Protocol for hit-to-lead optimization of compounds by auto in silico ligand directing evolution (AILDE) approach

Longcan Mei; Fengxu Wu; Gefei Hao; Guangfu Yang

doi:10.1016/j.xpro.2021.100312

Protocol for hit-to-lead optimization of compounds by auto in silico ligand directing evolution (AILDE) approach

STAR Protoc. 2021 Feb 1;2(1):100312. doi: 10.1016/j.xpro.2021.100312. eCollection 2021 Mar 19.

Authors

Longcan Mei^{1

2}, Fengxu Wu^{1

2}, Gefei Hao^{1

2

3}, Guangfu Yang^{1

2

4}

Affiliations

¹ Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
² International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China.
³ State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Research and Development Center for Fine Chemicals, Guizhou University, Guiyang 550025, China.
⁴ Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, China.

Abstract

Hit-to-lead (H2L) optimization is crucial for drug design, which has become an increasing concern in medicinal chemistry. A virtual screening strategy of auto in silico ligand directing evolution (AILDE) has been developed to yield promising lead compounds rapidly and efficiently. The protocol includes instructions for fragment compound library construction, conformational sampling by molecular dynamics simulation, ligand modification by fragment growing, as well as the binding free energy prediction. For complete details on the use and execution of this protocol, please refer to Wu et al. (2020).

Keywords: Bioinformatics; Protein biochemistry; Structural biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Binding Sites
Computer Simulation
Drug Design / methods*
Drug Discovery / methods*
Ligands
Molecular Conformation
Molecular Dynamics Simulation
Quantitative Structure-Activity Relationship
Software

Substances

Ligands