Biology subjects

Mukhyala, K.

Publications and source records attributed to Mukhyala, K..

4 recordsLinked to original sources

bioRxiv2026

Slivka and Slivka-bio: a lightweight framework for presenting executables as web services and its application in bioinformatics.

MotivationExecution of code is critical for computational biology, but technical requirements can prevent others from running it. Public web-apps and services thus remain the most effective way to make code accessible, but no fully reusable infrastructure exists to help researchers do this. ResultsWe developed Slivka to enable easy provision of robust HTTP-based execution services backed by local or distributed hardware; accessible via curl and dedicated clients. We demonstrate it with Slivka-bio, which provides semantically annotated services for Jalview 2.12 (https://www.jalview.org/development/jalview_develop/) and includes 15+ tools for protein and RNA analysis. Slivka has been in production in academic and industry environments for 5 years and ran more than 1.5M jobs. Availability and ImplementationSlivka and Slivka-bio are released under the Apache 2.0 License. Slivka-bio public instance at https://www.compbio.dundee.ac.uk/slivka with links to documentation, docker containers, and github repositories for Slivka-bio and Slivka. Contactj.procter@dundee.ac.uk and g.j.barton@dundee.ac.uk.

bioinformatics↗

bioRxiv2026

GYDE: A collaborative drug discovery platform for AI-powered protein design and engineering

As computational tools and machine learning models for protein sciences continue to advance and proliferate, bench scientists face increasing technical challenges adopting these tools for specific applications such as drug discovery. Here we present GYDE (Guide Your Design and Engineering), an open-source, versatile, and web-based collaboration platform designed to make computational analyses of proteins and antibodies easily accessible to bench scientists. GYDE enables the exploration of sequence-structure-function relationships through a tightly integrated visual interface, offering researchers a comprehensive exploration of protein functional determinants either via real assay data or computational tools. GYDEs intuitive interface facilitates seamless access to cutting-edge AI models for protein and antibody structure prediction, design, and downstream analyses. The flexible and easy addition of new tools and models is facilitated by the use of the Slivka compute API. The platform supports saved sessions that enable researchers to easily share their findings with other users, fostering a more collaborative scientific community. GYDE is freely available for protein scientists in academia and industry to build drug discovery analytics platforms customized to their needs.

bioinformatics↗

bioRxiv2025

Predicting specificity of TCR-pMHC interactions using machine learning and biophysical models

Understanding the mechanism of T-cell activation and T-cell receptor (TCR) discrimination of MHC-presented epitope peptides (pMHCs) remains an open problem. Machine learning (ML)-based prediction of TCR specificity has gained considerable recent attention. However, the capacity of current models to generalize to peptides unseen during training is currently unknown. Here, we use a proprietary cancer-patient data set that profiles TCR binding to novel regions of peptide space to show that peptide generalization remains an unsolved problem. Specifically, we show that while ML methods have demonstrable utility in predicting TCR specificity for known peptides, they fail to generalize to novel peptides. We also show that physics-based methods utilizing classical energy functions outperform ML methods when predicting TCR binding to novel peptides but underperform them on known peptides. In light of these observations, we develop a new ML model that leverages general knowledge acquired by protein foundation models to achieve better or comparable performance than either ML or biophysical methods on both in- and out-of-distribution TCR-pMHC specificity prediction. We furthermore analyze model performance as a function of distance of TCR sequence specificity between the training and test sets to quantitatively characterize the generalization potential of any given TCR-pMHC model. Our analysis sheds light on the status of modeling TCR-pMHC interactions and suggests new paths forward for continued method development and data acquisition.

immunology↗

bioRxiv2020

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of Natural Killer (NK) cells and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for 4-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

bioinformatics↗