Journal Article

FZJ-2021-00969

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

Manelfi, C. ; Gossen, J.FZJ* ; Gervasoni, S. ; Talarico, C. ; Albani, S.FZJ* ; Philipp, B. J. ; Musiani, F. ; Vistoli, G. ; Rossetti, G.FZJ* ; Beccari, A. R. ; Pedretti, A. (Corresponding author)

2021
MDPI70206 Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/27140  doi:10.3390/molecules26040797

Abstract: The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.

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Contributing Institute(s):

1. Computational Biomedicine (INM-9)
2. Jülich Supercomputing Center (JSC)
3. Computational Biomedicine (IAS-5)

Research Program(s):

1. 525 - Decoding Brain Organization and Dysfunction (POF4-525) (POF4-525)
2. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2021

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