Protein conformational search with geometric projections

Protein structure prediction remains a central challenge in computational structural biology. Even at the coarse-grained level of detail, the protein conformational space is vast, and available energy functions contain many false local minima. In order to effectively characterize this space, a conformational search must sample a geometrically-diverse set of low-energy conformations. Our recently published FeLTr framework achieves this goal by employing a low-dimensional geometric projection layer to bias conformational sampling towards unexplored regions of the search space. In this work we present a new geometric projection layer based on the effective connectivity measure, which encapsulates interatomic distances within a conformation. Extensive analysis indicates that effective connectivity allows equipping the high-dimensional conformational search with an effective projection layer. On several target proteins, this layer improves significantly over our previous work, resulting in sampling of conformations with significantly lower lRMSDs to the known native structure.

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