A Pilot Study of All-Computational Drug Design Protocol–From Structure Prediction to Interaction Analysis

Abstract

Speeding up the drug discovery process is of great significance. To achieve that, high-efficiency methods should be exploited. The conventional wet-bench methods hardly meet the high-speed demand due to time-consuming experiments. Conversely, in silico approaches are much more efficient for drug discovery and design. However, in silico approaches usually serve as a supportive role in research processes. To fully exert the strength of computational methods, we propose a protocol which integrates various in silico approaches, from de novo protein structure prediction to ligand-protein interaction simulation. As a proof of concept, human SK2/calmodulin complex was used as a target for validation. First, we obtained a predicted structure of SK2/calmodulin and predicted binding sites which were consistent with the literature data. Then we investigated the ligand-protein interaction via virtual mutagenesis, flexible docking, and binding affinity calculation. As a result, the binding energies of mutants have similar trends compared with the EC 50 values (R = 0.6 for NS309 in V481 mutants). The results indicate that our protocol can be applied to the drug design of structure unknown proteins. Our study also demonstrates that the integration of in silico approaches is feasible and it facilitates the acceleration of new drug discovery.

In silico identification of drug candidates against COVID-19

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Computer-Assisted Design of Macrocyclic Chelators for Actinium-225 Radiotherapeutics

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Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

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