Evaluation of a knowledge‐based potential of mean force for scoring docked protein–ligand complexes

The Biomolecular Ligand Energy Evaluation Protocol (BLEEP) is a knowledge‐based potential derived from high‐resolution X‐ray structures of protein–ligand complexes. The performance of this potential in ranking the hypothetical structures resulting from a docking study has been evaluated using fifteen protein–ligand complexes from the Protein Data Bank. In the majority of complexes BLEEP was successful in identifying the native (experimental) binding mode or an alternative of low rms deviation (from the native) as the lowest in energy. Overall BLEEP is slightly better than the DOCK energy function in discriminating native‐like modes. Even when alternative binding modes rank lower than the native structure, a reasonable energy is assigned to the latter. Breaking down the BLEEP scores into the atom–atom contributions reveals that this type of potential is grossly dominated by longer range interactions (>5 Å), which makes it relatively insensitive to small local variations in the binding site. However, despite this limitation, the lack, at present, of accurate protein–ligand potentials means that BLEEP is a promising approach to improve the filtering of structures resulting from docking programs. Moreover, BLEEP should improve with the continuously increasing number of complexes available in the PDB. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 673–688, 2001     

A Bayesian statistical approach of improving knowledge‐based scoring functions for protein–ligand interactions

Knowledge‐based scoring functions are widely used for assessing putative complexes in protein–ligand and protein–protein docking and for structure prediction. Even with large training sets, knowledge‐based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge‐based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge‐based scoring function with an alternative, force‐field‐based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein–ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge‐based scoring functions and can also be applied to protein–protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc.     

Update of the ATTRACT force field for the prediction of protein–protein binding affinity

Determining the protein–protein interactions is still a major challenge for molecular biology. Docking protocols has come of age in predicting the structure of macromolecular complexes. However, they still lack accuracy to estimate the binding affinities, the thermodynamic quantity that drives the formation of a complex. Here, an updated version of the protein–protein ATTRACT force field aiming at predicting experimental binding affinities is reported. It has been designed on a dataset of 218 protein–protein complexes. The correlation between the experimental and predicted affinities reaches 0.6, outperforming most of the available protocols. Focusing on a subset of rigid and flexible complexes, the performance raises to 0.76 and 0.69, respectively. © 2017 Wiley Periodicals, Inc.     

Evaluation and application of multiple scoring functions for a virtual screening experiment

In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of -40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.     

Semiempirical calculations of binding enthalpy for protein–ligand complexes

A systematic semiempirical quantum mechanical study of the interactions between proteins and ligands has been performed to determine the ability of this approach for the accurate estimation of the enthalpic contribution to the binding free energy of the protein–ligand systems. This approach has been applied for eight test protein–ligand complexes with experimentally known binding enthalpies. The calculations were performed using the semiempirical PM3 approach incorporated in the MOPAC 97, ZAVA originally elaborated in Algodign, and MOPAC 2002 with MOZYME facility packages. Special attention was paid to take into account structural water molecules, which were located in the protein–ligand binding site. It was shown that the results of binding enthalpy calculations fit experimental data within ～2 kcal/mol in the presented approach. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem, 2004     

DOX: A new computational protocol for accurate prediction of the protein–ligand binding structures

Molecular docking techniques have now been widely used to predict the protein–ligand binding modes, especially when the structures of crystal complexes are not available. Most docking algorithms are able to effectively generate and rank a large number of probable binding poses. However, it is hard for them to accurately evaluate these poses and identify the most accurate binding structure. In this study, we first examined the performance of some docking programs, based on a testing set made of 15 crystal complexes with drug statins for the human 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase (HMGR). We found that most of the top ranking HMGR–statin binding poses, predicted by the docking programs, were energetically unstable as revealed by the high theoretical‐level calculations, which were usually accompanied by the large deviations from the geometric parameters of the corresponding crystal binding structures. Subsequently, we proposed a new computational protocol, DOX, based on the joint use of molecular Docking, ONIOM, and eXtended ONIOM (XO) methods to predict the accurate binding structures for the protein–ligand complexes of interest. Our testing results demonstrate that the DOX protocol can efficiently predict accurate geometries for all 15 HMGR‐statin crystal complexes without exception. This study suggests a promising computational route, as an effective alternative to the experimental one, toward predicting the accurate binding structures, which is the prerequisite for all the deep understandings of the properties, functions, and mechanisms of the protein–ligand complexes. © 2015 Wiley Periodicals, Inc.     

Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics‐based force fields, although they cannot fully describe protein–ligand interactions. A noteworthy computational method in development involves large‐scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large‐scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange‐correlation functionals, and solvation effects) on the binding energies of the FK506‐binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2‐MP2/6‐31G(d), is suitable for evaluating the protein–ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure–activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein–ligand interaction distance. Our acceleration scheme, which uses FMO2‐HF/STO‐3G:MP2/6‐31G(d) at R_int = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy. © 2015 Wiley Periodicals, Inc.     

Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions

We present results of testing the ability of eleven popular scoring functions to predict native docked positions using a recently developed method (Ruvinsky and Kozintsev, J Comput Chem 2005, 26, 1089) for estimation the entropy contributions of relative motions to protein-ligand binding affinity. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We use a test set of 100 PDB protein-ligand complexes and ensembles of 101 docked positions generated by (Wang et al. J Med Chem 2003, 46, 2287) for each ligand in the test set. To test the suggested method we compared the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock scoring function, by 2-25% with G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by 0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compared the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a clustering-RMSD and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the clustering-RMSD for 11 scoring functions.     

Sequence‐based prediction of protein–peptide binding sites using support vector machine

Protein–peptide interactions are essential for all cellular processes including DNA repair, replication, gene‐expression, and metabolism. As most protein – peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein – peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine‐learning method called SPRINT to make Sequence‐based prediction of Protein – peptide Residue‐level Interactions. SPRINT yields a robust and consistent performance for 10‐fold cross validations and independent test. The most important feature is evolution‐generated sequence profiles. For the test set (1056 binding and non‐binding residues), it yields a Matthews’ Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence‐based technique shows comparable or more accurate than structure‐based methods for peptide‐binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/ . © 2016 Wiley Periodicals, Inc.     

Effect of ligand volume correction on PMF scoring

Recently, a knowledge‐based scoring function has been introduced that estimates the protein‐binding affinity based on the 3D structure of a protein–ligand complex (J Med Chem 1999, 42, 791). A ligand volume correction factor has been proposed and applied to filter out intraligand interactions in this simplified potential approach. Here we evaluate the effect of the ligand volume correction on the predictive power of the PMF scoring function. It is found that the effect of the ligand volume correction is significant on the derived potentials and large on the overall score. However, the effect of the ligand correction on the predictive power of the scoring function appears to be smaller. For a test set containing serine proteases the predictive power of the PMF scoring function does not change with the introduction of the volume correction. For a test set of metalloprotease complexes, the predictive power of the PMF scoring function improves only slightly when the volume correction is applied. For five test sets comprising a total of 225 diverse protein ligand complexes taken from the Brookhaven Protein Data Bank it is found, however, that the introduction of the ligand volume correction consistently improves the correlation between the PMF scores and the measured binding affinities. The effect of the correction factor on docking/scoring experiments is also analyzed using a test set of 61 biphenyl inhibitor‐stromelysin complexes. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 418–425, 2001     

GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.     

Assessing protein–ligand docking for the binding of organometallic compounds to proteins

Organometallic compounds are increasingly used as molecular scaffolds in drug development projects; their structural and electronic properties offering novel opportunities in protein–ligand complementarities. Interestingly, while protein–ligand dockings have long become a spearhead in computer assisted drug design, no benchmarking nor optimization have been done for their use with organometallic compounds. Pursuing our efforts to model metal mediated recognition processes, we herein present a systematic study of the capabilities of the program GOLD to predict the interactions of protein with organometallic compounds. The study focuses on inert systems for which no alteration of the first coordination sphere of the metal occurs upon binding. Several scaffolds are used as test systems with different docking schemes and scoring functions. We conclude that ChemScore is the most robust scoring function with ASP and ChemPLP providing with good results too and GoldScore slightly underperforming. This study shows that current state‐of‐the‐art protein‐ligand docking techniques are reliable for the docking of inert organometallic compounds binding to protein. © 2013 Wiley Periodicals, Inc.     

Mixed implicit/explicit solvation models in quantum mechanical calculations of binding enthalpy for protein–ligand complexes

An approach to quantum mechanical investigation of interactions in protein–ligand complexes has been developed that treats the solvation effect in a mixed scheme combining implicit and explicit solvent models. In this approach, the first solvation shell of the solvent around the solute is modeled with a limited number of hydrogen bonded explicit solvent molecules. The influence of the remaining bulk solvent is treated as a surrounding continuum in the conductor‐like screening model (COSMO). The enthalpy term of the binding free energy for the protein–ligand complexes was calculated using the semiempirical PM3 method implemented in the MOPAC package, applied to a trimmed model of the protein–ligand complex constructed with special rules. The dependence of the accuracy of binding enthalpy calculations on size of the trimmed model and number of optimized parameters was evaluated. Testing of the approach was performed for 12 complexes of different ligands with trypsin, thrombin, and ribonuclease with experimentally known binding enthalpies. The root‐mean‐square deviation (RMSD) of the calculated binding enthalpies from experimental data was found as ～1 kcal/mol over a large range. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006     

Flexible protein‐ligand docking using the Fleksy protocol

Considering protein plasticity is important in accurately predicting the three‐dimensional geometry of protein‐ligand complexes. Here, we present the first public release of our flexible docking tool Fleksy, which is able to consider both ligand and protein flexibility in the docking process. We describe the workflow and different features of the software and present its performance on two cross‐docking benchmark datasets. © 2012 Wiley Periodicals, Inc.     

Toward quantitative estimates of binding affinities for protein–ligand systems involving large inhibitor compounds: A steered molecular dynamics simulation route

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein – ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein – ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine‐based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein – ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis – Menten mechanism with a competitive inhibitor.     

Coarse‐grained molecular dynamics simulations of protein–ligand binding

Coarse‐grained molecular dynamics (CGMD) simulations with the MARTINI force field were performed to reproduce the protein–ligand binding processes. We chose two protein–ligand systems, the levansucrase–sugar (glucose or sucrose), and LinB–1,2‐dichloroethane systems, as target systems that differ in terms of the size and shape of the ligand‐binding pocket and the physicochemical properties of the pocket and the ligand. Spatial distributions of the Coarse‐grained (CG) ligand molecules revealed potential ligand‐binding sites on the protein surfaces other than the real ligand‐binding sites. The ligands bound most strongly to the real ligand‐binding sites. The binding and unbinding rate constants obtained from the CGMD simulation of the levansucrase–sucrose system were approximately 10 times greater than the experimental values; this is mainly due to faster diffusion of the CG ligand in the CG water model. We could obtain dissociation constants close to the experimental values for both systems. Analysis of the ligand fluxes demonstrated that the CG ligand molecules entered the ligand‐binding pockets through specific pathways. The ligands tended to move through grooves on the protein surface. Thus, the CGMD simulations produced reasonable results for the two different systems overall and are useful for studying the protein–ligand binding processes. © 2014 Wiley Periodicals, Inc.     

Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors

An increasing number of docking/scoring programs are available that use different sampling and scoring algorithms. A reliable scoring function is the crucial element of such approaches. Comparative studies are needed to evaluate their current capabilities. DOCK4 with force field and PMF scoring as well as FlexX were used to evaluate the predictive power of these docking/scoring approaches to identify the correct binding mode of 61 MMP-3 inhibitors in a crystal structure of stromelysin and also to rank them according to their different binding affinities. It was found that DOCK4/PMF scoring performs significantly better than FlexX and DOCK4/FF in both ranking ligands and predicting their binding modes. Most notably, DOCK4/PMF was the only scoring/docking approach that found a significant correlation between binding affinity and predicted score of the docked inhibitors. However, comparing only those cases where the correct binding mode was identified (scoring highest among sampled poses), FlexX showed the best `fine tuning' (lowest rmsd) in predicted binding modes. The results suggest that not so much the sampling procedure but rather the scoring function is the crucial element of a docking program.     

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.