Statistical potential for modeling and ranking of protein-ligand interactions

Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 ? from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).     

An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials

Using a novel iterative method, we have developed a knowledge-based scoring function (ITScore) to predict protein-ligand interactions. The pair potentials for ITScore were derived from a training set of 786 protein-ligand complex structures in the Protein Data Bank. Twenty-six atom types were used based on the atom type category of the SYBYL software. The iterative method circumvents the long-standing reference state problem in the derivation of knowledge-based scoring functions. The basic idea is to improve pair potentials by iteration until they correctly discriminate experimentally determined binding modes from decoy ligand poses for the ligand-protein complexes in the training set. The iterative method is efficient and normally converges within 20 iterative steps. The scoring function based on the derived potentials was tested on a diverse set of 140 protein-ligand complexes for affinity prediction, yielding a high correlation coefficient of 0.74. Because ITScore uses SYBYL-defined atom types, this scoring function is easy to use for molecular files prepared by SYBYL or converted by software such as BABEL.     

DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes

We introduce the new knowledge-based scoring function DSX that consists of distance-dependent pair potentials, novel torsion angle potentials, and newly defined solvent accessible surface-dependent potentials. DSX pair potentials are based on the statistical formalism of DrugScore, extended by a much more specialized set of atom types. The original DrugScore-like reference state is rather unstable with respect to modifications in the used atom types. Therefore, an important method to overcome this problem and to allow for robust results when deriving pair potentials for arbitrary sets of atom types is presented. A validation based on a carefully prepared test set is shown, enabling direct comparison to the majority of other popular scoring functions. Here, DSX features superior performance with respect to docking- and ranking power and runtime requirements. Furthermore, the beneficial combination with torsion angle-dependent and desolvation-dependent potentials is demonstrated. DSX is robust, flexible, and capable of working together with special features of popular docking engines, e.g., flexible protein residues in AutoDock or GOLD. The program is freely available to the scientific community and can be downloaded from our Web site www.agklebe.de .     

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.     

Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?

Fast and accurate predicting of the binding affinities of large sets of diverse protein?ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein?ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K(i)/K(d) units from measured inhibition affinities and the highest Pearson's correlation coefficient of R(p)2 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high-quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based scoring functions in binding affinity prediction.     

Multiple ligand docking by Glide: implications for virtual second-site screening

Performance of Glide was evaluated in a sequential multiple ligand docking paradigm predicting the binding modes of 129 protein-ligand complexes crystallized with clusters of 2-6 cooperative ligands. Three sampling protocols (single precision-SP, extra precision-XP, and SP without scaling ligand atom radii-SP hard) combined with three different scoring functions (GlideScore, Emodel and Glide Energy) were tested. The effects of ligand number, docking order and druglikeness of ligands and closeness of the binding site were investigated. On average 36?% of all structures were reproduced with RMSDs lower than 2??. Correctly docked structures reached 50?% when docking druglike ligands into closed binding sites by the SP hard protocol. Cooperative binding to metabolic and transport proteins can dramatically alter pharmacokinetic parameters of drugs. Analyzing the cytochrome P450 subset the SP hard protocol with Emodel ranking reproduced two-thirds of the structures well. Multiple ligand binding is also exploited by the fragment linking approach in lead discovery settings. The HSP90 subset from real life fragment optimization programs revealed that Glide is able to reproduce the positions of multiple bound fragments if conserved water molecules are considered. These case studies assess the utility of Glide in sequential multiple docking applications.     

An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA

An improved potential mean force (PMF) scoring function, named KScore, has been developed by using 23 redefined ligand atom types and 17 protein atom types, as well as 28 newly introduced atom types for nucleic acids (DNA and RNA). Metal ions and water molecules embedded in the binding sites of receptors are considered explicitly by two newly defined atom types. The individual potential terms were devised on the basis of the high-resolution crystal and NMR structures of 2,422 protein-ligand complexes, 300 DNA-ligand complexes, and 97 RNA-ligand complexes. The optimized atom pairwise distances and minima of the potentials overcome some of the disadvantages and ambiguities of current PMF potentials; thus, they more reasonably explain the atomic interaction between receptors and ligands. KScore was validated against five test sets of protein-ligand complexes and two sets of nucleic-acid-ligand complexes. The results showed acceptable correlations between KScore scores and experimentally determined binding affinities (log K i's or binding free energies). In particular, KScore can be used to rank the binding of ligands with metalloproteins; the linear correlation coefficient ( R) for the test set is 0.65. In addition to reasonably ranking protein-ligand interactions, KScore also yielded good results for scoring DNA/RNA--ligand interactions; the linear correlation coefficients for DNA-ligand and RNA-ligand complexes are 0.68 and 0.81, respectively. Moreover, KScore can appropriately reproduce the experimental structures of ligand-receptor complexes. Thus, KScore is an appropriate scoring function for universally ranking the interactions of ligands with protein, DNA, and RNA.     

应用机器学习方法构建药物分子解离速率常数的预测模型

越来越多的研究表明:药物分子与靶标分子的结合动力学性质与其在体内的药效有很强的相关性。因此,以改善结合动力学性质为导向的分子设计为药物研发提供了新的思路。本工作的研究目标在于得出预测药物分子解离速率常数(koff)的通用型定量结构-动力学关系(QSKR)模型。我们从文献中收集了406个配体分子的解离速率常数实验值,采用分子模拟方法构建了所有配体与靶蛋白复合物的三维结构模型。然后基于蛋白-配体原子对描述符,采用随机森林算法来构建预测配体分子解离速率常数的QSKR模型。通过探索不同条件(如距离区间,划分区间宽度和特征选择标准)下产生的描述符集合对模型预测精度的影响,确定当采用距离阈值为15?、划分区间宽度为3?、特征选择方差水平为2时得到的QSKR模型为最优,在两个独立测试集上获得良好的预测精度(相关系数为0.62)。本工作对预测药物分子解离速率常数这一关键科学问题进行了有益的探索,可为后续研究提供思路。     

Ligand Identification Scoring Algorithm (LISA)

A central problem in de novo drug design is determining the binding affinity of a ligand with a receptor. A new scoring algorithm is presented that estimates the binding affinity of a protein-ligand complex given a three-dimensional structure. The method, LISA (Ligand Identification Scoring Algorithm), uses an empirical scoring function to describe the binding free energy. Interaction terms have been designed to account for van der Waals (VDW) contacts, hydrogen bonding, desolvation effects, and metal chelation to model the dissociation equilibrium constants using a linear model. Atom types have been introduced to differentiate the parameters for VDW, H-bonding interactions, and metal chelation between different atom pairs. A training set of 492 protein-ligand complexes was selected for the fitting process. Different test sets have been examined to evaluate its ability to predict experimentally measured binding affinities. By comparing with other well-known scoring functions, the results show that LISA has advantages over many existing scoring functions in simulating protein-ligand binding affinity, especially metalloprotein-ligand binding affinity. Artificial Neural Network (ANN) was also used in order to demonstrate that the energy terms in LISA are well designed and do not require extra cross terms.     

PLASS: Protein-ligand affinity statistical score – a knowledge-based force-field model of interaction derived from the PDB

We have developed PLASS (Protein-Ligand Affinity Statistical Score), a pair-wise potential of mean-force for rapid estimation of the binding affinity of a ligand molecule to a protein active site. This scoring function is derived from the frequency of occurrence of atom-type pairs in crystallographic complexes taken from the Protein Data Bank (PDB). Statistical distributions are converted into distance-dependent contributions to the Gibbs free interaction energy for 10 atomic types using the Boltzmann hypothesis, with only one adjustable parameter. For a representative set of 72 protein-ligand structures, PLASS scores correlate well with the experimentally measured dissociation constants: a correlation coefficient R of 0.82 and RMS error of 2.0 kcal/mol. Such high accuracy results from our novel treatment of the volume correction term, which takes into account the inhomogeneous properties of the protein-ligand complexes. PLASS is able to rank reliably the affinity of complexes which have as much diversity as in the PDB.     

Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function

Physics-based force fields for ligand–protein docking usually determine electrostatic energy with distance-dependent dielectric (DDD) functions, which do not fully account for the dielectric permittivity variance between ~2 in the protein core and ~80 in bulk water. Here we propose an atom–atom solvent exposure- and distance-dependent dielectric (SEDDD) function, which accounts for both electrostatic and dehydration energy components. Docking was performed using the ZMM program, the AMBER force field, and precomputed libraries of ligand conformers. At the seeding stage, hundreds of thousands of positions and orientations of conformers from the libraries were sampled within the rigid protein. At the refinement stage, the ten lowest-energy structures from the seeding stage were Monte Carlo-minimized with the flexible ligand and flexible protein. A search was considered a success if the root mean square deviation (RMSD) of the ligand atoms in the apparent global minimum from the x-ray structure was <2 Å. Calculations on an examining set of 60 ligand–protein complexes with different DDD functions and solvent-exclusion energy revealed outliers in most of which the ligand-binding site was located at the protein surface. Using a training set of 16 ligand–protein complexes, which did not overlap with the examining set, we parameterized the SEDDD function to minimize the RMSD of the apparent global minima from the x-ray structures. Recalculation of the examining set with the SEDDD function demonstrated a 20% increase in the success rate versus the best-performing DDD function.     

Maximum common binding modes (MCBM): consensus docking scoring using multiple ligand information and interaction fingerprints

Improving the scoring functions for small molecule-protein docking is a highly challenging task in current computational drug design. Here we present a novel consensus scoring concept for the prediction of binding modes for multiple known active ligands. Similar ligands are generally believed to bind to their receptor in a similar fashion. The presumption of our approach was that the true binding modes of similar ligands should be more similar to each other compared to false positive binding modes. The number of conserved (consensus) interactions between similar ligands was used as a docking score. Patterns of interactions were modeled using ligand receptor interaction fingerprints. Our approach was evaluated for four different data sets of known cocrystal structures (CDK-2, dihydrofolate reductase, HIV-1 protease, and thrombin). Docking poses were generated with FlexX and rescored by our approach. For comparison the CScore scoring functions from Sybyl were used, and consensus scores were calculated thereof. Our approach performed better than individual scoring functions and was comparable to consensus scoring. Analysis of the distribution of docking poses by self-organizing maps (SOM) and interaction fingerprints confirmed that clusters of docking poses composed of multiple ligands were preferentially observed near the native binding mode. Being conceptually unrelated to commonly used docking scoring functions our approach provides a powerful method to complement and improve computational docking experiments.     

In silico footprinting of ligands binding to the minor groove of DNA

The sequence selectivity of small molecules binding to the minor groove of DNA can be predicted by "in silico footprinting". Any potential ligand can be docked in the minor groove and then moved along it using simple simulation techniques. By applying a simple scoring function to the trajectory after energy minimization, the preferred binding site can be identified. We show application to all known noncovalent binding modes, namely 1:1 ligand:DNA binding (including hairpin ligands) and 2:1 side-by-side binding, with various DNA base pair sequences and show excellent agreement with experimental results from X-ray crystallography, NMR, and gel-based footprinting.     

Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks

Scoring functions of protein–ligand interactions are widely used in computationally docking software and structure-based drug discovery. Accurate prediction of the binding energy between the protein and the ligand is the main task of the scoring function. The accuracy of a scoring function is normally evaluated by testing it on the benchmarks of protein–ligand complexes. In this work, we report the evaluation analysis of an improved version of scoring function SPecificity and Affinity (SPA). By testing on two independent benchmarks Community Structure-Activity Resource (CSAR) 2014 and Comparative Assessment of Scoring Functions (CASF) 2013, the assessment shows that SPA is relatively more accurate than other compared scoring functions in predicting the interactions between the protein and the ligand. We conclude that the inclusion of the specificity in the optimization can effectively suppress the competitive state on the funnel-like binding energy landscape, and make SPA more accurate in identifying the “native” conformation and scoring the binding decoys. The evaluation of SPA highlights the importance of binding specificity in improving the accuracy of the scoring functions.     

次级键与晶体中锑(III)螯合物配位多面体研究

考虑立体活性孤对电子附近次级键配位原子的贡献, 对文献报道的三十个氨基多羧酸锑(III)螯合物的晶体结构中配位多面体描述进行了全面的修正. 配位多面体的几何构型指定采用了单位球内截多面体的两面角判据及其相关的ANVPDA程序. 所有配位多面体几何构型的修正均得到了键价计算的有力支持.     

次级键与晶体中锑(III)螯合物配位多面体研究

考虑立体活性孤对电子附近次级键配位原子的贡献,对文献报道的三十个氨基多羧酸锑(III)螯合物的晶体结构中配位多面体描述进行了全面的修正.配位多面体的几何构型指定采用了单位球内截多面体的两面角判据及其相关的ANVPDA程序.所有配位多面体几何构型的修正均得到了键价计算的有力支持.     

A Combination of Spin Diffusion Methods for the Determination of Protein–Ligand Complex Structural Ensembles

Structure‐based drug design (SBDD) is a powerful and widely used approach to optimize affinity of drug candidates. With the recently introduced INPHARMA method, the binding mode of small molecules to their protein target can be characterized even if no spectroscopic information about the protein is known. Here, we show that the combination of the spin‐diffusion‐based NMR methods INPHARMA, trNOE, and STD results in an accurate scoring function for docking modes and therefore determination of protein–ligand complex structures. Applications are shown on the model system protein kinase A and the drug targets glycogen phosphorylase and soluble epoxide hydrolase (sEH). Multiplexing of several ligands improves the reliability of the scoring function further. The new score allows in the case of sEH detecting two binding modes of the ligand in its binding site, which was corroborated by X‐ray analysis.     

Investigation of MM-PBSA rescoring of docking poses

Target-based virtual screening is increasingly used to generate leads for targets for which high quality three-dimensional (3D) structures are available. To allow large molecular databases to be screened rapidly, a tiered scoring scheme is often employed whereby a simple scoring function is used as a fast filter of the entire database and a more rigorous and time-consuming scoring function is used to rescore the top hits to produce the final list of ranked compounds. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches are currently thought to be quite effective at incorporating implicit solvation into the estimation of ligand binding free energies. In this paper, the ability of a high-throughput MM-PBSA rescoring function to discriminate between correct and incorrect docking poses is investigated in detail. Various initial scoring functions are used to generate docked poses for a subset of the CCDC/Astex test set and to dock one set of actives/inactives from the DUD data set. The effectiveness of each of these initial scoring functions is discussed. Overall, the ability of the MM-PBSA rescoring function to (i) regenerate the set of X-ray complexes when docking the bound conformation of the ligand, (ii) regenerate the X-ray complexes when docking conformationally expanded databases for each ligand which include "conformation decoys" of the ligand, and (iii) enrich known actives in a virtual screen for the mineralocorticoid receptor in the presence of "ligand decoys" is assessed. While a pharmacophore-based molecular docking approach, PhDock, is used to carry out the docking, the results are expected to be general to use with any docking method.     

Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function

Based on a statistical mechanics-based iterative method, we have extracted a set of distance-dependent, all-atom pairwise potentials for protein-ligand interactions from the crystal structures of 1300 protein-ligand complexes. The iterative method circumvents the long-standing reference state problem in knowledge-based scoring functions. The resulted scoring function, referred to as ITScore 2.0, has been tested with the CSAR (Community Structure-Activity Resource, 2009 release) benchmark of 345 diverse protein-ligand complexes. ITScore 2.0 achieved a Pearson correlation of R(2) = 0.54 in binding affinity prediction. A comparative analysis has been done on the scoring performances of ITScore 2.0, the van der Waals (VDW) scoring function, the VDW with heavy atoms only, and the force field (FF) scoring function of DOCK which consists of a VDW term and an electrostatic term. The results reveal several important factors that affect the scoring performances, which could be helpful for the improvement of scoring functions.