Biology subjects

Choudhury, S.

Publications and source records attributed to Choudhury, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A new set of DNA methylation variants in the human genome show predominant tissue specificity and sensitivity to reprogramming with a potential for disease susceptibility.

Analyses of 3,370 normal human tissues of ectodermal, endodermal and mesodermal origins identified 12,587 regions averaging [~]585 bp with significant differences in DNA methylation levels within identical tissues. These methylation variants (MeVars) occurred in 8,037 genes enriched in neurological disorders and cancers of which, majority were tissue-specific rather than being systemic. This somatic variation was reduced by reprogramming in vitro into iPSCs and in vivo during spermatogenesis. Analysis of prefrontal cortices showed a higher incidence of MeVars in the candidate genes in controls than schizophrenia patients wherein a subset showed significantly altered transcript levels. Similar effects were observed for oral tissues and skin fibroblast cells. MeVars showed significant association with SINE1, simple and low complexity repeats, H3K27me3, H3k9me3 and H3K4me1 modifications and EZH2, SUZ12 and REST binding sites. Collectively, MeVars have postzygotic origins with an ability to reset during reprogramming, adding a new dimension in the form of epigenetic diversity and its relevance to disease susceptibility in humans.

bioinformatics↗

bioRxiv2026

TKI-Tolerant Persisters Emerge from a PKCα-Dependent and Highly Plastic Subpopulation of Stem-Like Cells in NSCLC

Reversible drug-tolerant persister states are emerging as key drivers of limited therapeutic durability, offering a complementary non-genetic perspective distinct from traditional models of acquired resistance. This is of particular interest in lung adenocarcinoma where EGFR tyrosine kinase inhibitors (TKIs) elicit dramatic responses, yet residual surviving cells persist and ultimately seed relapse. To define mechanisms that enable survival during this earliest residual-disease phase, we focused on the drug-tolerant persister population that remains after EGFR TKI exposure and can later give rise to outgrowth. Initial observations of elevated transcript levels of PRKCA, which encodes PKC, in established TKI-resistant models, together with markedly delayed tumor relapse following PKC suppression in vivo, nominated PKC as a candidate regulator of the persister-to-relapse transition. Genetic ablation of PRKCA or its inhibition with enzastaurin reduced residual survival and outgrowth after TKI exposure, indicating that PKC functions as an early dependency of drug-tolerant persisters rather than as a general mediator of acquired resistance. Mechanistically, PKC was required for persister-associated EMT, migratory capacity, and robust induction of ALDH1A1, the latter constraining oxidative stress and enhancing persister survival. Functionally, PKC was specifically necessary for survival of a rare, pre-existing CD44High stem-like subpopulation that exhibited marked plasticity and ultimately seeded persistence. Together, these data identify a PKC-dependent EMT/stemness/ROS pathway as a critical survival program in EGFR TKI-tolerant persister cells and support therapeutic strategies aimed at eliminating residual disease to prolong clinical responses.

cancer biology↗

bioRxiv2026

PathwaySeeker: Evidence-Grounded AI Reasoning over Organism-Specific Metabolic Networks

Metabolic activity is not an intrinsic property of an organism, but an emergent state shaped by environmental and experimental context. Despite recent advances in large language models (LLMs) and multi-omics profiling, current computational frameworks struggle to represent and reason over metabolism in a condition-specific manner. General-purpose AI systems operate on static, public biochemical knowledge, while multi-omics datasets capture dynamic measurements without a structured framework for mechanistic interpretation. As a result, metabolic networks remains analysis remains disconnected from the experimental states that define biological function. Here, we introduce PathwaySeeker, an evidence-grounded AI system for organism-specific metabolic network reasoning. PathwaySeeker reconstructs sample-specific metabolic graphs from integrated proteomic and metabolomic data, fine-tunes an LLM on the resulting graph structure, and verifies each reasoning step against the experimental graph through iterative hypothesis search, an approach we term Oracle-in-the-Loop inference. Every output claim carries explicit evidence provenance, distinguishing experimentally confirmed relationships from biochemically plausible hypotheses requiring validation. We demonstrate the system using multi-omics data from the non-model white-rot fungus Trametes versicolor, where PathwaySeeker recovers branched phenylpropanoid pathways and transparently stratifies confirmed reactions from testable extensions. Post-hoc thermodynamic analysis condition-specific metabolite dynamics support the biological feasibility of the reconstructed routes. By embedding experimental evidence provenance directly into language model-guided metabolic network reasoning, PathwaySeeker enables systematic differentiation between experimentally grounded knowledge and structured hypothesis, bridging frontier AI capabilities with organism-specific experimental evidence. Availability and ImplementationPathwaySeeker is available at https://github.com/pnnl/PathwaySeeker

bioinformatics↗

bioRxiv2025

AntiFP2: Genome and Metagenome-Wide Prediction of Antifungal Proteins

The identification of antifungal proteins (AFPs) is crucial for understanding microbial interactions and facilitating the discovery of antifungals on a large scale from genomic and metagenomic data. Most existing computational tools are developed for predicting antifungal peptides rather than proteins. To address this limitation, we developed AntiFP2. A manually curated dataset of experimentally validated AFPs was used to train and develop prediction models using cross-validation techniques. The ensemble strategy, which combined ESM2, BLAST, and MERCI, achieved the best results across independent validation. Beyond prediction, we implemented a complete pipeline for genome- and metagenome-wide AFP screening. AntiFP2 is freely available as a web server, standalone package, and Docker container, enabling scalable and reproducible antifungal protein discovery.

bioinformatics↗

bioRxiv2025

Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes

Detecting somatic mutations in normal tissues is challenging due to sequencing errors and the low allele fractions of post-zygotic variants. Duplex sequencing greatly reduces errors and can detect mutations at any allele fraction, but systematic, cross-platform comparisons are lacking. We present a comprehensive benchmarking of six duplex sequencing technologies used by the SMaHT Network: CODEC, CompDuplex-seq, HiDEF-seq, NanoSeq, ppmSeq, and VISTA-seq. We evaluated their performance using cord blood DNA, a tumor-normal cell line mixture, and homogenates from six human tissues. Each method shows distinct profiles in genomic footprint, sensitivity, and cost. Despite differences in library construction and sequencing platforms, estimates of mutation rates and mutational signatures are highly concordant. Integration with ultra-deep whole-genome sequencing shows that duplex approaches sensitively capture mutations and signatures beyond embryonic or clonally expanded variants. These results provide a foundation for selecting duplex methods and interpreting their data, enabling scalable single-molecule analyses of somatic mutation landscapes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/692823v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@123b221org.highwire.dtl.DTLVardef@83ba0aorg.highwire.dtl.DTLVardef@2b0ab0org.highwire.dtl.DTLVardef@1cae35e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

Interactions of long non-coding RNAs (lncRNAs) with proteins is responsible for numerous cellular processes, including transcriptional regulation, chromatin remodeling, cell differentiation, and intracellular signaling. In the past, numerous computational methods have been developed for predicting lncRNA-protein interacting (LPI) pairs. This study describes a highly accurate and reliable method for predicting LPI pairs built on largest possible non-redundant dataset having 262,244 interacting and equal number of non-interacting pairs. Initially, similarity-based approach BLAST has been tried which have poor discriminative power, due to low sequence similarity. Subsequently, we developed CNN based models and machine learning based models using traditional features and embedding. Our CatBoost model developed using embedding generated by DNABERT-2 and ESM-2-t30 achieved AUC of 0.989 with MCC 0.915 on an independent dataset. Our method performs better than existing methods on an independent dataset. We developed standalone software and web server lncrnaPI for predicting LPI pairs, scanning lncRNA interacting proteins in proteome and protein interacting lncRNA in genomes (https://webs.iiitd.edu.in/raghava/lncrnapi/). HIGHLIGHTSO_LIDiscrimination of LncRNA-protein interacting and non-interacting pairs. C_LIO_LINon-redundant dataset of 262,244 interacting and 262,244 non-interacting pairs. C_LIO_LIEmbedding of LncRNA using DNABERT-2 and protein using ESM-2-t30. C_LIO_LIRapid scanning of lncRNA interacting proteins at genome scale. C_LIO_LIA web server and software for predicting LncRNA-protein interacting pairs. C_LI

bioinformatics↗

bioRxiv2025

Spatially Resolved Microglial Expression Around Aβ Plaques in Human Alzheimers Disease Tissue

Using microglia-enriched spatial transcriptomics on human Alzheimers disease tissue, we identify distinct gene expression changes across microglia located in direct contact with plaques, in periplaque regions, and in areas distant from plaques. We define a group of plaque contact-only microglial (PCOM) genes whose expression increases exclusively in microglia directly contacting plaques. These genes show significant overlap with previously reported gene sets, suggesting that many of the well-characterised disease-associated microglia (DAM) and other AD-related gene-expression signatures are only upregulated when microglia contact plaques. We further identify distinct co-expression networks associated with disease-relevant covariates, including an immune module linked to APOE genotype and a synaptic-mitochondrial module negatively associated with Braak stage. Finally, we compare the human dataset to our previously published data from 18-month-old AppNL-F mice, generated using the same experimental paradigm and demonstrate cross-species concordance in gene expression particularly within plaque-contacting microglia.

neuroscience↗

bioRxiv2025

Resolving the molecular niche of pulmonary fibrosis using cryopreserved human precision cut lung slices

Background and methodsPulmonary fibrosis (PF) manifests locally and heterogeneously in the diseased lung. To uncover mechanistic underpinnings, we have utilized cryopreserved fibrotic human precision-cut lung slices (PCLS), and using single-cell spatial transcriptomics, mapped and interpreted the transcriptomic regulation of effector cell types/states. ResultsCryopreserved-thawed fibrotic human PCLS retained canonical molecular and cellular PF hallmarks including fibroblast and myofibroblast accumulation, epithelial depletion, and vascular remodeling. Notably, these cellular changes were spatially co-incident with 1) collagen-rich regions enriched for extracellular matrix (ECM)-related genes (e.g., COL1A1, SPARC), 2) expanded fibroblast-endothelial crosstalk with enhanced chemokine signaling (CXCL12- CXCR4), 3) integrin-driven mesenchymal signaling targets (ANGPTL-integrin), 4) stress-adapted and immune-interacting Alveolar Type 2 cells with impaired surfactant gene expression, and 5) endothelial reprogramming toward a contractile and profibrotic phenotype. At the transcriptomic level, the spatially-defined fibrotic niche was characterized by increased gene expression related to immune responses (IGKC and IGHG1), fibrosis (collagen, fibronectin, and DCN), mechanotransduction (MYH11, TPM2, ACTG2, ACTA2), immune cell recruitment (CCL2, IL6, ICAM1), ECM remodeling (e.g. thrombospondin 1, THBS1), and decreased gene expression related to surfactant production (SFTPB, SFTPC). At the functional level, the fibrotic niches were defined by the activation of three fibroblast mediating signaling pathways: (1) the matricellular glycoprotein, SPARC, (2) Relaxin signaling, and (3) multiple immunoglobulin transcripts, including IGHA1, IGHG1, IGHG3, and IGKC. ConclusionsWe verify model fidelity, identify both known and unanticipated pro-fibrotic genes, and advocate for the expanded use of spatial transcriptomic measurements using cryopreserved human PCLS for disease modeling and drug discovery.

genomics↗

bioRxiv2025

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

Generative machine learning methods that utilize neural networks to parameterize large-scale and near-genome-scale kinetic models have yielded significant efficiency gains in model construction, paving the way for high-throughput dynamic metabolism studies in biomedical and biotechnological applications. Nevertheless, challenges remain in interpreting the outputs of generative neural networks and developing strategies to quickly adapt these networks to different organisms and physiological contexts without having to restart the modeling process from scratch. Here, we present a systematic framework for repurposing generative neural networks trained under one physiological context to build large-scale kinetic models tailored to another. We showcase the effectiveness of this framework through three case studies in Escherichia coli: (i) adjusting the speed of the dynamic response of aerobic metabolism, (ii) improving interpretability by identifying key enzymatic steps that limit the dynamic response speed of the metabolic models, and (iii) adapting a trained generator to capture the distinct dynamic behavior of anaerobic metabolism. To assess robustness and generalizability beyond E. coli, we extend our approach to large-scale kinetic models of Saccharomyces cerevisiae, systematically exploring latent-space-driven control of network dynamics across generators at different training stages and across multiple representative regions of the latent input space. Together, these results demonstrate that latent space exploration provides a transferable and computationally efficient strategy for controlling the dynamic behavior in large-scale kinetic models across species and physiological regimes. Given the growing adoption of generative neural networks in biological systems modeling, our approach has the potential to facilitate applications in personalized medicine and accelerate the high-throughput design of cell factories by streamlining model construction across diverse living organisms.

systems biology↗

bioRxiv2025

A large language model for predicting pancreatic ductal adenocarcinoma patients from blood-derived exosomal transcriptomics data

Traditional machine learning approaches for text or sequence classification rely on converting textual data into numerical representations. In this study, we investigate a reverse strategy in which numerical features are transformed into sequence representations and classified using large language models (LLMs). We applied this methodology to predict pancreatic ductal adenocarcinoma (PDAC) using the expression profiles of 50 genes from 284 PDAC and 217 non-PDAC patients. Gene expression values were converted into sequence data, with each gene represented as a residue in a 50-residue protein sequence. Major LLMs like PeptideBERT, ProtBERT, and ESM2 were fine-tuned on a protein training dataset and evaluated on an independent dataset. The best-performing model, ProtBERT, achieved an AUC of 0.962 on an independent dataset. Additionally, an alignment-based approach employing BLAST and MERCI motifs was explored, and an ensemble model combining the LLM-based and alignment-based methods was developed. Our LLM-based model outperformed traditional machine learning models. To the best of our knowledge, this is the first study demonstrating the application of LLMs for mining transcriptomic profiles of cancer patients. HIGHLIGHTSO_LIIdentification of over and under-expressed genes in PDAC patients C_LIO_LIConvert numeric gene expression data to peptide sequence C_LIO_LILLM based models for predicting PDAC patients using peptide sequences C_LIO_LIMining of transcriptomics data using ProtBert and ESM2 C_LIO_LIGene expression profile for diagnostic of PDAC patients C_LI

bioinformatics↗

bioRxiv2025

NTxPred2: A large language model for predicting neurotoxic peptides and neurotoxins

Most of the existing methods including our NTxPred, use a single model to predict both neurotoxic peptides and neurotoxic proteins (neurotoxins). In this study, we developed distinct models for predicting neurotoxic peptides and neurotoxins. Our peptide dataset consists of 877 neurotoxic and 877 non-neurotoxic peptides, while our protein dataset includes 775 neurotoxins and 775 non-neurotoxins. Preliminary analysis reveals that certain residues, such as cysteine, are more prevalent in both neurotoxic peptides and proteins, though their abundance differs in magnitude. First, we developed machine learning models using composition and binary profiles, achieving a maximum AUC of 0.97 for peptides and 0.85 for proteins. The performance for proteins improved from an AUC of 0.85 to 0.89 when evolutionary information was incorporated. Next, we built machine learning models using embeddings from protein language models, attaining an AUC of 0.96 for peptides and 0.94 for proteins. We also developed protein language models and achieved an AUC of 0.98 for peptides using esm2-t30 and 0.91 for proteins using esm2-t6. All models were trained and tested using 5-fold cross-validation, and final models were evaluated on an independent dataset not used in training. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets. Our neurotoxicity prediction models will aid in the safety assessment of genetically modified foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2, for predicting and scanning neurotoxins (https://webs.iiitd.edu.in/raghava/ntxpred2/, https://github.com/raghavagps/ntxpred2/). HighlightsO_LIDevelopment of models for predicting neurotoxic peptides and neurotoxins C_LIO_LIML Models Using LLM Embeddings for Neurotoxin Prediction C_LIO_LIProtein language models esm2-t30 for predicting neurotoxic peptides C_LIO_LIImportant for risk assessment of genetically modified foods/proteins C_LIO_LINTxPred2 is an updated version of NTxPred for predicting neurotoxicity C_LI

bioinformatics↗

bioRxiv2024

CytoLNCpred - A computational method for predicting cytoplasm associated long-coding RNAs in 15 cell-lines

The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. Previous methods have used noisy datasets, including mRNA transcripts in tools intended for lncRNAs, and excluded lncRNAs lacking significant differential localization between the cytoplasm and nucleus. In order to overcome these shortcomings, a method has been developed for predicting cytoplasm-associated lncRNAs in 15 human cell-lines, identifying which lncRNAs are more abundant in the cytoplasm compared to the nucleus. All models in this study were trained using five-fold cross validation and tested on an independent dataset. Initially, we developed machine and deep learning based models using traditional features like composition and correlation. Using composition and correlation based features, machine learning algorithms achieved an average AUC of 0.7049 and 0.7089, respectively for 15 cell-lines. Secondly, we developed machine based models developed using embedding features obtained from the large language model DNABERT-2. The average AUC for all the cell-lines achieved by this approach was 0.6604. Subsequently, we also fine-tuned DNABERT-2 on our training dataset and evaluated the fine-tuned DNABERT-2 model on the independent dataset. The fine-tuned DNABERT-2 model achieved an average AUC of 0.6336. Correlation-based features combined with ML algorithms outperform LLM-based models, in the case of predicting differential lncRNA localization. These cell-line specific models as well as web-based service are available to the public from our web server (https://webs.iiitd.edu.in/raghava/cytolncpred/) . HIGHLIGHTSO_LIPrediction of cytoplasm-associated lncRNAs in 15 human cell lines C_LIO_LIMachine learning using composition and correlation features C_LIO_LIDNABERT-2 embeddings for lncRNA localization prediction C_LIO_LICorrelation-based models outperform LLM-based models C_LIO_LIWeb server and models available for public use C_LI AUTHORS BIOGRAPHYO_LIShubham Choudhury is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LINaman Kumar Mehta is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LI

bioinformatics↗

bioRxiv2024

Prediction of Adeno-Associated Virus Fitness with a Protein Language Based Machine Learning Model

Adeno-associated viral (AAV)-based therapeutics have the potential to transform the lives of patients by delivering one-time treatments for a variety of diseases. However, a critical challenge to their widespread adoption and distribution is the high cost of goods (CoGS). Reducing manufacturing costs by developing AAV capsids with improved yield, or fitness, is key to making gene therapies more affordable. AAV fitness is largely determined by the amino acid sequence of the capsid, however, engineered AAVs are rarely optimized for manufacturability. Here, we report a state-of-the art machine learning (ML) model that predicts the fitness of AAV capsid mutants based on the amino acid sequence of the capsid monomer. By combining a protein language model (PLM) and classical ML techniques, our model achieved a significantly high prediction accuracy (Pearson correlation = 0.818) for capsid fitness. Importantly, tests on completely independent datasets showed robustness and generalizability of our model, even for multi-mutant AAV capsids. Our accurate ML-based model can be used as a surrogate for laborious in vitro experiments, thus saving time and resources, and can be deployed to increase fitness of clinical AAV capsids to make gene therapies economically viable for patients.

bioinformatics↗

bioRxiv2024

Prediction of Hemolytic Peptides and their Hemolytic Concentration (HC50)

Several peptide-based drugs fail in clinical trials due to their toxicity or hemolytic activity against red blood cells (RBCs). Existing methods predict hemolytic peptides but not the concentration (HC50) required to lyse 50% of RBCs. In this study, we developed a classification model and regression model to identify and quantify the hemolytic activity of peptides. Our models were trained and validated on 1924 peptides with experimentally determined HC50 against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Our classification models achieved a maximum AUC of 0.909 using a hybrid model of ESM-2 and a motif-based approach. Regression models using compositional features achieved R of 0.739 with R{superscript 2} of 0.543. Our models outperform existing methods and are implemented in the web-based platform HemoPI2 and standalone software for designing hemolytic peptides with desired HC50 values (http://webs.iiitd.edu.in/raghava/hemopi2/). HighlightsO_LIDeveloped classification and regression models to predict hemolytic activity and HC50 values of peptides. C_LIO_LIA hybrid model combining machine learning and motif prediction excels in accuracy. C_LIO_LIBenchmarking of the existing classification methods on independent datasets. C_LIO_LIWeb server, standalone software, and pip package for hemolytic activity prediction of peptides/proteins. C_LI

bioinformatics↗

bioRxiv2024

Prediction of plant resistance proteins using alignment-based and alignment-free approaches

Plant Disease Resistance (PDR) proteins are critical in identifying and killing plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as BLAST for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning-based methods using compositional features of proteins. Our machine learning-based model, developed using compositional features of proteins, achieved a maximum performance AUROC of 0.92. The performance of our model improved significantly from AUROC of 0.92 to 0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best machine learning model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, "PlantDRPpred" (https://webs.iiitd.edu.in/raghava/plantdrppred). HighlightsO_LIDevelopment of a Machine-learning model for resistance protein prediction. C_LIO_LIUsed alignment-based and alignment-free ensemble methods. C_LIO_LIWeb server development and standalone package. C_LIO_LIPrediction and design of PDR proteins. C_LI

bioinformatics↗

bioRxiv2024

Prediction of anti-freezing proteins from their evolutionary profile

1.Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates proposed and existing methods on an independent dataset containing 81 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition-based protein features and achieved a peak AUC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST-based similarity and motif-based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine-learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user-friendly web server with a standalone package named "AFPropred" was developed (https://webs.iiitd.edu.in/raghava/afpropred). HighlightsO_LIPrediction of antifreeze proteins with high precision C_LIO_LIEvaluation of prediction models on an independent dataset C_LIO_LIMachine learning based models using sequence composition C_LIO_LIEvolutionary information based prediction models C_LIO_LIA webserver for predicting, scanning, and designing AFPs. C_LI Authors BiographyO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINisha Bajiya is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2024

Impact of phylogeny on the inference of functional sectors from protein sequence data

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information. Author SummaryProteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences.

bioinformatics↗

bioRxiv2024

Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City

Highly pathogenic avian influenza viruses of the H5N1 clade 2.3.4.4b arrived in North America in the winter of 2021/2022. These viruses have spread across the Americas causing morbidity and mortality in both wild and domestic birds as well as some mammalian species, including cattle. Many surveillance programs in wildlife as well as commercial poultry operations have detected these viruses. Here we conducted surveillance of avian species in the urban environment in New York City. We detected highly pathogenic H5N1 viruses in six samples from four different bird species and performed full genome sequencing. Sequence analysis showed the presence of multiple different genotypes. Our work highlights that the interface between animals and humans that may give rise to zoonotic infections or even pandemics is not limited to rural environments and commercial poultry operations but extends into the heart of our urban centers. ImportanceWhile surveillance for avian influenza viruses is often focused on migratory routes and their associated stop-over locations, or commercial poultry operations, many bird species - including migratory birds - frequent or live in urban green spaces and wetlands. This brings them into contact with a highly dense population of humans and pets providing an extensive urban animal-human interface in which the general public may have little awareness of circulating infectious diseases. This study focuses on virus surveillance at this interface, combined with culturally responsive science education and community outreach.

microbiology↗