Asia & Pacific Bioinformatics Joint Conference 2024
開催地
和文:
沖縄
英文:
Okinawa
アブストラクト
Some functional proteins change their structure to give rise to a binding site only when a binding molecule approaches them. Such binding sites are called cryptic sites and important targets to expand the scope of drug discovery. However, it’s still difficult to predict cryptic sites correctly. Therefore, we propose a method to correctly detect cryptic sites using topological data analysis and mixed-solvent molecular dynamics (MSMD) simulation.
To detect hotspots, we employed MSMD simulations using six probes with various chemical properties (Benzene, Isopropanol, Phenol, Imidazole, Acetonitrile, and Ethylene glycol) Then, the possibility of cryptic site was then ranked using our topological data analysis method, the Dynamical Analysis of Interaction and Structural changes (DAIS).
For nine target proteins with cryptic sites, the proposed method significantly outperformed the accuracy of the recent machine learning method, Pocketminer. We can detect six of the nine cryptic sites at hotspot Rank 1.
In our method, the MSMD simulations with six different probes were employed to search for hotspots showing “ligandability” on protein surfaces, and the DAIS was used for ranking to the possibility of cryptic sites based on estimation of “structural changeability” of protein. The synergistic combination enables to predict cryptic sites with highly accuracy.
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