Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Understanding the prion-like behavior of mutant p53 proteins in triple-negative breast cancer pathogenesis: The current therapeutic strategies and future directions

Breast cancer (BC) is viewed as a significant public health issue and is the primary cause of cancer-related deaths among women worldwide. Triple-negative breast cancer (TNBC) is a particularly aggressive subtype that predominantly affects young premenopausal women. The tumor suppressor p53 playsa vital role in the cellular response to DNA damage, and its loss or mutations are commonly present in many cancers, including BC. Recent evidence suggests that mutant p53 proteins can aggregate and form prion-like structures, which may contribute to the pathogenesis of different types of malignancies, such as BC. This review provides an overview of BC molecular subtypes, the epidemiology of TNBC, and the role of p53 in BC development. We also discuss the potential implications of prion-like aggregation in BC and highlight future research directions. Moreover, a comprehensive analysis of the current therapeutic approaches targeting p53 aggregates in BC treatment is presented. Strategies including small molecules, chaperone inhibitors, immunotherapy, CRISPR-Cas9, and siRNA are discussed, along with their potential benefits and drawbacks. The use of these approaches to inhibit p53 aggregation and degradation represents a promising target for cancer therapy. Future investigations into the efficacy of these approaches against various p53 mutations or binding to non-p53 proteins should be conducted to develop more effective and personalized therapies for BC treatment.

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.

Molecular and immunohistochemical study of APC exon 16 and its possible role in colorectal carcinoma development

Background

Colorectal cancer ranks second as a cause of cancer deaths. Mutations in the adenomatous polyposis coli (APC) gene, especially in exon 16, could contribute to colorectal carcinoma development. This study explored the correlations between APC gene exon 16 variations/expression and colorectal carcinoma progression.

Methods

In a case-control study, blood samples from 150 colorectal carcinoma patients and 50 healthy volunteers were analyzed by PCR and sequencing for APC exon 16 variations. The APC protein expression on tissue samples was evaluated by immunohistochemistry and statistical analyses were used to examine clinicopathological correlations.

Results

The sequencing analysis revealed a mutation in exon 16 of the APC gene (rs459552) in 36 % of colorectal cancer cases while absent in all non-cancer controls. Subgroup analysis by tumor grade showed higher prevalence of mutant allele in Grade II and Grade III cases, with frequencies reaching 60.0 % and 69.2 %, respectively, compared to a substantially lower prevalence of 29.4 % in Grade I patients. Immunohistochemistry showed no significant correlation between this mutation and APC expression. APC positivity proportions were 25.5 % in Grade I tumors (n = 26/102) versus 17.1 % in Grade II (n = 6/35) and 46.2 % in Grade III (n = 6/13), showing a non-significant trend of reduced positivity in higher grade tumors (p>0.05).

Conclusions

The frequency of APC exon 16 mutation (rs459552) rose significantly with increasing tumor grade. Similarly, although not statistically significant, the percentage of APC positive staining increased with poorer tumor differentiation, rather than declining. Therefore, the APC exon 16 mutation and expression analysis provides insights into colorectal cancer progression, with the rs459552 mutation correlating with grade and may promoting aggression.

Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights

Background A group of genes called oncogenes includes the Harvey rat sarcoma virus (hRAS) gene. Along with hRAS, Kirsten rat sarcoma viral oncogene homolog (kRAS) and neuroblastoma RAS viral oncogene homolog (nRAS) genes belong to the Rat sarcoma (Ras) family of oncogenes. These three genes result in Rho guanosine triphosphate hydrolases (GTPases) as their protein product. Instructions for producing the protein hRAS, which is mainly involved in controlling cell division, are provided by the hRAS gene. The hRAS protein transfers signals from outside through a process called signal transduction. Because the hRAS protein is a GTPase, it changes the chemical guanosine-5'-triphosphate (GTP) into guanosine diphosphate (GDP). GTP and GDP molecules operate as switches to turn on and off the hRAS. This study aimed to anticipate the structure and stability of the protein resulting from missense single-nucleotide polymorphisms (SNPs) in the human hRAS genes. Methodology To investigate the possible negative effects associated with these SNPs, bioinformatic analysis is typically essential. The following tools were employed for forecasting harmful SNPs: Scale-Invariant Feature Transform (SIFT), Protein Analysis Through Evolutionary Relationships (PANTHER), non-synonymous SNP by Protein Variation Effect Analyzer (PROVEAN), and non-synonymous SNP by Single Nucleotide Polymorphism Annotation Platform (SNAP). Results The present study identified a total of 11 SNPs using the SIFT approach, which were shown to have functional significance. Only two of these 11 SNPs were determined to be tolerable, whereas nine were shown to be detrimental. Among the 11 SNPs analyzed, seven (Q61H, Q99H, K117R, A121D, A146V, R169W, R169Q) were classified as possibly damaging,and four (G13V, Q22K, Q61K, Q13V) were categorized as probably benign according to the predictions made by PANTHER tools. Therefore, the seven SNPs were identified as high-risk SNPs. Conclusions Given that SNPs have the potential to be candidates for cellular alterations brought on by mutations that are associated with cancer, this study provides vital information about how SNPs might be utilized as a diagnostic marker for cancer.

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

Predicting Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) of HRAS Gene and In Silico Evaluation of Their Structural and Functional Consequences towards Diagnosis and Prognosis of Cancer

The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein–protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein–protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as “pathogenic” or “harmful”. Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein–protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan–Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and ‘all’ available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21–0.5 and 0–0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51–0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\Delta \Delta G$\end{document} predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.

Discovery of Novel Tubulin Polymerization Inhibitors by Utilizing 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Tubulin is a potential therapeutic target for cancer. Compounds inhibit the polymerization of tubulin or promote the polymerization of tubulin to interfere with the mitotic process of cells, resulting in...