Challenge #2

Hit Identification
Method type (check all that applies)
High-throughput docking
Machine learning
Physics-based
Other (specify)
Structural systems pharmacology
Description of your approach (min 200 and max 800 words)

We will screen out the hit compounds using a Structural Systems Pharmacology (SSP) scheme (1-2). In this scheme, the core is the function-site interaction fingerprint (Fs-IFP) approach (3). Using the Fs-IFP approach, we explore the structural insights into the binding sites across the whole structural proteome. Additionally, this SSP scheme combines MD simulations, Free energy calculations, and machine learning models. Specifically, we first explore the characteristics of the binding site of SARS-CoV-2 helicase NSP13  based on the whole structural proteome using the Fs-IFP method. Then, upon the insights into the binding site we obtained above, we screen the compound datasets virtually using the off-the-shell, classical high-throughput docking tools, such as Autodock Vina. 3) Using MD simulations, the flexibility of the binding sites will be revealed and the corresponding docking process will be performed to target the different conformations. 4) The compounds with top scores will be further optimized by checking the binding affinity and the binding fingerprints. 
(1) Zheng Zhao, Philip E. Bourne. J. Proteome Res. 2020, 19 (11), 4698–4705
(2) Zheng Zhao, Philip E. Bourne.  J. Chem. Theory Comput. 2020 16 (5), 3152-3161
(3) Zheng Zhao, Li Xie, Lei Xie, Philip E. Bourne. J. Med. Chem. 2016 59 (9), 4326-4341

What makes your approach stand out from the community? (<100 words)

 The Structural Systems Pharmacology (SSP) scheme including a function-site interaction fingerprint method has been identified as viable (the newest review from us is at https://doi.org/10.1016/j.drudis.2022.07.004).

Method Name
Function-site interaction fingerprint method
Free software packages used

IChem (from Dr. Rognan group)

Autodock Vina (Docking)

Acemd (MD simulations)

Relevant publications of previous uses by your group of this software/method

(1) Zheng Zhao, Philip E. Bourne. J. Proteome Res. 2020, 19 (11), 4698–4705
(2) Zheng Zhao, Philip E. Bourne.  J. Chem. Theory Comput. 2020, 16 (5), 3152-3161
(3) Zheng Zhao, Lei Xie, Philip E. Bourne. J. Chem. Inf. Model. 2018, 59 (1), 453-462
(4) Zheng Zhao, Lei Xie, Philip E. Bourne. PLoS One 2017, 12 (6), e0179936
(5) Zheng Zhao, Qingsong Liu, Spencer Bliven, Lei Xie, Philip E Bourne. J. Med. Chem. 2017, 60 (7), 2879–2889
(6) Zheng Zhao, Li Xie, Lei Xie, Philip E. Bourne. J. Med. Chem. 2016, 59 (9), 4326-4341