Biology subjects

Murali, T. M.

Publications and source records attributed to Murali, T. M..

12 recordsLinked to original sources

bioRxiv2026

Using peptide-exchange systems to interrogate peptide-specific KIR binding to HLA Class -I

The killer-cell immunoglobulin-like receptors (KIR) are a family of activating and inhibitory HLA class I (HLA-I) binding receptors expressed on natural killer (NK) cells and subsets of T cells. The KIR detect HLA-I molecules in a peptide-dependent manner, with some KIR displaying exquisite peptide-specificity. Studying peptide recognition by KIR often uses TAP-deficient cell lines expressing single HLA-I alleles, which are heterogenous and time consuming to generate. Here, we established an alternative approach using peptide-exchange technologies hitherto developed for studying T cell recognition of HLA-I. We tested two methods; dipeptide-mediated peptide exchange and open-HLA-I, HLA-I molecules consisting of heavy chain-{beta}2m disulphide bonded dimers. We combined peptide-exchange technologies with SpyTag-SpyCatcher chemistry to allow rapid detection of KIR binding via HLA-I displayed on plates or cells. We demonstrated the fidelity of this system with peptides of known KIR specificity bound to HLA-C*05:01. We then screened a peptide library to identify novel strong KIR2DS4 binding peptides presented by HLA-C*04:01. Peptide-exchanged HLA-C was functionally competent, promoting activation of KIR2DS4+ NK cells and inhibiting activation of KIR2DL1+ NK cells. Together, we show that peptide-exchangeable HLA-I molecules are ligands for KIR, presenting a flexible, efficient system for examining the peptide-sequence dependent recognition of HLA-I by KIR.

immunology↗

bioRxiv2026

A Cophylogenetic Approach for Virus-Host Interaction Prediction

Advances in metagenomics have rapidly expanded viral discovery, revealing vast diversity across Earths virosphere. Yet most virus-host interactions--i.e., which viruses infect which hosts--remain unrecorded. Identifying these interactions is essential for anticipating zoonotic spillover events and advancing biomedical applications such as bacteriophage therapy. However, the sheer diversity of viruses and hosts makes comprehensive experimental mapping infeasible, motivating the need for computational approaches. Most existing prediction methods rely on supervised learning strategies that use sequence-derived features, such as codon usage bias or k-mer frequencies, and do not model the coevolutionary processes that shape virus-host interactions. This limits their ability to generalize and the evolutionary interpretability of their predictions. We introduce CoEvoLink, a framework for predicting virus-host interactions that integrates sequence-based evidence with phylogenetic signal by explicitly modeling the coevolutionary histories of viruses and hosts. CoEvoLink infers likely but unobserved interactions by minimizing the number of evolutionary events required to explain them, yielding the most parsimonious interaction under a coevolutionary model. This formulation generalizes classical maximum parsimony, typically defined on a single phylogeny, by jointly optimizing parsimony across both virus and host phylogenies. Sequence-based information is incorporated by assigning a cost to each potential interaction that reflects its likelihood based on genomic features. By drawing a connection between computing parsimony on interaction matrices and maximum parsimony on phylogenetic networks, we derive a polynomial-time algorithm that balances parsimony with sequence-derived prediction cost. We demonstrate the effectiveness of CoEvoLink on simulated data under diverse coevolutionary models. Applying CoEvoLink, we identified putative bat hosts of betacoronaviruses that have not yet been cataloged in the VIRION database. On a benchmark derived from metagenomic sequencing data, we demonstrate that CoEvoLink improves the performance of existing phage-host prediction tools using cophylogenetic information. Code availabilityhttps://github.com/sashittal-group/CoEvoLink Note: This paper is accepted at RECOMB 2026 (30th Annual International Conference on Research in Computational Molecular Biology).

evolutionary biology↗

bioRxiv2025

HAVEN: Hierarchical Attention for Viral protEin-based host iNference

It is crucial to accurately predict hosts of viruses to understand and anticipate human infectious diseases that originate from animals. There is a lack of versatile models that handle out-of-distribution factors such as unseen hosts and viruses. We develop a machine learning model for predicting the host infected by a virus, given only the sequence of a protein encoded by the genome of that virus. Our approach, HAVEN, is the first to apply to multiple hosts and to generalize to unseen hosts and viruses. HAVEN is a transformer-based architecture coupled with hierarchical self-attention that can accept sequences of highly diverse lengths. We integrate HAVEN with a prototype-based few-shot learning classifier to predict rare classes. We demonstrate the accuracy, robustness, and generalizability of HAVEN through a comprehensive series of experiments. In particular, we show that HAVEN can achieve a median AUPRC of 0.67 while predicting common hosts. Moreover, HAVEN retains this AUPRC value even for rare hosts (median prevalence as low as 0.09%). Our model performs on par with state-of-the-art foundation models, which are 65 to 5, 000 times larger in size, and outperforms them in identifying hosts of SARS-CoV-2 variants of concern.

bioinformatics↗

bioRxiv2025

SynVerse: A Framework for Systematic Evaluation of Deep Learning Based Drug Synergy Prediction Models

Synergistic drug combinations are often used to treat cancer. Experimental exploration of all possibilities is expensive. Deep learning (DL) for predicting the synergy of drug pairs in specific cell lines might provide an alternative. However, current approaches often suffer from data leakage. They also lack systematic ablation studies. To address these gaps, we propose SynVerse, a comprehensive evaluation framework featuring four data-splitting strategies to assess DL model generalizability and three ablation studies: module-based, feature shuffling, and a novel network-based approach to disentangle factors influencing model performance. We evaluated sixteen models incorporating eight drug- and cell line-specific features, five preprocessing techniques, and two widely used encoders. Our analysis revealed several insights. None of the models outperformed a naive baseline using one-hot encoding as features. Biologically meaningful drug or cell line features and drug-drug interactions were not the drivers of predictive performance. All models demonstrated poor generalization to unseen drugs and cell lines. SynVerse emphasizes the need for substantial improvements before computational predictors can reliably support experimental and clinical settings.

pharmacology and toxicology↗

bioRxiv2024

NEFFy: A Versatile Tool for Computing the Number of Effective Sequences

SummaryA Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The "Number of Effective Sequences" (NEFF) quantifies the diversity of sequences of an MSA. Several tools can compute the NEFF of an MSA, each offering various options. NEFFy is the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to nucleic acids (DNA and RNA). Availability and ImplementationNEFFy is released as open-source software under the GNU General Public License v3.0. The source code in C++ and a Python wrapper are available on GitHub at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy

bioinformatics↗

bioRxiv2024

ICoN: Integration using Co-attention across Biological Networks

MotivationMolecular interaction networks are powerful tools for studying cellular functions. Integrating diverse types of networks enhances performance in downstream tasks such as gene module detection and protein function prediction. The challenge lies in extracting meaningful protein feature representations due to varying levels of sparsity and noise across these heterogeneous networks. ResultsWe propose ICoN, a novel co-attention-based, denoising, unsupervised graph neural network model that takes multiple protein-protein association networks as inputs and generates an integrated single network by computing a unified feature representation for each protein. A key contribution of ICoN is a novel approach that enables cross-network communication through co-attention during training. The model also incorporates a denoising training technique, introducing perturbations to each input network and training the model to reconstruct the original network from its corrupted version, a method previously unexplored in network integration. Our experimental results demonstrate that ICoN surpasses individual networks across three downstream tasks: gene module detection, gene coannotation prediction, and protein function prediction. Compared to existing unsupervised network integration models, ICoN exhibits superior performance across the majority of downstream tasks and exhibits enhanced robustness against noise. This work introduces a promising approach for effectively integrating diverse protein-protein association networks, aiming to achieve a biologically meaningful unified representation of proteins. AvailabilityThe ICoN software is available under the GNU Public License v3 at https://github.com/Murali-group/ICoN.

systems biology↗

bioRxiv2020

Modeling and Analysis of the Macronutrient Signaling Network in Budding Yeast

In eukaryotes, distinct nutrient signals are relayed by specific plasma membrane receptors to signal transduction pathways that are interconnected in complex information-processing networks. The function of these networks is to govern robust cellular responses to unpredictable changes in the nutritional environment of the cell. In the budding yeast, Saccharomyces cerevisiae, these nutrient signaling pathways and their interconnections have been well characterized. However the complexity of the signaling network confounds the interpretation of the overall regulatory logic of the control system. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling network in budding yeast, focusing on early temporal responses to carbon and nitrogen signaling. We build a computational model of this network to reconcile literature-curated quantitative experimental data with our proposed molecular mechanism. We evaluate the robustness of our estimates of the models kinetic parameter values. We test the model by comparing predictions made in mutant strains with qualitative experimental observations made in the same strains. Finally, we use the model to predict nutrient-responsive transcription factor activities in a number of mutant strains undergoing complex nutrient shifts.

systems biology↗

bioRxiv2019

Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.\n\nAuthor SummaryThe process of cell division, also called the cell cycle, is controlled by a highly complex network of interconnected genes. If this process goes awry, diseases such as cancer can result. In order to unravel the complex interactions within the cell cycle control network, computational biologists have developed mathematical models that describe how different cell cycle genes are related. These models are built using large datasets describing the effect of mutating one or more genes within the network. In this manuscript, we present a novel method for producing such datasets. Using our method, we generate 7,350 yeast mutants to explore the interactions between key cell cycle genes. We measure the effect of the mutations by monitoring the growth rate of the yeast mutants under different environmental conditions. We use our mutants to revise an existing model of the yeast cell cycle and present a dataset of [~]44,000 gene by environment combinations as a resource to the yeast genetics and modeling communities.

systems biology↗

bioRxiv2019

Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network

MotivationNearly 40% of the genes in sequenced genomes have no experimentally- or computationally-derived functional annotations. To fill this gap, we seek to develop methods for network-based gene function prediction that can integrate heterogeneous data for multiple species with experimentally-based functional annotations and systematically transfer them to newly-sequenced organisms on a genomewide scale. However, the large size of such networks pose a challenge for the scalability of current methods. ResultsWe develop a label propagation algorithm called FastSinkSource. By formally bounding its the rate of progress, we decrease the running time by a factor of 100 without sacrificing accuracy. We systematically evaluate many approaches to construct multi-species bacterial networks and apply FastSinkSource and other state-of-the-art methods to these networks. We find that the most accurate and efficient approach is to pre-compute annotation scores for species with experimental annotations, and then to transfer them to other organisms. In this manner, FastSinkSource runs in under three minutes for 200 bacterial species. Availability and ImplementationPython implementations of each algorithm and all data used in this research are available at http://bioinformatics.cs.vt.edu/~jeffl/supplements/2020-fastsinksource. Contactmurali@cs.vt.edu Supplementary InformationA supplementary file is available at bioRxiv online.

systems biology↗

bioRxiv2019

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. We develop a systematic framework called BEELINE for this purpose. We use synthetic networks with predictable cellular trajectories as well as curated Boolean models to serve as the ground truth for evaluating the accuracy of GRN inference algorithms. We develop a strategy to simulate single-cell gene expression data from these two types of networks that avoids the pitfalls of previously-used methods. We selected 12 representative GRN inference algorithms. We found that the accuracy of these methods (measured in terms of AUROC and AUPRC) was moderate, by and large, although the methods were better in recovering interactions in the synthetic networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of the algorithms we tested. The predicted networks were considerably inconsistent with each other, indicating that combining GRN inference algorithms using ensembles is likely to be challenging. Based on the results, we present some recommendations to users of GRN inference algorithms, including suggestions on how to create simulated gene expression datasets for testing them. BEELINE, which is available at http://github.com/murali-group/BEELINE under an open-source license, will aid in the future development of GRN inference algorithms for single-cell transcriptomic data.

bioinformatics↗

bioRxiv2019

Connectivity Measures for Signaling Pathway Topologies

Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules \"connected\" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathways downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.\n\nAuthor summarySignaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules \"connected\" in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are \"connected\" in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is \"downstream\" of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.

bioinformatics↗

bioRxiv2018

Efficient Synthesis of Mutants Using Genetic Crosses

The genetic cross is a fundamental, flexible, and widely-used experimental technique to create new mutant strains from existing ones. Surprisingly, the problem of how to efficiently compute a sequence of crosses that can make a desired target mutant from a set of source mutants has received scarce attention. In this paper, we make three contributions to this question.\n\nFirst, we formulate several natural problems related to efficient synthesis of a target mutant from source mutants. Our formulations capture experimentally-useful notions of verifiability (e.g the need to confirm that a mutant contains mutations in the desired genes) and permissibility (e.g., the requirement that no intermediate mutants in the synthesis be inviable).\n\nSecond, we develop combinatorial techniques to solve these problems. We prove that checking the existence of a verifiable, permissible synthesis is NP-complete in general. We complement this result with three polynomial time or fixed-parameter tractable algorithms for optimal synthesis of a target mutant for special cases of the problem that arise in practice.\n\nThird, we apply these algorithms to simulated data and to synthetic data. We use results from simulations of a mathematical model of the cell cycle to replicate realistic experimental scenarios where a biologist may be interested in creating several mutants in order to verify model predictions. Our results show that the consideration of permissible mutants can affect the existence of a synthesis or the number of crosses in an optimal one. Our algorithms gracefully handle the restrictions that permissible mutants impose. Results on synthetic data show that our algorithms scale well with increases in the size of the input and the fixed parameters.

systems biology↗