Forging the basis for developing protein-ligand interaction scoring functions.
Abstract
In structure-based drug design, scoring functions are widely used for fast evaluation of protein-ligand interactions. They are often applied in combination with molecular docking and de novo design methods. Since the early 1990s, a whole spectrum of protein-ligand interaction scoring functions have been developed. Regardless of their technical difference, scoring functions all need data sets combining protein-ligand complex structures and binding affinity data for parametrization and validation. However, data sets of this kind used to be rather limited in terms of size and quality. On the other hand, standard metrics for evaluating scoring function used to be ambiguous. Scoring functions are often tested in molecular docking or even virtual screening trials, which do not directly reflect the genuine quality of scoring functions. Collectively, these underlying obstacles have impeded the invention of more advanced scoring functions. In this Account, we describe our long-lasting efforts to overcome these obstacles, which involve two related projects. On the first project, we have created the PDBbind database. It is the first database that systematically annotates the protein-ligand complexes in the Protein Data Bank (PDB) with experimental binding data. This database has been updated annually since its first public release in 2004. The latest release (version 2016) provides binding data for 16 179 biomolecular complexes in PDB. Data sets provided by PDBbind have been applied to many computational and statistical studies on protein-ligand interaction and various subjects. In particular, it has become a major data resource for scoring function development. On the second project, we have established the Comparative Assessment of Scoring Functions (CASF) benchmark for scoring function evaluation. Our key idea is to decouple the "scoring" process from the "sampling" process, so scoring functions can be tested in a relatively pure context to reflect their quality. In our latest work on this track, i.e. CASF-2013, the performance of a scoring function was quantified in four aspects, including "scoring power", "ranking power", "docking power", and "screening power". All four performance tests were conducted on a test set containing 195 high-quality protein-ligand complexes selected from PDBbind. A panel of 20 standard scoring functions were tested as demonstration. Importantly, CASF is designed to be an open-access benchmark, with which scoring functions developed by different researchers can be compared on the same grounds. Indeed, it has become a popular choice for scoring function validation in recent years. Despite the considerable progress that has been made so far, the performance of today's scoring functions still does not meet people's expectations in many aspects. There is a constant demand for more advanced scoring functions. Our efforts have helped to overcome some obstacles underlying scoring function development so that the researchers in this field can move forward faster. We will continue to improve the PDBbind database and the CASF benchmark in the future to keep them as useful community resources.
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The development of more accurate scoring functions is strategic in the field of SBDD and remains a challenging task, especially in hit-to-lead optimization and de novo design.
“Moreover, the development of more accurate scoring functions is strategic in the field of SBDD and remains a challenging task, especially in the hit-to-lead optimization (Enyedy and Egan, 2008) and de novo design (Liu et al., 2017).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Liu et al., 2017
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- Authors
- Liu, Z., Su, M., Han, L., Liu, J., Yang, Q., Li, Y., & Wang, R.
- Affiliations
- Chinese Academy of Sciences (CN)
- Macau University of Science and Technology (MO)
- Corresponding author
- Li, Yan
- Wang, Renxiao
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-02-09
- Volume
- 50
- Issue
- 2
- Pages
- 302-309
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W2587598315
- Cited by
- 493
- References
- 37
- Citations by year
- 2026: 74 · 2025: 90 · 2024: 84 · 2023: 59 · 2022: 50 · 2021: 43 · 2020: 31 · 2019: 30 · 2018: 27 · 2017: 4 · 2012: 1
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Accounts of Chemical Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2016YFA0502302)
- Fundo para o Desenvolvimento das Ci?ncias e da Tecnologia (055/2013/A2)
- National Natural Science Foundation of China (21102168, 21402230, 21472227, 21673276, 81430083)
- Chinese Academy of Sciences (XDB20000000)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- PDBbind, protein-ligand complex, structure-based drug design, molecular docking, virtual screening, benchmarking, protein-ligand binding affinity prediction, protein-protein interaction, protein-DNA interactions, RNA-protein interactions
- MeSH terms
- Ligands, Protein Binding, Proteins, Drug Design, Databases, Protein, Molecular Docking Simulation
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