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Protein-ligand binding is a fundamental biological process that is paramountto many other biological processes, such as signal transduction, metabolicpathways, enzyme construction, cell secretion, gene expression, etc. Accurateprediction of protein-ligand binding affinities is vital to rational drugdesign and the understanding of protein-ligand binding and binding inducedfunction. Existing binding affinity prediction methods are inundated withgeometric detail and involve excessively high dimensions, which underminestheir predictive power for massive binding data. Topology provides an ultimatelevel of abstraction and thus incurs too much reduction in geometricinformation. Persistent homology embeds geometric information into topologicalinvariants and bridges the gap between complex geometry and abstract topology.However, it over simplifies biological information. This work introduceselement specific persistent homology (ESPH) to retain crucial biologicalinformation during topological simplification. The combination of ESPH andmachine learning gives rise to one of the most efficient and powerful tools forrevealing protein-ligand binding mechanism and for predicting bindingaffinities.

Topological fingerprints reveal protein-ligand binding mechanism