Biology subjects

Devkota, K.

Publications and source records attributed to Devkota, K..

13 recordsLinked to original sources

bioRxiv2026

Learning a PRECISE language for small-molecule binding

Virtual screening of billion-scale compound libraries has become feasible through machine learning approaches. In particular, CoNCISE (RECOMB 2025) introduced drug quantization via code-books, achieving highly scalable and accurate binary predictions. However, drug discovery requires understanding not just whether molecules bind, but where they bind and how to target specific sites. Here, we present PRECISE which leverages CoNCISEs quantized small-molecule representations while operating on the targets 3D structure as its input. The key innovation of PRECISE is reimagining drug-target interaction as compatibility between quantized drug embeddings and a latent representation of the targets surface mesh, enriched with electrostatic and geometric features. PRECISE designs a novel surface representation, interpreted through a geometric deep learning architecture, enabling it to identify binding sites more accurately than state-of-the-art methods (DiffDock-L, Chai, and Boltz-2) while the codebook ensures billion-scale screening capability. Our formulation unlocks zero-shot generalization to complex targets such as metalloproteins and multi-chain complexes. To enable efficient integration with downstream docking workflows, we introduce PO_SCPLOWRECISEC_SCPLOW-MCTS, which combines fast PO_SCPLOWRECISEC_SCPLOW-based screening with selective Vina docking through an iterative Monte Carlo Tree Search approach. By providing both mechanistic understanding and massive scalability, PRECISE delivers capabilities that were previously mutually exclusive in virtual screening.

bioinformatics↗

bioRxiv2025

Iterative immunogen optimization to focus elicited responses on a conserved, subdominant influenza hemagglutinin epitope

Designing effective vaccination strategies against genetically diverse viruses, such as HIV or influenza, is hindered by the ability of these pathogens to mutate and readily evade immune responses. While these viruses contain conserved regions that cannot easily mutate without affecting viral fitness, these sites are subdominant, meaning that they are not the primary target of antibodies elicited by natural infection or current vaccines. Here, we integrated recent advances in machine learning methods for protein structure prediction and design to engineer protein immunogens that focus immune responses on a conserved, subdominant HA epitope targeted by broadly cross-reactive influenza antibodies. Iterating between computation-guided optimization and in vivo analyses generated immunogens that faithfully displayed the target epitope and redirected humoral responses toward it upon vaccination. These results provide a blueprint for applying recent protein engineering approaches to immunogen design and may inform the development of broadly protective influenza vaccines.

immunology↗

bioRxiv2025

Learning a CoNCISE language for small-molecule binding

Rapid advances in deep learning have improved in silico methods for drug-target interaction (DTI) prediction. However, current methods do not scale to the massive catalogs that list millions or billions of commercially-available small molecules. Here, we introduce CoNCISE, a method that accelerates drug-target interaction (DTI) prediction by 2-3 orders of magnitude while maintaining high accuracy. CoNCISE uses a novel vector-quantized codebook approach and a residual-learning based training of hierarchical codes. Strikingly, we find that much of binding-specificity information in the small molecule space can be compressed into just 15 bits of information per compound, characterizing all small molecules into 32,768 hierarchically-organized binding categories. Our DTI architecture, which combines these compact ligand representations with fixed-length protein embeddings in a cross-attention framework, achieves state-of-the-art prediction accuracy at unprecedented speed. We demonstrate CoNCISEs practical utility by indexing 6.4 billion ligands in the Enamine dataset, enabling researchers to query vast chemical libraries against a protein target in seconds. A "CoNCISE + docking" pipeline screened Enamine to propose strong binders (predicted KD {approx} 10-20 {micro}M) of three difficult-to-drug targets, each within two hours. CoNCISEs advance could democratize access to largescale computational drug discovery, potentially enabling rapid identification of promising molecules for therapeutic targets and cellular perturbations.

bioinformatics↗

bioRxiv2024

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC

Despite the widespread availability of genome sequencing pipelines, many genes remain part of the genome's "dark matter," where existing inference tools cannot even begin to guess the biological function of their proteins from sequence alone. This challenge is especially pronounced in organisms that are highly evolutionarily distant from well-studied models, where homology-based methods break down. Here, we describe PHILHARMONIC, a computational method that combines deep learning-based de novo protein interaction network inference with robust unsupervised spectral clustering and remote homology to illuminate functional organization in any non-model organism. From only a sequenced proteome, we show PHILHARMONIC generates meaningful functional hypothesis for protein functions, functional communities, and higher-order network structure. We validate its performance using experimental gene expression and pathway data in D. melanogaster, and we demonstrate its broad utility by analyzing temperature sensing and stress response pathways in the reef-building coral P. damicornis and its algal symbiont C. goreaui. PHILHARMONIC provides a general-purpose engine for functional discovery and biological hypothesis generation in non-model organisms, enabling systems-level insights across the full diversity of life.

bioinformatics↗

bioRxiv2024

Allo-Allo: Data-efficient prediction of allosteric sites

Allostery, a fundamental structural mechanism where ligand binding at a protein site affects protein function at another site, plays a crucial role in key drug-target proteins like GPCRs. Unfortunately, existing methods for predicting allosteric sites have limited performance- they are particularly constrained by scarce ground-truth experimental data. We introduce Allo-Allo, a data-efficient, sequence-based method that predicts allosteric sites by leveraging protein language models (PLMs). Honing in on ESM-2 attention heads that capture allosteric residue associations, Allo-Allo achieves a 67% higher AUPRC than state-of-the-art methods. Our innovative, data-efficient pipeline not only outperforms alternate, commonly-used PLM-based prediction architectures but also generalizes well. Notably, mutations in Allo-Allo-predicted sites show significant association with elevated disease risk scores from AlphaMissense, highlighting its translational potential. Beyond Allo-Allos biological and translational applicability, its architecture presents a powerful framework for other data-scarce problems in protein analysis.

bioinformatics↗

bioRxiv2024

Miniaturizing, Modifying, and Augmenting Nature's Proteins with Raygun

Proteins have evolved over billions of years through extensive and coordinated substitutions, insertions and deletions (indels). Computational protein design cannot yet fully mimic natures ability to engineer new proteins from existing templates. Protein language models generate informative per-residue representations, but leveraging them to execute large-scale, function-preserving mutations and indels has remained beyond reach. We introduce Raygun, a generative AI framework that unlocks efficient miniaturization, modification, and augmentation of proteins, using a novel probabilistic encoding of protein sequences constructed from language model embeddings. Emulating evolution, Raygun shrinks proteins by 10-25% (sometimes over 50%) while preserving predicted structural integrity and fidelity, introduces extensive sequence diversity while preserving functional sites, and can expand proteins beyond their natural size. These capabilities unlock new opportunities in gene therapy and biotechnology. In cell-based validation, Raygun successfully miniaturized fluorescent proteins, two of which are smaller than 96% of fluorescent proteins reported in FPbase, as well as TurboID, a synthetic biotin ligase widely adopted for proteomics. It also successfully expanded Epidermal Growth Factor (EGF), a natural binding partner to the EGFR protein, generating EGF variants with higher binding affinity than the wildtype. Rayguns conceptual innovations in template-based protein design reveal that protein function can be encoded in a length-independent space. This fundamental insight bridges protein representation learning with evolutionary biology and could unlock the development of more efficient molecular tools and biological therapeutics.

bioinformatics↗

bioRxiv2024

The distribution of Apis laboriosa revisited: range extensions and biogeographic affinities

Apis laboriosa, the Himalayan giant honeybee, inhabits the foothills of the Himalaya and neighboring mountainous regions. Here we revise its distribution in light of recent reports and discoveries. The range now extends from longitude 105.9{degrees}E in Cao Bang, Vietnam, in the east to 74.4{degrees}E in the Pir Panjal Range of western Himalaya, a linear distance of 3300 km, with the most notable new localities in northeastern Vietnam, central Myanmar, northern Thailand, and AJK-Pakistan. The species generally occurs at lower elevations in the eastern part of its range than in Nepal, northern India, and the border region between India and Pakistan. Most but not all of the new localities are within the range predicted by species distribution modelling. We discuss the new localities that fall outside of the predicted range, the biotic characteristics of the terrestrial ecoregions in which the species occurs, and the remaining regions that may harbor this spectacular honey bee species.

ecology↗

bioRxiv2024

Decline of honeybees and its consequences for beekeepers and crop pollination in western Nepal

O_LIPollination service by insects is globally threatened, yet trends outside of Europe and North America are poorly understood. Therefore, in less-studied regions of the world, beekeeper records can provide valuable insights into changes in pollinator populations. C_LIO_LIWe conducted a questionnaire survey of 116 beekeepers in 10 villages in the Jumla District of western Nepal, where the native honeybee Apis cerana cerana is widely kept as a managed honeybee. We complemented the beekeeper survey with field data on insect-crop visitation, a household income survey, and an interview with a local lead beekeeper. C_LIO_LI76% of beekeepers reported declines in the Apis cerana cerana population. 86% experienced declines in honey yield per hive, and 78% reported declines in the number of beehives per beekeeper. Honey yield per hive fell by 50% between 2012 and 2022, whilst the number of occupied hives decreased by 44%. Beekeepers ranked climate change and declining flower abundance as the most-important drivers of the decline. C_LIO_LIThe drastic bee declines raise concerns for the future food and economic security of this region, where honey sales contribute to 16% of total household farming income. Apis cerana cerana plays a major role in crop pollination and contributes more than 50% of all flower visits to apple, cucumber, and pumpkin. C_LIO_LITo mitigate further declines in this region, we promote the use of pollinator-friendly farming practices including more wild flowers and maintenance of native habitat areas, and well-insulated log or top-bar log hives to buffer bees against extreme temperature fluctuations, which are expected to worsen with climate warming. C_LI

ecology↗

bioRxiv2023

Fast Approximate IsoRank for Scalable Global Alignment of Biological Networks

A well-studied approximate version of the graph matching problem is directly relevant for the study of protein-protein interaction networks. Called by the computational biology community Global Network Alignment, the two networks to be matched are derived from the protein-protein interaction (PPI) networks from organisms of two different species. If these two species evolved recently from a common ancestor, we can view the two PPI networks as a single network that evolved over time. It is the two versions of this network that we want to align using approximate graph matching. The first spectral method for the PPI global alignment problem proposed by the biological community was the IsoRank method of Singh et al. This method for global biological network alignment is still used today. However, with the advent of many more experiments, the size of the networks available to match has grown considerably, making running IsoRank unfeasible on these networks without access to state of the art computational resources. In this paper, we develop a new IsoRank approximation, which exploits the mathematical properties of IsoRanks linear system to solve the problem in quadratic time with respect to the maximum size of the two PPI networks. We further propose a computationally cheaper refinement to this initial approximation so that the updated result is even closer to the original IsoRank formulation. In experiments on synthetic and real PPI networks, we find that the results of our approximate IsoRank are not only nearly as accurate as the original IsoRank results but are also much faster, which makes the global alignment of large-scale biological networks feasible and scalable.

bioinformatics↗

bioRxiv2023

Structure-based discovery of inhibitors of the SARS-CoV-2 Nsp14 N7-methyltransferase

An under-explored target for SARS-CoV-2 is non-structural protein 14 (Nsp14), a crucial enzyme for viral replication that catalyzes the methylation of N7-guanosine of the viral RNA at 5'-end; this enables the virus to evade the host immune response by mimicking the eukaryotic post-transcriptional modification mechanism. We sought new inhibitors of the S-adenosyl methionine (SAM)-dependent methyltransferase (MTase) activity of Nsp14 with three large library docking strategies. First, up to 1.1 billion make-on-demand ("tangible") lead-like molecules were docked against the enzymes SAM site, seeking reversible inhibitors. On de novo synthesis and testing, three inhibitors emerged with IC50 values ranging from 6 to 43 M, each with novel chemotypes. Structure-guided optimization and in vitro characterization supported their non-covalent mechanism. In a second strategy, docking a library of 16 million tangible fragments revealed nine new inhibitors with IC50 values ranging from 12 to 341 M and ligand efficiencies from 0.29 to 0.42. In a third strategy, a newly created library of 25 million tangible, virtual electrophiles were docked to covalently modify Cys387 in the SAM binding site. Seven inhibitors emerged with IC50 values ranging from 3.2 to 39 M, the most potent being a reversible aldehyde. Initial optimization of a second series yielded a 7 M acrylamide inhibitor. Three inhibitors characteristic of the new series were tested for selectivity against 30 human protein and RNA MTases, with one showing partial selectivity and one showing high selectivity. Overall, 32 inhibitors encompassing eleven chemotypes had IC50 values <50 M and 5 inhibitors in four chemotypes had IC50 values <10 M. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing multiple starting points for optimizing towards antiviral activity.

biophysics↗

bioRxiv2021

Probing the SAM Binding Site of SARS-CoV-2 nsp14 in vitro Using SAM Competitive Inhibitors Guides Developing Selective bi-substrate Inhibitors

The COVID-19 pandemic has clearly brought the healthcare systems world-wide to a breaking point along with devastating socioeconomic consequences. The SARS-CoV-2 virus which causes the disease uses RNA capping to evade the human immune system. Non-structural protein (nsp) 14 is one of the 16 nsps in SARS-CoV-2 and catalyzes the methylation of the viral RNA at N7-guanosine in the cap formation process. To discover small molecule inhibitors of nsp14 methyltransferase (MT) activity, we developed and employed a radiometric MT assay to screen a library of 161 in house synthesized S-adenosylmethionine (SAM) competitive methyltransferase inhibitors and SAM analogs. Among seven identified screening hits, SS148 inhibited nsp14 MT activity with an IC50 value of 70 {+/-} 6 nM and was selective against 20 human protein lysine methyltransferases indicating significant differences in SAM binding sites. Interestingly, DS0464 with IC50 value of 1.1 {+/-} 0.2 M showed a bi-substrate competitive inhibitor mechanism of action. Modeling the binding of this compound to nsp14 suggests that the terminal phenyl group extends into the RNA binding site. DS0464 was also selective against 28 out of 33 RNA, DNA, and protein methyltransferases. The structure-activity relationship provided by these compounds should guide the optimization of selective bi-substrate nsp14 inhibitors and may provide a path towards a novel class of antivirals against COVID-19, and possibly other coronaviruses.

biochemistry↗

bioRxiv2021

A high-throughput radioactivity-based assay for screening SARS-CoV-2 nsp10-nsp16 complex

Frequent outbreaks of novel coronaviruses (CoVs), highlighted by the current SARS-CoV-2 pandemic, necessitate the development of therapeutics that could be easily and effectively administered world-wide. The conserved mRNA-capping process enables CoVs to evade their host immune system and is a target for antiviral development. Nonstructural protein (nsp) 16 in complex with nsp10 catalyzes the final step of coronaviral mRNA-capping through its 2-O-methylation activity. Like other methyltransferases, SARS-CoV-2 nsp10-nsp16 complex is druggable. However, the availability of an optimized assay for high-throughput screening (HTS) is an unmet need. Here, we report the development of a radioactivity-based assay for methyltransferase activity of nsp10-nsp16 complex in a 384-well format, and kinetic characterization, and optimization of the assay for HTS (Z'-factor: 0.83). Considering the high conservation of nsp16 across known CoV species, the potential inhibitors targeting SARS-CoV-2 nsp10-nsp16 complex may also be effective against other emerging pathogenic CoVs.

biochemistry↗

bioRxiv2020

A high throughput RNA displacement assay for screening SARS-CoV-2 nsp10-nsp16 complex towards developing therapeutics for COVID-19

SARS-CoV-2, the coronavirus that causes COVID-19, evades the human immune system by capping its RNA. This process protects the viral RNA and is essential for its replication. Multiple viral proteins are involved in this RNA capping process including the nonstructural protein 16 (nsp16) which is an S-adenosyl-L-methionine (SAM)-dependent 2-O-methyltransferase. Nsp16 is significantly active when in complex with another nonstructural protein, nsp10, which plays a key role in its stability and activity. Here we report the development of a fluorescence polarization (FP)-based RNA displacement assay for nsp10-nsp16 complex in 384-well format with a Z'-Factor of 0.6, suitable for high throughput screening. In this process, we purified the nsp10-nsp16 complex to higher than 95% purity and confirmed its binding to the methyl donor SAM, product of the reaction, SAH, and a common methyltransferase inhibitor, sinefungin using Isothermal Titration Calorimetry (ITC). The assay was further validated by screening a library of 1124 drug-like compounds. This assay provides a cost-effective high throughput method for screening nsp10-nsp16 complex for RNA-competitive inhibitors towards developing COVID-19 therapeutics.

biochemistry↗