Biology subjects

Guan, J. S.

Publications and source records attributed to Guan, J. S..

4 recordsLinked to original sources

bioRxiv2026

CrossAffinity: A Sequence-Based Protein-Protein Binding Affinity Prediction Tool Using Cross-Attention Mechanism

Protein-protein binding affinity is important for understanding protein interactions within a protein complex and for identifying strong drug-peptide binders to a target protein. Many structure-based models were built previously with reasonable performance. However, such models require protein complex structure as input, which is usually unavailable due to high cost and experimental constraints. To tackle such an issue, the sequence-based CrossAffinity model was constructed in this study, using the cross-attention module to extract contextual information of interacting protein components while separating the protein complex into two distinct parts to predict the protein-protein binding affinity. CrossAffinity managed to outperform all structure-based models and sequence-based models in an S34 test set containing newer protein complex structures and binding affinity values in a timeline while being trained on an older dataset, showing generalisability to new data points. In other test sets, namely S90, S90 subset and S79*, CrossAffinity also managed to outperform all other sequence-based models while maintaining comparable performance to many recently published structure-based models. The acceptable performance and quick inference of CrossAffinity enable it to be deployed in situations requiring the prediction of the binding affinity of many protein complexes that lack structural information.

bioinformatics↗

bioRxiv2024

Protein language models are performant in structure-free virtual screening

1.1Hitherto virtual screening has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein - a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited virtual screening due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structure.

bioinformatics↗

bioRxiv2022

In silico repurposed drugs against monkeypox virus

Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations a nd variants. Currently the US Food and Drug Administration (FDA) approved poxvirus treatment involves use of tecovirimat. However, there is limited pharmacopoeia otherwise, and limited research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication.

bioinformatics↗

bioRxiv2021

Bacteria.guru: comparative transcriptomics and co-expression database for bacterial pathogens

SummaryThe bacterial kingdom comprises unicellular prokaryotes able to establish symbioses from mutualism to parasitism. To combat bacterial pathogenicity, we need an enhanced understanding of gene function and regulation, which will mediate the development of novel antimicrobials. Gene expression can predict gene function, but there lacks a database enabling expansive inter- and intraspecific exploration of gene expression profiles and co-expression networks for bacteria. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and studied bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, co-expression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid the discovery of multi-faceted antibiotic targets. Hence, we believe bacteria.guru will facilitate future bacterial research. AvailabilityThe database and co-expression networks are freely available from https://bacteria.guru/. The sample annotations are found in the supplemental data.

bioinformatics↗