Robust scoring functions for protein-ligand interactions with quantum chemical charge models

Examining Hemin and its Derivatives: Induction of Heme-Oxygenase-1 Activity and Oxidative Stress in Breast Cancer Cells through Collaborative Experimental Analysis and Molecular Dynamics Simulations

Hemin triggers intracellular reactive oxygen species (ROS) accumulation and enhances heme oxygenase-1 (HOX-1) activity, indicating its potential as an anticancer agent, though precise control of its intracellular levels is crucial. The study explores the impact of hemin and its derivatives, hemin-tyrosine, and hemin-styrene (H-Styr) conjugates on migration, HOX-1 expression, specific apoptosis markers, mitochondrial functions, and ROS generation in breast cancer cells. Molecular docking and dynamics simulations were used to understand the interactions among HOX-1, heme, and the compounds. Hemin outperforms its derivatives in inducing HOX-1 expression, exhibiting pro-oxidative effects and reducing cell migration. Molecular simulations show that heme binds favorably to HOX-1, followed by the other compounds, primarily through van der Waals and electrostatic forces. However, only van der Waals forces determine the H-Styr complexation. These interactions, influenced by metalloporphyrin characteristics, provide insights into HOX-1 regulation and ROS generation, potentially guiding the development of breast cancer therapies targeting oxidative stress.

Do electrostatic interactions make a difference in physics-based AutoDock4 scoring function?

Physics-based scoring function AutoDock4 is one of the most successfully applied tools in the area of structure-based drug design. However, current scoring functions are still far from being perfect. In a recent work highlighting the strengths and deficiencies of current scoring functions, we discovered that the residual error of ΔGbind predictions made by AutoDock4 is highly correlated to the presence of formally charged fragments in a ligand. In this work, we study how the use of the high-quality atomic charges, applied for contemporary force fields calculation, affects the quality of the experimental ΔGbind prediction by means of AutoDock4. We initially expected that the previously found discrepancy could be attributed to the Gasteiger charges used within AutoDock4. We show that AutoDock4 is, surprisingly, not sensitive to the charges used, and the use of QC-derived atomic charges does not lead to any statistical improvements. We also briefly discuss the role of the explicit empirical hydrogen bond term along with the electrostatic term.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features

Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand-receptor complexes, which is a common problem in the field.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem

Despite considerable advances obtained by applying machine learning approaches in protein–ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein–ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Novel Acetylcholinesterase Inhibitors Based on Uracil Moiety for Possible Treatment of Alzheimer Disease

In this study, novel derivatives based on 6-methyluracil and condensed uracil were synthesized, namely, 2,4-quinazoline-2,4-dione with ω-(ortho-nitrilebenzylethylamino) alkyl chains at the N atoms of the pyrimidine ring. In this series of synthesized compounds, the polymethylene chains were varied from having tetra- to hexamethylene chains, and secondary NH, tertiary ethylamino, and quaternary ammonium groups were introduced into the chains. The molecular modeling of the compounds indicated that they could function as dual binding site acetylcholinesterase inhibitors, binding to both the peripheral anionic site and active site. The data from in vitro experiments show that the most active compounds exhibit affinity toward acetylcholinesterase within a nanomolar range, with selectivity for acetylcholinesterase over butyrylcholinesterase reaching four orders of magnitude. In vivo biological assays demonstrated the potency of these compounds in the treatment of memory impairment using an animal model of Alzheimer disease.

Hesperidin methyl chalcone interacts with NFκB Ser276 and inhibits zymosan-induced joint pain and inflammation, and RAW 264.7 macrophage activation

Arthritis can be defined as a painful musculoskeletal disorder that affects the joints. Hesperidin methyl chalcone (HMC) is a flavonoid with analgesic, anti-inflammatory, and antioxidant effects. However, its effects on a specific cell type and in the zymosan-induced inflammation are unknown. We aimed at evaluating the effects of HMC in a zymosan-induced arthritis model. A dose-response curve of HMC (10, 30, or 100 mg/kg) was performed to determine the most effective analgesic dose after intra-articular zymosan stimuli. Knee joint oedema was determined using a calliper. Leukocyte recruitment was performed by cell counting on knee joint wash as well as histopathological analysis. Oxidative stress was measured by colorimetric assays (GSH, FRAP, ABTS and NBT) and RT-qPCR (gp91^phox and HO-1 mRNA expression) performed. In vitro, oxidative stress was assessed by DCFDA assay using RAW 264.7 macrophages. Cytokine production was evaluated in vivo and in vitro by ELISA. In vitro NF-κB activation was analysed by immunofluorescence. We observed HMC reduced mechanical hypersensitivity and knee joint oedema, leukocyte recruitment, and pro-inflammatory cytokine levels. We also observed a reduction in zymosan-induced oxidative stress as per increase in total antioxidant capacity and reduction in gp91^phox and increase in HO-1 mRNA expression. Accordingly, total ROS production and macrophage NFκB activation were diminished. HMC interaction with NFκB p65 at Ser276 was revealed using molecular docking analysis. Thus, data presented in this work suggest the usefulness of HMC as an analgesic and anti-inflammatory in a zymosan-induced arthritis model, possibly by targeting NFκB activation in macrophages.

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

6-Methyluracil derivatives as peripheral site ligand-hydroxamic acid conjugates: Reactivation for paraoxon-inhibited acetylcholinesterase

New uncharged conjugates of 6-methyluracil derivatives with imidazole-2-aldoxime and 1,2,4-triazole-3-hydroxamic acid units were synthesized and studied as reactivators of organophosphate-inhibited cholinesterase. Using paraoxon (POX) as a model organophosphate, it was shown that 6-methyluracil derivatives linked with hydroxamic acid are able to reactivate POX-inhibited human acetylcholinesterase (AChE) in vitro. The reactivating efficacy of one compound (5b) is lower than that of pyridinium-2-aldoxime (2-PAM). Meanwhile, unlike 2-PAM, in vivo study showed that the lead compound 5b is able: (1) to reactivate POX-inhibited AChE in the brain; (2) to decrease death of neurons and, (3) to prevent memory impairment in rat model of POX-induced neurodegeneration.

Improving inverse docking target identification with Z-score selection

The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.

GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1)

Quantum mechanical approaches for the massive computation on large biological system such as virtual screening in drug design and development have presented a challenge to computational chemists for many years. In this study, we demonstrated that by taking advantage of rapid growth of GPU-based hardware and software (i.e., teraChem), it is feasible to perform virtual screening of a refined chemical library at quantum mechanical level in order to identify the lead compounds with improved accuracy, especially for the drug targets such as metalloproteins in which significant charge transfer and polarization occur amongst the metal ions and their coordinated amino acids. Our calculations predicted four nature compounds (i.e., Curcumin, Catechin, menthol, and Ferulic acid) as the suitable inhibitors for antibiotics resistance against New Delhi Metallo-β-lactamase-1 (NDM-1). Molecular orbitals (MOs) of the QM region of metal ions and their coordinated residues indicate that the bridged hydroxide ion delocalized the electron over the Zn-OH-Zn group at HOMO, different from MOs when the OH^- is not presented in NDM-1. This indicates that the bridged hydroxide ion plays an important role in the design of antibiotics and other inhibitors targeting the metalloproteins.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Docking-based inverse virtual screening: methods, applications, and challenges

Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.

Structural evidence of quercetin multi-target bioactivity: A reverse virtual screening strategy

The ubiquitous flavonoid quercetin is broadly recognized for showing diverse biological and health-promoting effects, such as anti-cancer, anti-inflammatory and cytoprotective activities. The therapeutic potential of quercetin and similar compounds for preventing such diverse oxidative stress-related pathologies has been generally attributed to their direct antioxidant properties. Nevertheless, accumulated evidence indicates that quercetin is also able to interact with multiple cellular targets influencing the activity of diverse signaling pathways. Even though there are a number of well-established protein targets such as phosphatidylinositol 3 kinase and xanthine oxidase, there remains a lack of a comprehensive knowledge of the potential mechanisms of action of quercetin and its target space. In the present work we adopted a reverse screening strategy based on ligand similarity (SHAFTS) and target structure (idTarget, LIBRA) resulting in a set of predicted protein target candidates. Furthermore, using this method we corroborated a broad array of previously experimentally tested candidates among the predicted targets, supporting the suitability of this screening approach. Notably, all of the predicted target candidates belonged to two main protein families, protein kinases and poly [ADP-ribose] polymerases. They also included key proteins involved at different points within the same signaling pathways or within interconnected signaling pathways, supporting a pleiotropic, multilevel and potentially synergistic mechanism of action of quercetin. In this context we highlight the value of quercetin's broad target profile for its therapeutic potential in diseases like inflammation, neurodegeneration and cancer.

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data

Background

One goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.

Methods

X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann–Whitney U test.

Results

Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68% (p = 4.32x10⁻³). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.

Conclusions

The results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein’s structure determines its function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1533-z) contains supplementary material, which is available to authorized users.

Correcting the impact of docking pose generation error on binding affinity prediction

Background

Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.

Results

Against commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses. In this way, the relationship between Vina-generated protein-ligand poses and their binding affinities is directly learned. As a result, test set performance after this error-correcting procedure is much closer to that of predicting the binding affinity in the absence of pose generation error (i.e. on crystal structures). We evaluated several strategies, obtaining better results for those using a single docked pose per ligand than those using multiple docked poses per ligand.

Conclusions

Binding affinity prediction is often carried out on the docked pose of a known binder rather than its co-crystallised pose. Our results suggest than pose generation error is in general far less damaging for binding affinity prediction than it is currently believed. Another contribution of our study is the proposal of a procedure that largely corrects for this error. The resulting machine-learning scoring function is freely available at http://istar.cse.cuhk.edu.hk/rf-score-4.tgz and http://ballester.marseille.inserm.fr/rf-score-4.tgz.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1169-4) contains supplementary material, which is available to authorized users.

Molecular Docking of Biologically Active Substances to Double Helical Nucleic Acids

Molecular docking of ligands to DNA-targets is of great importance for the design of new anticancer drugs. Unfortunately, most docking programs were developed for protein-ligand docking which raises a question about their applicability for the DNA-ligand docking. In this study, the popular docking programs AutoDock Vina, AutoDock4 and AutoDock3 were compared for a test set of 50 DNA-ligand complexes taken from the Nucleic Acid Database. It was shown that the version 3.05 of the AutoDock program was the most successful in reproducing the structures of intercalation and minor-groove complexes. The program AutoDock4 was able to re-dock to within 2 Å RMSD most of the intercalation complexes of the test set, but showed poor performance for minor groove binders. While Vina, on the contrary, failed to construct six intercalation complexes of the test set, but showed satisfactory results for DNA-ligand minor-groove complexes when small search space was used.

Genetic determinants of antithyroid drug-induced agranulocytosis by human leukocyte antigen genotyping and genome-wide association study

Graves' disease is the leading cause of hyperthyroidism affecting 1.0–1.6% of the population. Antithyroid drugs are the treatment cornerstone, but may cause life-threatening agranulocytosis. Here we conduct a two-stage association study on two separate subject sets (in total 42 agranulocytosis cases and 1,208 Graves' disease controls), using direct human leukocyte antigen genotyping and SNP-based genome-wide association study. We demonstrate HLA-B*38:02 (Armitage trend P_combined=6.75 × 10⁻³²) and HLA-DRB1*08:03 (P_combined=1.83 × 10⁻⁹) as independent susceptibility loci. The genome-wide association study identifies the same signals. Estimated odds ratios for these two loci comparing effective allele carriers to non-carriers are 21.48 (95% confidence interval=11.13–41.48) and 6.13 (95% confidence interval=3.28–11.46), respectively. Carrying both HLA-B*38:02 and HLA-DRB1*08:03 increases odds ratio to 48.41 (P_combined=3.32 × 10⁻²¹, 95% confidence interval=21.66–108.22). Our results could be useful for antithyroid-induced agranulocytosis and potentially for agranulocytosis caused by other chemicals.

Graves' disease is the leading cause of hyperthyroidism but treatment options can cause life-threatening complications. Chen et al. conduct two-stage direct HLA genotyping and genome-wide association studies to identify HLA-B*38:02 and HLA-DRB1*08:03 as major pharmacogenetic determinants.

Prediction of protein targets of kinetin using in silico and in vitro methods: a case study on spinach seed germination mechanism

Kinetin, a cytokinin which promotes seed germination by inhibiting the action of abscisic acid, is an important molecule known to trigger various molecular mechanisms by interacting with an array of proteins shown from experimental observations in various model organisms. We report here the prediction of most probable protein targets of kinetin from spinach proteome using in silico approaches. Inverse docking and ligand-based similarity search was performed using kinetin as molecule. The former method prioritized six spinach proteins, whereas the latter method provided a list of protein targets retrieved from several model organisms. The most probable protein targets were selected by comparing the rank list of docking and ligand similarity methods. Both of these methods prioritized chitinase as the most probable protein target (ΔG pred = 5.064 kcal/mol) supported by the experimental structure of yeast chitinase 1 complex with kinetin (PDB: 2UY5) and Gliocladium roseum chitinase complex with 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione (caffeine; 3G6M) which bears a 3D similarity of 0.43 with kinetin. An in vitro study to evaluate the effect of kinetin on spinach seed germination indicated that a very low concentration of kinetin (0.5 mg/l) did not show a significant effect compared to control in inducing seed germination process. Further, higher levels of kinetin (>0.5 mg/l) constituted an antagonist effect on spinach seed germination. It is anticipated that kinetin may have a molecular interaction with prioritized protein targets synthesized during the seed germination process and reduces growth. Thus, it appears that kinetin may not be a suitable hormone for enhancing spinach seed germination in vitro.

Scoring functions for fragment-based drug discovery

Fragment-based drug design represents a challenge for computational drug design because almost inevitably fragments will be weak binders to the biomolecular targets of a specific disease, and the performances of the scoring functions for weak binders are usually poorer than those for the stronger binders. This protocol describes how to predict the binding modes and binding affinities of fragments towards their binding partner with our refined AutoDock scoring function incorporating a quantum chemical charge model, namely, the restrained electrostatic potential (RESP) model. This scoring function was calibrated by robust regression analysis and has been demonstrated to perform well for general classes of protein-ligand interactions and for weak binders (with root-mean square of error of about 2.1 kcal/mol).

Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets

There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.

Drug-induced conformational population shifts in topoisomerase-DNA ternary complexes

Type II topoisomerases (TOP2) are enzymes that resolve the topological problems during DNA replication and transcription by transiently cleaving both strands and forming a cleavage complex with the DNA. Several prominent anti-cancer agents inhibit TOP2 by stabilizing the cleavage complex and engendering permanent DNA breakage. To discriminate drug binding modes in TOP2-α and TOP2-β, we applied our newly developed scoring function, dubbed AutoDock4^RAP, to evaluate the binding modes of VP-16, m-AMSA, and mitoxantrone to the cleavage complexes. Docking reproduced crystallographic binding mode of VP-16 in a ternary complex of TOP2-β with root-mean-square deviation of 0.65 Å. Molecular dynamics simulation of the complex confirmed the crystallographic binding mode of VP-16 and the conformation of the residue R503. Drug-related conformational changes in R503 have been observed in ternary complexes with m-AMSA and mitoxantrone. However, the R503 rotamers in these two simulations deviate from their crystallographic conformations, indicating a relaxation dynamics from the conformations determined with the drug replacement procedure. The binding mode of VP-16 in the cleavage complex of TOP2-α was determined by the conjoint use of docking and molecular dynamics simulations, which fell within a similar binding pocket of TOP2-β cleavage complex. Our findings may facilitate more efficient design efforts targeting TOP2-α specific drugs.

Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results

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), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.

idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach

Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw.