TY - JOUR
T1 - AA-Score
T2 - a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking
AU - Pan, Xiaolin
AU - Wang, Hao
AU - Zhang, Yueqing
AU - Wang, Xingyu
AU - Li, Cuiyu
AU - Ji, Changge
AU - Zhang, John Z.H.
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/5/23
Y1 - 2022/5/23
N2 - The protein−ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein−ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, πstacking, π-cation, and metal−ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein−ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.
AB - The protein−ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein−ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, πstacking, π-cation, and metal−ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein−ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.
UR - http://www.scopus.com/inward/record.url?scp=85129278615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129278615&partnerID=8YFLogxK
U2 - 10.1021/acs.jcim.1c01537
DO - 10.1021/acs.jcim.1c01537
M3 - Article
C2 - 35452230
AN - SCOPUS:85129278615
SN - 1549-9596
VL - 62
SP - 2499
EP - 2509
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 10
ER -