Biology subjects

Simovski, B.

Publications and source records attributed to Simovski, B..

4 recordsLinked to original sources

bioRxiv2024

Deep learning of antibody epitopes using molecular permutation vectors

BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary molecular permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody, rather than the traditional approach of defining epitopes as scores per amino acid on a protein sequence that pertain to their probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary molecular permutation vectors, the approach also uses the 3D macrostructure features of the unbound 3D protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we demonstrate that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model in training and generalizes in very robust manner on dissimilar data not seen by the model in training. ConclusionsWith the approach described herein, a primary protein sequence with the query molecular permutation vector alone is required to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

bioinformatics↗

bioRxiv2020

European Primary Forest Database (EPFD) v2.0

Primary forests are scarce in Europe and continue to disappear at an alarming rate. Despite these losses, we know little about where such forests still occur. Here, we present an updated geodatabase and map of Europes known primary forests. Our geodatabase harmonizes 48 different datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). To assess the robustness of our geodatabase, we checked each patch for forest disturbance events using Landsat satellite-image time series (1985-2018). We estimate that 94% of the patches in our database did not experience significant disturbances that would alter their primary forest status in the last 30 year. Our database is the most comprehensive dataset on primary forests in Europe, and will be useful for biogeographic and ecological studies, and conservation planning to safeguard these unique forests.

ecology↗

bioRxiv2020

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

bioinformatics↗

bioRxiv2018

Mind your gaps: Overlooking assembly gaps confounds statistical testing in genome analysis

BackgroundThe difficulties associated with sequencing and assembling some regions of the DNA sequence result in gaps in the reference genomes that are typically represented as stretches of Ns. Although the presence of assembly gaps causes a slight reduction in the mapping rate in many experimental settings, that does not invalidate the typical statistical testing comparing read count distributions across experimental conditions. However, we hypothesize that not handling assembly gaps in the null model may confound statistical testing of co-localization of genomic features.\n\nResultsFirst, we performed a series of explorative analyses to understand whether and how the public genomic tracks intersect the assembly gaps track (hg19). The findings rightly confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps and the intersection was observed only at the beginning and end regions of the assembly gaps rather than covering the whole gap sizes. Further, we simulated a set of query and reference genomic tracks in a way that nullified any dependence between them to test our hypothesis that not avoiding assembly gaps in the null model would result in spurious inflation of statistical significance. We then contrasted the distributions of test statistics and p-values of Monte Carlo simulation-based permutation tests that either avoided or not avoided assembly gaps in the null model when testing for significant co-localization between a pair of query and reference tracks. We observed that the statistical tests that did not account for the assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribu tion of p-values that is shifted to the left (leading to inflated significance).\n\nConclusionOur results shows that not accounting for assembly gaps in statistical testing of co-localization analysis may lead to false positives and over-optimistic findings.

bioinformatics↗