Corrigendum: Cerebral Cavernous Malformations: Review of the Genetic and Protein-Protein Interactions Resulting in Disease Pathogenesis

Comprehensive in-silico analysis of deleterious SNPs in APOC2 and APOA5 and their differential expression in cancer and cardiovascular diseases conditions

Genetic variations in APOC2 and APOA5 genes involve activating lipoprotein lipase (LPL), responsible for the hydrolysis of triglycerides (TG) in blood and whose impaired functions affect the TG metabolism and are associated with metabolic diseases. In this study, we investigate the biological significance of genetic variations at the DNA sequence and structural level using various computational tools. Subsequently, 8 (APOC2) and 17 (APOA5) non-synonymous SNPs (nsSNPs) were identified as high-confidence deleterious SNPs based on the effects of the mutations on protein conservation, stability, and solvent accessibility. Furthermore, based on our docking results, the interaction of native and mutant forms of the corresponding proteins with LPL depicts differences in root mean square deviation (RMSD), and binding affinities suggest that these mutations may affect their function. Furthermore, in vivo, and in vitro studies have shown that differential expression of these genes in disease conditions due to the influence of nsSNPs abundance may be associated with promoting the development of cancer and cardiovascular diseases. Preliminary screening using computational methods can be a helpful start in understanding the effects of mutations in APOC2 and APOA5 on lipid metabolism; however, further wet-lab experiments would further strengthen the conclusions drawn from the computational study.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

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Prediction of mutations in transmembrane proteins is of significance for diseases diagnosis.

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Building on the evolutionary information, proposed the Gaussian WAPSSM algorithm.

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Based on WAPSSM and sequence and structure-based features, proposed the cascade XGBoost algorithm.

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Webserver is freely at (http://csbio.njust.edu.cn/bioinf/ffmsresmutp/).

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Implement MutTMPredictor to predict mutations in transmembrane proteins.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew’s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions

Nearly one-third of non-synonymous single-nucleotide polymorphism (nsSNPs) are deleterious to human health, but recognition of the disease-associated mutations remains a significant unsolved problem. We proposed a new algorithm, DAMpred, to identify disease-causing nsSNPs through the coupling of evolutionary profiles with structure predictions of proteins and protein-protein interactions. The pipeline was trained by a novel Bayes-guided artificial neural network algorithm that incorporates posterior probabilities of distinct feature classifiers with the network training process. DAMpred was tested on a large-scale data set involving 10,635 nsSNPs from 2154 ORFs in the human genome and recognized disease-associated nsSNPs with an accuracy 0.80 and a Matthews correlation coefficient of 0.601, which is 9.1% higher than the best of other state-of-the-art methods. In the blind test on the TP53 gene, DAMpred correctly recognized the mutations causative of Li-Fraumeni-like syndrome with a Matthews correlation coefficient that is 27% higher than the control methods. The study demonstrates an efficient avenue to quantitatively model the association of nsSNPs with human diseases from low-resolution protein structure prediction, which should find important usefulness in diagnosis and treatment of genetic diseases.