Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark

2018 ◽  
Vol 13 (4) ◽  
pp. 666-680 ◽  
Author(s):  
Yan Li ◽  
Minyi Su ◽  
Zhihai Liu ◽  
Jie Li ◽  
Jie Liu ◽  
...  
Keyword(s):  
Scoring Functions ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals Protein Ligand Interaction Fingerprints

2017 ◽  
pp. 1072-1091
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

Improving structure-based virtual screening performance via learning from scoring function components

2020 ◽  
Author(s):  
Guo-Li Xiong ◽  
Wen-Ling Ye ◽  
Chao Shen ◽  
Ai-Ping Lu ◽  
Ting-Jun Hou ◽  
...  
Keyword(s):  
Scoring Function ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Promising Alternative ◽  
Screening Performance ◽  
Comparable Performance ◽  
Protein Ligand Interaction ◽  
New Protein ◽  
Virtual Screening Performance ◽  
Energy Components

Abstract Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein–ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

scholarly journals PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1

2013 ◽  
Vol 13 (3) ◽  
pp. 283-286 ◽  
Author(s):  
Muhammad Radifar ◽  
Nunung Yuniarti ◽  
Enade Perdana Istyastono
Keyword(s):  
Initial Step ◽  
Binding Pocket ◽  
Scoring Functions ◽  
Amino Acid Residues ◽  
Ligand Interaction ◽  
Cyclooxygenase 1 ◽  
External Software ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Cox 1

Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIF-assisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.

Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions

2017 ◽  
Vol 50 (2) ◽  
pp. 302-309 ◽  
Author(s):  
Zhihai Liu ◽  
Minyi Su ◽  
Li Han ◽  
Jie Liu ◽  
Qifan Yang ◽  
...  
Keyword(s):  
Scoring Functions ◽  
Ligand Interaction ◽  
Protein Ligand Interaction

Protein Ligand Interaction Fingerprints

2016 ◽  
pp. 128-147
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

scholarly journals Two Rules on the Protein-Ligand Interaction

2008 ◽  
Author(s):  
Xiaodong Pang ◽  
Linxiang Zhou ◽  
Lily Zhang ◽  
Lina Xu ◽  
Xinyi Zhang
Keyword(s):  
Ligand Interaction ◽  
Protein Ligand Interaction

scholarly journals SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Surendra Kumar ◽  
Mi-hyun Kim
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Interaction ◽  
Fingerprint Pattern ◽  
Comparable Performance ◽  
Direct Interpretation ◽  
Benchmark Datasets ◽  
Complex Features

AbstractIn drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

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