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Moya-Garcia, A. A.

Publications and source records attributed to Moya-Garcia, A. A..

2 recordsLinked to original sources

Topological attention asymmetry in ESM-2 attention implicitly encodes the allosteric hierarchy of the adenosine A2A receptor

Protein language models (PLMs) learn complex structural and functional dependencies from evolutionary sequence variation alone. While these models lack a temporal axis, it remains an open question whether their static attention maps encode the directional hierarchies characteristic of allosteric communication. We investigate this in the human adenosine A2A receptor using the ESM-2 transformer. We show that attention heads tuned to functional sites exhibit elevated structural asymmetry compared to the background. Using random-site and sequence-shuffle controls, we establish that this asymmetry is not an architectural artefact of the softmax operation, but a learned, sequence-dependent signal. By defining a signed pathway score between the extracellular ligand-binding triad and the intracellular G-protein interface, we identify a robust topological polarity: the model consistently routes information from the dynamic extracellular site to the conserved intracellular interface. This directed bias emerges with network depth and is independent of intrinsic column-sink heads. Rather than simulating a forward-propagating physical wave, the network represents allosteric coupling by utilising the effector site as a structural anchor. We conclude that PLMs implicitly encode the causal hierarchy of allosteric transmission through a query-anchor topology, establishing directed attention as a robust indicator of long-range functional coordination.

bioinformatics↗

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) - structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.\n\nOur approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

bioinformatics↗