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ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

ABSTRACT: Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.

SUBMITTER: Krassowski M

PROVIDER: S-EPMC8021862 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

Project description:Most of pyruvoyl-dependent proteins observed in prokaryotes and eukaryotes are critical regulatory enzymes, which are primary targets of inhibitors for anti-cancer and anti-parasitic therapy. These proteins undergo an autocatalytic, intramolecular self-cleavage reaction in which a covalently bound pyruvoyl group is generated on a conserved serine residue. Traditional detections of the modified serine sites are performed by experimental approaches, which are often labor-intensive and time-consuming. In this study, we initiated in an attempt for the computational predictions of such serine sites with Feature Selection based on a Random Forest. Since only a small number of experimentally verified pyruvoyl-modified proteins are collected in the protein database at its current version, we only used a small dataset in this study. After removing proteins with sequence identities >60%, a non-redundant dataset was generated and was used, which contained only 46 proteins, with one pyruvoyl serine site for each protein. Several types of features were considered in our method including PSSM conservation scores, disorders, secondary structures, solvent accessibilities, amino acid factors and amino acid occurrence frequencies. As a result, a pretty good performance was achieved in our dataset. The best 100.00% accuracy and 1.0000 MCC value were obtained from the training dataset, and 93.75% accuracy and 0.8441 MCC value from the testing dataset. The optimal feature set contained 9 features. Analysis of the optimal feature set indicated the important roles of some specific features in determining the pyruvoyl-group-serine sites, which were consistent with several results of earlier experimental studies. These selected features may shed some light on the in-depth understanding of the mechanism of the post-translational self-maturation process, providing guidelines for experimental validation. Future work should be made as more pyruvoyl-modified proteins are found and the method should be evaluated on larger datasets. At last, the predicting software can be downloaded from http://www.nkbiox.com/sub/pyrupred/index.html.

Dataset Information

ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

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