Virtual screening has become central to early-stage drug discovery, and structure-based approaches have recently been reframed as a retrieval problem through contrastive learning methods such as DrugCLIP, which project protein pockets and ligands into a shared embedding space. However, what these abstract representatio...
Javier S. Utgés, David T. Jones, C. Orengo· bioRxiv· 0 citations
This study analyzed how mutations identified in Genome-Wide-Association-Studies affect protein structure and function and identified potential interaction partners for 18 proteins with mutations at predicted interfaces and modeled their complex structures.
Fatima Shahid, Neeladri Sen, C. Orengo et al.· Bioinformatics Advances· 0 citations
ContrasTED uses CATH-supervised center-contrastive learning to project structure-aware embeddings into a domain-level metric space for nearest-centroid superfamily assignment, which separates superfamilies while retaining structural information below 20% sequence identity.
David Miller, Nicola Bordin, Janusan Jeyananthan et al.· bioRxiv· 0 citations
A characterization of a large subset of PAAT enzymes, including their likely oligomeric states, functional site properties, and experimental functions is presented, including their likely oligomeric states, functional site properties, and experimental functions.
J. Roca-Martínez, Hazel N. Leiva Martel, Jialin Yin et al.· Structure· 0 citations
De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3–7. As a result, de novo designed proteins are becoming larger and more complex8–10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved...
K. I. Albanese, Joel J. Chubb, L. Gutierrez-Rus et al.· bioRxiv· 0 citations
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