arXiv subjects

Hongyan Du

Publications and source records attributed to Hongyan Du.

3 recordsLinked to original sources

arXiv2024

Drug Resistance Predictions Based on a Directed Flag Transformer

The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed drug PAXLOVID, of which the main ingredient nirmatrelvir inhibits the viral main protease (Mpro). Here, we developed CAPTURE (direCted flAg laPlacian Transformer for drUg Resistance prEdictions) to analyze the effects of Mpro mutations on nirmatrelvir-Mpro binding affinities and identify potential drug-resistant mutations. CAPTURE combines a comprehensive mutation analysis with a resistance prediction module based on DFFormer-seq, which is a novel ensemble model that leverages a new Directed Flag Transformer and sequence embeddings from the protein and small-molecule-large-language models. Our analysis of the evolution of Mpro mutations revealed a progressive increase in mutation frequencies for residues near the binding site between May and December 2022, suggesting that the widespread use of PAXLOVID created a selective pressure that accelerated the evolution of drug-resistant variants. Applied to mutations at the nirmatrelvir-Mpro binding site, CAPTURE identified several potential resistance mutations, including H172Y and F140L, which have been experimentally confirmed, as well as five other mutations that await experimental verification. CAPTURE evaluation in a limited experimental data set on Mpro mutants gives a recall of 57\% and a precision of 71\% for predicting potential drug-resistant mutations. Our work establishes a powerful new framework for predicting drug-resistant mutations and real-time viral surveillance. The insights also guide the rational design of more resilient next-generation therapeutics.

q-bio.QM↗

arXiv2023

Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction (PPI) network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis, and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5, and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

q-bio.BM↗

arXiv2023

Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network

Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods. Deep learning-based lead optimization takes expert knowledge as a starting point, learning from numerous historical cases about how to modify the structure for better drug-forming properties. However, compared with the more established de novo generation schemes, lead optimization is still an area that requires further exploration. Previously developed models are often limited to resolving one (or few) certain subtask(s) of lead optimization, and most of them can only generate the two-dimensional structures of molecules while disregarding the vital protein-ligand interactions based on the three-dimensional binding poses. To address these challenges, we present a novel tool for lead optimization, named Delete (Deep lead optimization enveloped in protein pocket). Our model can handle all subtasks of lead optimization involving fragment growing, linking, and replacement through a unified deleting (masking) strategy, and is aware of the intricate pocket-ligand interactions through the geometric design of networks. Statistical evaluations and case studies conducted on individual subtasks demonstrate that Delete has a significant ability to produce molecules with superior binding affinities to protein targets and reasonable drug-likeness from given fragments or atoms. This feature may assist medicinal chemists in developing not only me-too/me-better products from existing drugs but also hit-to-lead for first-in-class drugs in a highly efficient manner.

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