Showing papers in "Journal of Chemical Information and Modeling in 2014"

g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations.

Abstract: Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks–Chandler–Andersen like integral method). We showcase the effectiveness of the tool ...

iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach.

Abstract: The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational effi...

Docking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring

Abstract: Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein–ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 A, and 76% of test set inhibitors have an RMSD of less than 2.0 A. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization o...

Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results

Abstract: Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein–ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein–ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of “scoring power” (binding affinity prediction), “ranking power” (relative ranking prediction), ...

Halogen bond: its role beyond drug-target binding affinity for drug discovery and development.

Abstract: Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens ...

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis.

Abstract: We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available

Viscosity of ionic liquids: an extensive database and a new group contribution model based on a feed-forward artificial neural network.

Abstract: A knowledge of various thermophysical (in particular transport) properties of ionic liquids (ILs) is crucial from the point of view of potential applications of these fluids in chemical and related industries. In this work, over 13 000 data points of temperature- and pressure-dependent viscosity of 1484 ILs were retrieved from more than 450 research papers published in the open literature in the last three decades. The data were critically revised and then used to develop and test a new model allowing in silico predictions of the viscosities of ILs on the basis of the chemical structures of their cations and anions. The model employs a two-layer feed-forward artificial neural network (FFANN) strategy to represent the relationship between the viscosity and the input variables: temperature, pressure, and group contributions (GCs). In total, the resulting GC-FFANN model employs 242 GC-type molecular descriptors that are capable of accurately representing the viscosity behavior of ILs composed of 901 distinct...

AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins.

Abstract: Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer’s disease In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands This potential describes both the energetic and geometric components of the interaction The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in r

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set.

Abstract: Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein–ligand complexes. This data set was selected out of 8302 protein–ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualifie...

AlzPlatform: An Alzheimer’s Disease Domain-Specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research

Abstract: Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioac...

Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination.

Abstract: Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

Abstract: Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad ra...

In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

Abstract: Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silico methods are hence developed as an alternative. In this study, a comprehensive data set containing 12 204 diverse compounds with median lethal dose (LD50) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), κ-nearest neighbor (kNN), and naive Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-...

Heteroaromatic π-stacking energy landscapes.

Abstract: In this study we investigate π-stacking interactions of a variety of aromatic heterocycles with benzene using dispersion corrected density functional theory. We calculate extensive potential energy surfaces for parallel-displaced interaction geometries. We find that dispersion contributes significantly to the interaction energy and is complemented by a varying degree of electrostatic interactions. We identify geometric preferences and minimum interaction energies for a set of 13 5- and 6-membered aromatic heterocycles frequently encountered in small drug-like molecules. We demonstrate that the electrostatic properties of these systems are a key determinant for their orientational preferences. The results of this study can be applied in lead optimization for the improvement of stacking interactions, as it provides detailed energy landscapes for a wide range of coplanar heteroaromatic geometries. These energy landscapes can serve as a guide for ring replacement in structure-based drug design.

Steered molecular dynamics simulations for studying protein-ligand interaction in cyclin-dependent kinase 5.

Abstract: In this study, we applied steered molecular dynamics (SMD) simulations to investigate the unbinding mechanism of nine inhibitors of the enzyme cyclin-dependent kinase 5 (CDK5). The study had two ma...

Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study.

Abstract: Accurate and affordable assessment of ligand–protein affinity for structure-based virtual screening (SB-VS) is a standing challenge Hence, empirical postdocking filters making use of various types of structure–activity information may prove useful Here, we introduce one such filter based upon three-dimensional structural protein–ligand interaction fingerprints (SPLIF) SPLIF permits quantitative assessment of whether a docking pose interacts with the protein target similarly to a known ligand and rescues active compounds penalized by poor initial docking scores An extensive benchmark study on 10 diverse data sets selected from the DUD-E database has been performed in order to evaluate the absolute and relative efficiency of this method SPLIF demonstrated an overall better performance than relevant standard methods

Supervised Molecular Dynamics (SuMD) as a Helpful Tool To Depict GPCR–Ligand Recognition Pathway in a Nanosecond Time Scale

Abstract: Supervised MD (SuMD) is a computational method that allows the exploration of ligand–receptor recognition pathway investigations in a nanosecond (ns) time scale. It consists of the incorporation of a tabu-like supervision algorithm on the ligand–receptor approaching distance into a classic molecular dynamics (MD) simulation technique. In addition to speeding up the acquisition of the ligand–receptor trajectory, this implementation facilitates the characterization of multiple binding events (such as meta-binding, allosteric, and orthosteric sites) by taking advantage of the all-atom MD simulations accuracy of a GPCR–ligand complex embedded into explicit lipid–water environment.

Docking studies on DNA intercalators.

Abstract: DNA is an important target for the treatment of multiple pathologies, most notably cancer. In particular, DNA intercalators have often been used as anticancer drugs. However, despite their relevance to drug discovery, only a few systematic computational studies were performed on DNA-intercalator complexes. In this work we have analyzed ligand binding sites preferences in 63 high resolution DNA-intercalator complexes available in the PDB and found that ligands bind preferentially between G and C and between the C and A base pairs (70% and 11%, respectively). Next, we examined the ability of AUTODOCK to accurately dock ligands into preformed intercalation sites. Following the optimization of the docking protocol, AUTODOCK was able to generate conformations with RMSD values 2.00 A suggests that AUTODOCK may overemphasize the hydrogen bonding term. A decision tree was built to identify ligands which are likely to be accurately docked based on their characteristics. This analysis revealed that AUTODOCK performs best for intercalators characterized by a large number of aromatic rings, low flexibility, high molecular weight, and a small number of hydrogen bond acceptors. Finally, for canonical B-DNA structures (where preformed sites are unavailable), we demonstrated that intercalation sites could be formed by inserting an anthracene moiety between the (anticipated) site-flanking base pairs and by relaxing the structure using either energy minimization or preferably molecular dynamics simulations. Such sites were suitable for the docking of different intercalators by AUTODOCK.

Fragment-Based Lead Discovery and Design

Abstract: Fragment-based lead discovery and design has and continues to show increasing promise in drug discovery. In this article, the current state of the art in terms of hot-spot characterization, fragment screening techniques, and fragment-based design is discussed. Three overall fragment-based lead generation strategies are explored and involve the chemical biology characterization of biological targets via fragment screening, fragment screening as a complementary approach to high-throughput screening of drug-like compounds, and direct fragment-based drug discovery, respectively. The evolution and development of fragment libraries is described. With an emphasis on computational approaches and the strategies applied at AstraZeneca, the review illustrates how integration of data from one regime can inform the design of experiments in the other, ultimately leading to the discovery of high quality chemical matter.

Accuracy assessment and automation of free energy calculations for drug design.

Abstract: As the free energy of binding of a ligand to its target is one of the crucial optimization parameters in drug design, its accurate prediction is highly desirable. In the present study we have assessed the average accuracy of free energy calculations for a total of 92 ligands binding to five different targets. To make this study and future larger scale applications possible we automated the setup procedure. Starting from user defined binding modes, the procedure decides which ligands to connect via a perturbation based on maximum common substructure criteria and produces all necessary parameter files for free energy calculations in AMBER 11. For the systems investigated, errors due to insufficient sampling were found to be substantial in some cases whereas differences in estimators (thermodynamic integration (TI) versus multistate Bennett acceptance ratio (MBAR)) were found to be negligible. Analytical uncertainty estimates calculated from a single free energy calculation were found to be much smaller than...

Structure-Based Virtual Screening Approach for Discovery of Covalently Bound Ligands

Abstract: We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS) We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors We implemented a customized “VS mode” of the Schrodinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein–ligand interactions known to be important for activity We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33 The poses

Beware of Machine Learning-Based Scoring Functions—On the Danger of Developing Black Boxes

Abstract: Training machine learning algorithms with protein–ligand descriptors has recently gained considerable attention to predict binding constants from atomic coordinates. Starting from a series of recent reports stating the advantages of this approach over empirical scoring functions, we could indeed reproduce the claimed superiority of Random Forest and Support Vector Machine-based scoring functions to predict experimental binding constants from protein–ligand X-ray structures of the PDBBind dataset. Strikingly, these scoring functions, trained on simple protein–ligand element–element distance counts, were almost unable to enrich virtual screening hit lists in true actives upon docking experiments of 10 reference DUD-E datasets; this is a a feature that, however, has been verified for an a priori less-accurate empirical scoring function (Surflex-Dock). By systematically varying ligand poses from true X-ray coordinates, we show that the Surflex-Dock scoring function is logically sensitive to the quality of doc...

Data Set Modelability by QSAR

Abstract: We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (correct classification rate above 0.7) for a binary data set of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of nearest-neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 data sets, and the threshold of 0.65 was found to separate the nonmodelable and modelable data sets.

Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition.

Abstract: Protein-peptide interactions are prevalent and play essential roles in many living activities. Peptides recognize their protein partners by direct nonbonded interactions and indirect adjustment of conformations. Although processes of protein-peptide recognition have been comprehensively studied in both sequences and structures recently, flexibility of peptides and the configuration entropy penalty in recognition did not get enough attention. In this study, 20 protein-peptide complexes and their corresponding unbound peptides were investigated by molecular dynamics simulations. Energy analysis revealed that configurational entropy penalty introduced by restriction of the degrees of freedom of peptides in indirect readout process of protein-peptide recognition is significant. Configurational entropy penalty has become the main content of the indirect readout energy in protein-peptide recognition instead of deformation energy which is the main source of the indirect readout energy in classical biomolecular recognition phenomena, such as protein-DNA binding. These results provide us a better understanding of protein-peptide recognition and give us some implications in peptide ligand design.

Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization.

Abstract: With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the biological activity in a single cell line or a single tissue type. We extend this line of research in two directions: (1) an integrative QSAR approach predicting the responses to new drugs for a panel of multiple known cancer cell lines simultaneously and (2) a personalized QSAR approach predicting the responses to new drugs for new cancer cell lines. To solve the modeling task, we apply a novel kernelized Bayesian matrix factorization method. For maximum applicability and predictive performance, the method optionally utilizes genomic features of cell lines and target information on drugs in addition to chemical drug descriptors. In a case study with 116 anticancer drugs and 650 cell lines, we demonstrate the usefulness of the method in several relevant prediction scenarios, differing in the amount of available information, and analyze the importance of various types of drug features for the response prediction. Furthermore, after predicting the missing values of the data set, a complete global map of drug response is explored to assess treatment potential and treatment range of therapeutically interesting anticancer drugs.

PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins

Abstract: In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory. PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions. PyInteraph also allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs, along with performing network analysis on the resulting interaction graphs. The program also integrates the network description with a knowledge-based ...

Endocrine Disruptome—An Open Source Prediction Tool for Assessing Endocrine Disruption Potential through Nuclear Receptor Binding

Abstract: Predicting the endocrine disruption potential of compounds is a daunting but essential task. Here we report a new tool for this purpose that we have termed Endocrine Disruptome. It is a free and simple-to-use Web service that runs on an open source platform called Docking interface for Target Systems (DoTS). The molecular docking is handled via AutoDock Vina. Compounds are docked to 18 integrated and well-validated crystal structures of 14 different human nuclear receptors: androgen receptor; estrogen receptors α and β; glucocorticoid receptor; liver X receptors α and β; mineralocorticoid receptor; peroxisome proliferator activated receptors α, β/δ, and γ; progesterone receptor; retinoid X receptor α; and thyroid receptors α and β. Endocrine Disruptome is free of charge and available at http://endocrinedisruptome.ki.si.

Macrocycle conformational sampling with MacroModel.

Abstract: Sampling low energy conformations of macrocycles is challenging due to the large size of many of these molecules and the constraints imposed by the macrocycle. We present a new conformational search method (implemented in MacroModel) that uses brief MD simulations followed by minimization and normal-mode search steps. The method was parametrized using a set of 100 macrocycles from the PDB and CSD. It was then tested on a publicly available data set for which there are published results using alternative methods; we found that when the same force field is used (in this case MMFFs in vacuum), our method tended to identify conformations with lower energies than what the other methods identified. The performance on a new set of 50 macrocycles from the PDB and CSD was also quite good; the mean and median RMSD values for just the ring atoms were 0.60 and 0.33 A, respectively. However, the RMSD values for macrocycles with more than 30 ring-atoms were quite a bit larger compared to the smaller macrocycles. Possib...

Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility.

Abstract: In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naive Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitor...