Biology subjects

Umerenkov, D.

Publications and source records attributed to Umerenkov, D..

3 recordsLinked to original sources

bioRxiv2023

Z-Flipon Variants reveal the many roles of Z-DNA and Z-RNA in health and disease

Identifying roles for Z-flipons remains challenging given their dynamic nature. Here we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA sequences. We show Z-flipons are enriched in promoters and telomeres and overlap quantitative trait loci for RNA expression, RNA editing, splicing and disease associated variants. Surprisingly, many effects are mediated through Z-RNA formation. We describe Z-RNA motifs present in SCARF2, SMAD1 and CACNA1 transcripts and others in non-coding RNAs. We also provide evidence for another Z-RNA motif that likely enables an adaptive anti-viral intracellular defense through alternative splicing of KRAB domain zinc finger proteins. An analysis of OMIM and gnomAD predicted loss-of-function datasets reveals an overlap of predicted and experimentally validated Z-flipons with disease causing variants in 8.6% and 2.9% of mendelian disease genes respectively, with frameshift variants present in 22% of cases. The work greatly extends the number of phenotypes mapped to Z-flipon variants.

genomics↗

bioRxiv2022

PROSTATA: Protein Stability Assessment using Transformers

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning. In this work, we introduce PROSTATA, a predictive model built in knowledge transfer fashion on a new curated dataset. PROSTATA demonstrates superiority over existing solutions based on neural networks. We show that the large margin of improvement is due to both the architecture of the model and the quality of the new training data set. This work opens up opportunities for developing new lightweight and accurate models for protein stability assessment. PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA.

bioinformatics↗

bioRxiv2022

SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1b, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the RBD domain of SARS-CoV-2. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

bioinformatics↗