Biology subjects

Stebliankin, V.

Publications and source records attributed to Stebliankin, V..

9 recordsLinked to original sources

bioRxiv2023

A Comprehensive Survey of Scoring Functions for Protein Docking Models

The protein-protein docking problem is fundamental to our understanding of how proteins interact. It is also important because of its relevance to drug discovery and vaccine design. Docking methods typically involve two steps. The first step generates many possible candidate conformations, while the second step evaluates the candidates using some scoring function. Despite numerous experiments with many innovative scoring functions, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches for scoring protein-protein complexes, evaluating their strengths and weaknesses to aid researchers in understanding progress made in this field. Our survey compares the state-of-the-art in classical and deep learning-based scoring methods.

molecular biology↗

bioRxiv2023

PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks

The computational studies of protein binding are widely used to investigate fundamental biological processes and facilitate the development of modern drugs, vaccines, and therapeutics. Scoring functions aim to predict complexes that would be formed by the binding of two biomolecules and to assess and rank the strength of the binding at the interface. Despite past efforts, the accurate prediction and scoring of protein binding interfaces remain a challenge. The physics-based methods are computationally intensive and often have to trade accuracy for computational cost. The possible limitations of current machine learning (ML) methods are ineffective data representation, network architectures, and limited training data. Here, we propose a novel approach called PIsToN (evaluating Protein binding Interfaces with Transformer Networks) that aim to distinguish native-like protein complexes from decoys. Each protein interface is transformed into a collection of 2D images (interface maps), where each image corresponds to a geometric or biochemical property in which pixel intensity represents the feature values. Such a data representation provides atomic-level resolution of relevant protein characteristics. To build hybrid machine learning models, additional empirical-based energy terms are computed and provided as inputs to the neural network. The model is trained on thousands of native and computationally-predicted protein complexes that contain challenging examples. The multi-attention transformer network is also endowed with explainability by highlighting the specific features and binding sites that were the most important for the classification decision. The developed PIsToN model significantly outperforms existing state-of-the-art scoring functions on well-known datasets.

bioinformatics↗

bioRxiv2022

EMoMiS: A Pipeline for Epitope-based Molecular Mimicry Search in Protein Structures with Applications to SARS-CoV-2

MotivationEpitope-based molecular mimicry occurs when an antibody cross-reacts with two different antigens due to structural and chemical similarities. Molecular mimicry between proteins from two viruses can lead to beneficial cross-protection when the antibodies produced by exposure to one also react with the other. On the other hand, mimicry between a protein from a pathogen and a human protein can lead to auto-immune disorders if the antibodies resulting from exposure to the virus end up interacting with host proteins. While cross-protection can suggest the possible reuse of vaccines developed for other pathogens, cross-reaction with host proteins may explain side effects. There are no computational tools available to date for a large-scale search of antibody cross-reactivity. ResultsWe present a comprehensive Epitope-based Molecular Mimicry Search (EMoMiS) pipeline for computational molecular mimicry searches. EMoMiS, when applied to the SARS-CoV-2 Spike protein, identified eight examples of molecular mimicry with viral and human proteins. These findings provide possible explanations for (a) differential severity of COVID-19 caused by cross-protection due to prior vaccinations and/or exposure to other viruses, and (b) commonly seen COVID-19 side effects such as thrombocytopenia and thrombophilia. Our findings are supported by previously reported research but need validation with laboratory experiments. The developed pipeline is generic and can be applied to find mimicry for novel pathogens. It has applications in improving vaccine design. AvailabilityThe developed Epitope-based Molecular Mimicry Search Pipeline (EMoMiS) is available from https://biorg.cs.fiu.edu/emomis/. Contactgiri@cs.fiu.edu

bioinformatics↗

bioRxiv2021

Prior exposure to microcystin alters host gut resistome and is associated with dysregulated immune homeostasis in translatable mouse models

The increased propensity of harmful algal blooms (HABs) and exposure from HABs-cyanotoxin causes human toxicity. It has been associated with the progression of several diseases that encompass the liver, kidneys, and immune system. Recently, a strong association of cyano-HAB toxicity with the altered host gut microbiome has been shown. We tested the hypothesis that prior exposure to cyanotoxin microcystin may alter the microbiome and induce microbiome-host-resistome crosstalk. Using both wild-type and humanized mice, we show that the mice exposed to microcystin had an altered microbiome signature that harbored antimicrobial resistance genes. Host resistome phenotypes such as mefA, msrD, mel, ant6, and tet40 increased in diversity and relative abundance following microcystin exposure. Interestingly, the increased abundance of these genes was traced to resistance to common antibiotics such as tetracycline, macrolides, glycopeptide, and aminoglycosides, crucial for modern-day treatment for several diseases. Increased abundance of these genes was positively associated with increased expression of PD1, a T-cell homeostasis marker, and pleiotropic inflammatory cytokine IL-6 with a concomitant negative association with immunosurveillance markers IL7 and TLR2. Microcystin exposure also caused decreased TLR2, TLR4, and REG3G expressions, increased immunosenescence, and higher systemic levels of IL-6 in both wild-type and humanized mice. In conclusion, the results show a first-ever characterization of the host resistome of microcystin exposure and its connection to host immune status and antibiotic resistance. The results may be crucial for understanding the ability of exposed subjects to fight future bacterial infections and the progression of the debilitating disease in hospital settings.

microbiology↗

bioRxiv2021

Molecular mimicry between Spike and human thrombopoietin may induce thrombocytopenia in COVID-19

SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. We computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins such as PRKG1 and tropomyosin. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions.

bioinformatics↗

bioRxiv2021

Mutation-induced Changes in the Receptor-binding Interface of the SARS-CoV-2 Delta Variant B.1.617.2 and Implications for Immune Evasion

While the vaccination efforts against SARS-CoV-2 infections are ongoing worldwide, new genetic variants of the virus are emerging and spreading. Following the initial surges of the Alpha (B.1.1.7) and the Beta (B.1.351) variants, a more infectious Delta variant (B.1.617.2) is now surging, further deepening the health crises caused by the pandemic. The sharp rise in cases attributed to the Delta variant has made it especially disturbing and is a variant of concern. Fortunately, current vaccines offer protection against known variants of concern, including the Delta variant. However, the Delta variant has exhibited some ability to dodge the immune system as it is found that neutralizing antibodies from prior infections or vaccines are less receptive to binding with the Delta spike protein. Here, we investigated the structural changes caused by the mutations in the Delta variants receptor-binding interface and explored the effects on binding with the ACE2 receptor as well as with neutralizing antibodies. We find that the receptor-binding {beta}-loop-{beta} motif adopts an altered but stable conformation causing separation in some of the antibody binding epitopes. Our study shows reduced binding of neutralizing antibodies and provides a possible mechanism for the immune evasion exhibited by the Delta variant.

biophysics↗

bioRxiv2021

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

MotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. ResultsWe created JO_SCPLOWASPERC_SCPLOW, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a "Microbiome Map", where each position in the image represents the abundance of a single taxon from a reference collection. JO_SCPLOWASPERC_SCPLOW can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use JO_SCPLOWASPERC_SCPLOW to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes. AvailabilityJO_SCPLOWASPERC_SCPLOW is freely available at microbiomemaps.org and via biorg.cs.fiu.edu/jasper Contactcvaldes2@unl.edu; giri@fiu.edu Supplementary informationSupplementary materials are available at microbiomemaps.org

bioinformatics↗

bioRxiv2020

Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

MotivationMetagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as {beta}-lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. ResultsWe introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PO_SCPLOWEC_SCPLOWTRO_SCPLOWIC_SCPLOW, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to {beta}-lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome. AvailabilityThe pipeline for calculation of bacterial replication rates using the high performance computing framework Slurm is available from https://biorg.cs.fiu.edu/petri/.

bioinformatics↗

bioRxiv2020

Causal Inference in Microbiomes Using Intervention Calculus

Inferring causal effects is critically important in biomedical research as it allows us to move from the typical paradigm of associational studies to causal inference, and can impact treatments and therapeutics. Association patterns can be coincidental and may lead to wrong inferences in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in health and diseases. Hence knowledge of genuine causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and interactions are essential for understanding disease mechanisms and making treatment recommendations. In this paper, we investigate fundamental causal inference techniques to measure the causal effects of various entities in a microbiome. In particular, we show how to use these techniques on microbiome datasets to study the rise and impact of antibiotic-resistance in microbiomes. Our main contributions include the following. We introduce a novel pipeline for microbiome studies, new ideas for experimental design under weaker assumptions, and data augmentation by context embedding. Our pipeline is robust, different from traditional approaches, and able to predict interventional effects without any controlled experiments. Our work shows the advantages of causal inference in identifying potential pathogenic, beneficial, and antibiotic-resistant bacteria. We validate our results using results that were previously published.

bioinformatics↗