Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Summary Exploitation of protein structures for potential drug leads by molecular docking is critically dependent on methods for scoring putative protein-ligand interactions. An ideal function for scoring must exhibit predictive accuracy and high computational speed, and must be tolerant of variations in the relative protein-ligand molecular alignment and conformation. This paper describes the development of an empirically derived scoring function, based on the binding affinities of protein-ligand complexes coupled with their crystallographically determined structures. The function's primary terms involve hydrophobic and polar complementarity, with additional terms for entropic and solvation effects. The issue of alignment/conformation dependence was solved by constructing a continuous differentiable nonlinear function with the requirement that maxima in ligand conformation/alignment space corresponded closely to crystallographically determined structures. The expected error in the predicted affinity based on cross-validation was 1.0 log unit. The function is sufficiently fast and accurate to serve as the objective function of a molecular-docking search engine. The function is particularly well suited to the docking problem, since it has spatially narrow maxima that are broadly accessible via gradient descent.     

蛋白质-蛋白质分子对接中打分函数研究进展

分子对接是研究分子间相互作用与识别的有效方法.其中,用于近天然构象挑选的打分函数的合理设计对于对接中复合物结构的成功预测至关重要.本文回顾了蛋白质-蛋白质分子对接组合打分函数中一些主要打分项,包括几何互补项、界面接触面积、范德华相互作用能、静电相互作用能以及统计成对偏好势等打分项的计算方法.结合本研究小组的工作,介绍了目前普遍使用的打分方案以及利用与结合位点有关的信息进行结构筛选的几种策略,比较并总结了常用打分函数的特点.最后,分析并指出了当前蛋白质-蛋白质对接打分函数所存在的主要问题,并对未来的工作进行了展望.     

14种结合自由能评价函数的比较

采用LigandFit作为构象采样工具，以230个蛋白质-配体复合物组成的预测集，系统地比较了14种自由能评价函数（Ligscore1、Ligscore2、Plp1、Plp2、Jain、Pmf、Ludi1、Ludi2、Ludi3、D-score、Pmf-score、G-score、Chemscore以及Xscore）对蛋白质和小分子之间的结合模式以及结合自由能的预测能力. Plp1、Plp2、G-score、Pmf和Xscore在预测测试集结合自由能时得到的分数同实验测定的结合自由能的线性相关系数大于50%. 在识别配体分子实验结合构象的能力方面， 选择测试构象与实际构象间的位置均方根偏差rmsd≤0.20 nm作为评价标准，14种评价函数的成功率从46%到77%不等，其中Ligscore1、Ligscore2、Plp1、Plp2以及Xscore的成功率都在70%以上. 将评价函数中的2个或者3个组合得到一组共同评价函数可以进一步提高实验构象的预测能力， 其预测成功率可以达到80%. 实验表明Xscore、Plp1和Plp2在对接和评价方面都得到较好的结果.     

The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure

Summary A new simple empirical function has been developed that estimates the free energy of binding for a given protein-ligand complex of known 3D structure. The function takes into account hydrogen bonds, ionic interactions, the lipophilic protein-ligand contact surface and the number of rotatable bonds in the ligand. The dataset for the calibration of the function consists of 45 protein-ligand complexes. The new energy function reproduces the binding constants (ranging from 2.5·10^-2 to 4·10^-14 M, corresponding to binding energies between -9 and -76 kJ/mol) of the dataset with a standard deviation of 7.9 kJ/mol, corresponding to 1.4 orders of magnitude in binding affinity. The individual contributions to protein-ligand binding obtained from the scoring function are: ideal neutral hydrogen bond: -4.7 kJ/mol; ideal ionic interaction: -8.3 kJ/mol; lipophilic contact: -0.17 kJ/mol Ų; one rotatable bond in the ligand: +1.4 kJ/mol. The function also contains a constant contribution (+5.4 kJ/mol) which may be rationalized as loss of translational and rotational entropy. The function can be evaluated very fast and is therefore also suitable for application in a 3D database search or de novo ligand design program such as LUDI.     

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.     

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.     

Design,synthesis and evaluation of PPAR gamma binding activity of 2-thioxo-4-thiazolidinone derivatives

We designed and synthesized a series of 2-thioxo-4-thiazolidinone derivatives and evaluated them on peroxisome proliferator activated receptor γ(PPARγ) binding activities.Through the biological assays,compounds 18 and 38 were highlighted with K_i values of 12.15 nmol/Land 14.46 nmol/L,respectively.Then structure-activity relationship(SAR) was analyzed to screen privileged structural modifications.Moreover,molecular fitting of these compounds onto the approved drug Rosightazone in the PPARγligand binding domain was performed to elucidate the SAR and explore potential receptor-ligand interactions.These results demonstrate that the 2-thioxo-4-thiazolidinones can be considered as new promising molecular probes with excellent binding activities to PPARγ.     

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.     

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand‐specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide‐protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X‐ray structure refinement programs. © 2013 Wiley Periodicals, Inc.     

Impressive Enrichment of Semiempirical Quantum Mechanics-Based Scoring Function: HSP90 Protein with 4541 Inhibitors and Decoys

This paper describes the excellent performance of a newly developed scoring function (SF), based on the semiempirical QM (SQM) PM6-D3H4X method combined with the conductor-like screening implicit solvent model (COSMO). The SQM/COSMO, Amber/GB and nine widely used SFs have been evaluated in terms of ranking power on the HSP90 protein with 72 biologically active compounds and 4469 structurally similar decoys. Among conventional SFs, the highest early and overall enrichment measured by EF₁ and AUC% obtained using single-scoring-function ranking has been found for Glide SP and Gold-ASP SFs, respectively (7, 75 % and 3, 76 %). The performance of other standard SFs has not been satisfactory, mostly even decreasing below random values. The SQM/COSMO SF, where P−L structures were optimised at the advanced Amber level, has resulted in a dramatic enrichment increase (47, 98 %), almost reaching the best possible receiver operator characteristic (ROC) curve. The best SQM frame thus inserts about seven times more active compounds into the selected dataset than the best standard SF.     

Multiple ligand simultaneous docking: Orchestrated dancing of ligands in binding sites of protein

Present docking methodologies simulate only one single ligand at a time during docking process. In reality, the molecular recognition process always involves multiple molecular species. Typical protein–ligand interactions are, for example, substrate and cofactor in catalytic cycle; metal ion coordination together with ligand(s); and ligand binding with water molecules. To simulate the real molecular binding processes, we propose a novel multiple ligand simultaneous docking (MLSD) strategy, which can deal with all the above processes, vastly improving docking sampling and binding free energy scoring. The work also compares two search strategies: Lamarckian genetic algorithm and particle swarm optimization, which have respective advantages depending on the specific systems. The methodology proves robust through systematic testing against several diverse model systems: E. coli purine nucleoside phosphorylase (PNP) complex with two substrates, SHP2NSH2 complex with two peptides and Bcl‐xL complex with ABT‐737 fragments. In all cases, the final correct docking poses and relative binding free energies were obtained. In PNP case, the simulations also capture the binding intermediates and reveal the binding dynamics during the recognition processes, which are consistent with the proposed enzymatic mechanism. In the other two cases, conventional single‐ligand docking fails due to energetic and dynamic coupling among ligands, whereas MLSD results in the correct binding modes. These three cases also represent potential applications in the areas of exploring enzymatic mechanism, interpreting noisy X‐ray crystallographic maps, and aiding fragment‐based drug design, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Metal(II) complexes of bioactive aminonaphthoquinone-based ligand: synthesis,characterization and BSA binding,DNA binding/cleavage,and cytotoxicity studies

An aminonaphthoquinone ligand, L, and its metal complexes of general formula [MLCl₂] {M = Co(II), Ni(II), Cu(II) and Zn(II)} have been synthesized and characterized by analytical and spectral techniques. Tetrahedral geometry has been assigned to Ni(II) and Zn(II) complexes and square planar geometry to Co(II) and Cu(II) complexes on the basis of electronic spectral and magnetic susceptibility data. The binding of complexes with bovine serum albumin (BSA) is relatively stronger than that of free ligand and alters the conformation of the protein molecule. Interaction of these complexes with CT-DNA has been investigated using UV-Vis and fluorescence quenching experiments, which show that the complexes bind strongly to DNA through intercalative mode of binding (K_app 10⁵ M^?1). Molecular docking studies reiterate the mode of binding of these compounds with DNA, proposed by spectral studies. The ligand and its complexes cleave plasmid DNA pUC18 to nicked (Form II) and linear (Form III) forms in the presence of H₂O₂ oxidant. The in vitro cytotoxicity screening shows that Cu(II) complex is more potent against MCF-7 cells and Zn(II) complex exhibits marked cytotoxicity against A-549 cells equal to that of cisplatin. Cell imaging studies suggested apoptosis mode of cell death in these two chosen cell lines.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.     

Electrostatic component of binding energy: Interpreting predictions from poisson–boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson–Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔG_elec) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔG_elec. We argue that the sensitivity of the absolute ΔΔG_elec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM‐27, AMBER‐94, and OPLS‐AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔG_elec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔG_elec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.     

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.     

2-Electron reduction of N2O yields a new ligand binding mode: N-Nitrosoimide

A new mode of bonding of the traditionally weak ligand N₂O to highly reducing transition metal complexes is described based on DFT calculations for a variety of late transition metals. These η¹-NNO can be singly bent (at the central nitrogen) or doubly bent (at both N), and thus involve a triple or double M/N bond, respectively. The ligand is therefore properly termed an N-nitrosoimide, N₂O²⁻.     

Binding interactions of mixed ligand copper(II) amino acid Schiff base complexes with biological targets: Spectroscopic evaluation and molecular docking

Three mixed ligand copper(II) complexes [Cu(o ‐vanillin‐l ‐tryptophan Schiff base)(diimine)] (diimine =2,2′‐bipyridine ( 1 ), 1,10‐phenanthroline ( 2 ) and 5,6‐dimethyl‐1,10‐phenanthroline( 3 )) were synthesized and characterized using analytical and spectral methods. The molecular structures of 1 – 3 were optimized using density functional theory (DFT) at B3LYP/LanL2DZ levels in the gas phase. Spectral and DFT studies suggest a distorted square pyramidal geometry around the copper ion. Binding interactions of 1 – 3 with calf thymus DNA and bovine serum albumin protein were studied using UV–visible and fluorescence spectroscopies, viscometric titrations and cyclic voltammetry and also using molecular docking analysis. Studies of the binding of the complexes with calf thymus DNA reveal intercalation, which is supported by molecular docking simulation. The DNA cleavage nature of 1 – 3 with pUC19 DNA shows that the complexes can cleave DNA without any external agents, and the efficiency follows the order 1  >  3  >  2 . Synchronous and three‐dimensional fluorescence spectral studies suggest that the secondary structures of the protein are altered by the complexes. Antioxidant studies reveal that the complexes have significant radical scavenging activity against DPPH. In vitro cytotoxic activity of the complexes was evaluated against breast cancer cells (MCF‐7), revealing that complex 2 exhibits higher cytotoxicity than the other complexes. Nuclear chromatin condensation and fragmentation were observed with DAPI staining assay. The mitochondrial membrane potential damage was studied by FITC staining assay. Flow cytometric analysis suggests that all the metal complexes induce cell apoptosis.     

Stannoxane capping derived from chiral tridentate NNO donor ligand for nickel and copper macrocycles: Comparative binding studies of stannoxane moiety and its modulated copper complex with CTDNA

Novel stannoxane type dinuclear tin complex C₁₆H₁₃N₄O₂Sn₂Cl₇ (1) and its modulated macrocyclic complexes [C₂₄H₃₆N₁₀O₃Sn₂CuCl₇] ClO₄ (2) and [C₂₄H₃₄N₁₀O₂Sn₂NiCl₇] ClO₄ (3) were synthesized and characterized by elemental analysis and various spectroscopic techniques (IR, ¹H, ¹³C, ¹¹⁹Sn NMR, ESI-MS, EPR and UV-Vis). ¹¹⁹Sn NMR shows the presence of two tin metal centers in different environment. The proposed pseudo-octahedral geometry of copper in complex 2 and square pyramidal geometry of nickel in complex 3 were established by the analysis of spectroscopic data. Absorption and fluorescence spectral studies and viscosity measurements have been carried out to assess the comparative binding of dinuclear stannoxane complex 1 and its modulated copper complex 2 with calf thymus DNA. The intrinsic binding constants K_b of the complex 1 and 2 were determined as 4.4 × 10⁴ M⁻¹ and 7.5 × 10⁴ M⁻¹, respectively. Cyclic voltammetric studies have also been employed to ascertain the binding of complex 2 with CTDNA. The results suggest that the complex 2 binds to CTDNA twice in the order of magnitude compared to complex 1. Interaction studies of complex 2 with guanosine 5′-monophosphate further confirm the binding via N₇ position of guanine and phosphate moiety.     

Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes

This paper describes the development of a simple empirical scoringfunction designed to estimate the free energy of binding for aprotein–ligand complex when the 3D structure of the complex is knownor can be approximated. The function uses simple contact terms to estimatelipophilic and metal–ligand binding contributions, a simple explicitform for hydrogen bonds and a term which penalises flexibility. Thecoefficients of each term are obtained using a regression based on 82ligand–receptor complexes for which the binding affinity is known. Thefunction reproduces the binding affinity of the complexes with across-validated error of 8.68 kJ/mol. Tests on internal consistency indicatethat the coefficients obtained are stable to changes in the composition ofthe training set. The function is also tested on two test sets containing afurther 20 and 10 complexes, respectively. The deficiencies of this type offunction are discussed and it is compared to approaches by other workers.     

Advanced statistical evaluation of the complex formation constants from electrophoretic data II: Diastereomeric ion-pairs of (R,S)-N-(3,5-dinitrobenzoyl)leucine and tert-butylcarbamoylquinine

Intermolecular association and ion-pair formation, respectively, between a cationic chiral selector, viz. o-9-(tert-butylcarbamoyl) quinine (CQN), and the both enantiomers of anionic N-(3,5-dinitrobenzoyl)leucine, (R)-DNB-Leu and (S)-DNB-Leu, were investigated by affinity capillary electrophoresis (ACE). Thus, binding constants of the both diastereomeric ion-pairs, (R) and (S)-DNB-Leu/CQN associates, were determined by different experimental setups and correction of nonlinear effects. A reciprocal setup was employed for the high-affinity (S)-enantiomer, and the experimental mobility data obtained for CQN at variable (S)-DNB-Leu concentrations in the background electrolyte were linearized and evaluated by advanced statistical model. A binding constant of K_S=125.1 l mol⁻¹ was afforded. The constant for the (R)-enantiomer, which is outside the range suitable for direct affinity CE, was obtained from indirect affinity CE utilizing the separation of the DNB-Leu racemate at a single appropriate CQN concentration in the BGE (resolution method) taking advantage of the known constant for the (S)-enantiomer yielding a binding constant of K_R=2.51 l mol⁻¹. Thereby, the so-called “constant time method” was adopted for the required precise measurement of the effective mobilities of the both enantiomers. A combined approach of reciprocal affinity CE with racemic DNB-Leu as additive and the resolution method confirmed the results. The resulting constants evidence excellent enantioselectivity of the tert-butylcarbamoyl derivative of the cinchona alkaloid quinine as chiral selector for N-(3,5-dinitrobenzoyl) derivatives of amino acids.