Literature DB >> 28182403

Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.

Zhihai Liu1, Minyi Su1, Li Han1, Jie Liu1, Qifan Yang1, Yan Li1, Renxiao Wang1,2.   

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.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 28182403     DOI: 10.1021/acs.accounts.6b00491

Source DB:  PubMed          Journal:  Acc Chem Res        ISSN: 0001-4842            Impact factor:   22.384


  57 in total

1.  MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints.

Authors:  Jian Wang; Nikolay V Dokholyan
Journal:  J Chem Inf Model       Date:  2019-04-17       Impact factor: 4.956

2.  Visualizing convolutional neural network protein-ligand scoring.

Authors:  Joshua Hochuli; Alec Helbling; Tamar Skaist; Matthew Ragoza; David Ryan Koes
Journal:  J Mol Graph Model       Date:  2018-06-18       Impact factor: 2.518

3.  On the polarization of ligands by proteins.

Authors:  Soohaeng Yoo Willow; Bing Xie; Jason Lawrence; Robert S Eisenberg; David D L Minh
Journal:  Phys Chem Chem Phys       Date:  2020-06-04       Impact factor: 3.676

4.  Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

Authors:  Richard D Smith; Jordan J Clark; Aqeel Ahmed; Zachary J Orban; James B Dunbar; Heather A Carlson
Journal:  J Mol Biol       Date:  2019-05-22       Impact factor: 5.469

5.  Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions.

Authors:  Edelmiro Moman; Maria A Grishina; Vladimir A Potemkin
Journal:  J Comput Aided Mol Des       Date:  2019-11-14       Impact factor: 3.686

6.  Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.

Authors:  Maria Kadukova; Vladimir Chupin; Sergei Grudinin
Journal:  J Comput Aided Mol Des       Date:  2019-11-29       Impact factor: 3.686

7.  Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Authors:  Jianing Lu; Xuben Hou; Cheng Wang; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2019-10-31       Impact factor: 4.956

8.  Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Authors:  Bo Wang; Ho-Leung Ng
Journal:  J Comput Aided Mol Des       Date:  2020-01-08       Impact factor: 3.686

9.  AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters.

Authors:  Joseph Katigbak; Haotian Li; David Rooklin; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2020-02-11       Impact factor: 4.956

10.  Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Authors:  Yuwei Yang; Jianing Lu; Chao Yang; Yingkai Zhang
Journal:  J Comput Aided Mol Des       Date:  2019-11-15       Impact factor: 3.686

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.