Nonsynonymous Single-Nucleotide Variations on Some Posttranslational Modifications of Human Proteins and the Association with Diseases

In Silico Evaluation of Nonsynonymous SNPs in Human ADAM33: The Most Common Form of Genetic Association to Asthma Susceptibility

ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th2 cytokines and growth factors. Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.

New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice.

Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach

Autophagy, an evolutionary conserved multifaceted lysosome-mediated bulk degradation system, plays a vital role in liver pathologies including hepatocellular carcinoma (HCC). Post-translational modifications (PTMs) and genetic variations in autophagy components have emerged as significant determinants of autophagy related proteins. Identification of a comprehensive spectrum of genetic variations and PTMs of autophagy related proteins and their impact at molecular level will greatly expand our understanding of autophagy based regulation. In this study, we attempted to identify high risk missense mutations that are highly damaging to the structure as well as function of autophagy related proteins including LC3A, LC3B, BECN1 and SCD1. Number of putative structural and functional residues, including several sites that undergo PTMs were also identified. In total, 16 high-risk SNPs in LC3A, 18 in LC3B, 40 in BECN1 and 43 in SCD1 were prioritized. Out of these, 2 in LC3A (K49A, K51A), 1 in LC3B (S92C), 6 in BECN1 (S113R, R292C, R292H, Y338C, S346Y, Y352H) and 6 in SCD1 (Y41C, Y55D, R131W, R135Q, R135W, Y151C) coincide with potential PTM sites. Our integrated analysis found LC3B Y113C, BECN1 I403T, SCD1 R126S and SCD1 Y218C as highly deleterious HCC-associated mutations. This study is the first extensive in silico mutational analysis of the LC3A, LC3B, BECN1 and SCD1 proteins. We hope that the observed results will be a valuable resource for in-depth mechanistic insight into future investigations of pathological missense SNPs using an integrated computational platform.

Bioinformatics Analysis of Functional Associations of PTMs

Post-translational modifications (PTMs) are an important source of protein regulation; they fine-tune the function, localization, and interaction with other molecules of the majority of proteins and are partially responsible for their multifunctionality. Usually, proteins have several potential modification sites, and their patterns of occupancy are associated with certain functional states. These patterns imply cross talk among PTMs within and between proteins, the majority of which are still to be discovered. Several methods detect associations between PTMs; these have recently combined into a global resource, the PTMcode database, which contains already known and predicted functional associations between pairs of PTMs from more than 45,000 proteins in 19 eukaryotic species.