one NKG2C with a DAP12 dimer.