Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.     

Gene ontology improves template selection in comparative protein docking

Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains—biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.     

Energy-based graph convolutional networks for scoring protein docking models

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.     

Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines

Biological systems and processes rely on a complex network of molecular interactions. While the association of biological macromolecules is a fundamental biochemical phenomenon crucial for the understanding of complex living systems, protein-protein docking methods aim for the computational prediction of protein complexes from individual subunits. Docking algorithms generally produce large numbers of putative protein complexes with only few of these conformations resembling the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract near-native structure(s) out of the large pool of solutions, the so called scoring or ranking problem. A series of structural, chemical, biological and physical properties are used in this work to classify docked protein-protein complexes. These properties include specialized energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established classifiers are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. Using classification probabilities the ranking of near-native structures was drastically improved, leading to a significant enrichment of near-native complex conformations within the top ranks. It could be shown that the developed schemes outperform five other previously published scoring functions.     

Targeting imidazoline site on monoamine oxidase B through molecular docking simulations

Monoamine oxidase (MAO) is an enzyme of major importance in neurochemistry, because it catalyzes the inactivation pathway for the catecholamine neurotransmitters, noradrenaline, adrenaline and dopamine. In the last decade it was demonstrated that imidazoline derivatives were able to inhibit MAO activity. Furthermore, crystallographic studies identified the imidazoline-binding domain on monoamine oxidase B (MAO-B), which opens the possibility of molecular docking studies focused on this binding site. The goal of the present study is to identify new potential inhibitors for MAO-B. In addition, we are also interested in establishing a fast and reliable computation methodology to pave the way for future molecular docking simulations focused on the imidazoline-binding site of this enzyme. We used the program 'molegro virtual docker' (MVD) in all simulations described here. All results indicate that simplex evolution algorithm is able to succesfully simulate the protein-ligand interactions for MAO-B. In addition, a scoring function implemented in the program MVD presents high correlation coefficient with experimental activity of MAO-B inhibitors. Taken together, our results identified a new family of potential MAO-B inhibitors and mapped important residues for intermolecular interactions between this enzyme and ligands.     

Investigation of glucose binding sites on insulin

Possible insulin binding sites for D-glucose have been investigated theoretically by docking and molecular dynamics (MD) simulations. Two different docking programs for small molecules were used; Multiple Copy Simultaneous Search (MCSS) and Solvation Energy for Exhaustive Docking (SEED) programs. The configurations resulting from the MCSS search were evaluated with a scoring function developed to estimate the binding free energy. SEED calculations were performed using various values for the dielectric constant of the solute. It is found that scores emphasizing non-polar interactions gave a preferential binding site in agreement with that inferred from recent fluorescence and NMR NOESY experiments. The calculated binding affinity of -1.4 to -3.5 kcal/mol is within the measured range of -2.0 +/- 0.5 kcal/mol. The validity of the binding site is suggested by the dynamical stability of the bound glucose when examined with MD simulations with explicit solvent. Alternative binding sites were found in the simulations and their relative stabilities were estimated. The motions of the bound glucose during molecular dynamics simulations are correlated with the motions of the insulin side chains that are in contact with it and with larger scale insulin motions. These results raise the question of whether glucose binding to insulin could play a role in its activity. The results establish the complementarity of molecular dynamics simulations and normal mode analyses with the search for binding sites proposed with small molecule docking programs.     

SOFTDOCK application to protein-protein interaction benchmark and CAPRI

The success of molecular docking requires cooperation of sampling and scoring of various conformations. The SOFTDOCK package uses a coarse-grained docking method to sample all possible conformations of complexes. SOFTDOCK uses a new Voronoi molecular surface and calculates several grid-based scores. It is shown by the leave-one-out test that three geometry scores and an FTDOCK-like electrostatics score contribute the most to the discrimination of near-native conformations. However, an atom-based solvation score is shown to be ineffective. It is also found that an increased Voronoi surface thickness greatly increases the accuracy of docking results. Finally, the clustering procedure is shown to improve the overall ranking, but leads to less accurate docking results. The application of SOFTDOCK in Critical Assessment of PRedicted Interactions involves four steps: (i) sampling with INTELEF; (ii) clustering; (iii) AMBER energy minimization; and (iv) manual inspection. Biological information from literature is used as filters in some of the sampling and manual inspection according to different targets. Two of our submissions have L_rmsd around 10 A. Although they are not classified as acceptable solutions, they are considered successful because they are comparable to the accuracy of our method. Availability: SOFTDOCK is open source code and can be downloaded at http://bio.iphy.ac.cn     

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

Halogen bonding, a non-covalent interaction between the halogen σ-hole and Lewis bases, could not be properly characterized by majority of current scoring functions. In this study, a knowledge-based halogen bonding scoring function, termed XBPMF, was developed by an iterative method for predicting protein-ligand interactions. Three sets of pairwise potentials were derived from two training sets of protein-ligand complexes from the Protein Data Bank. It was found that two-dimensional pairwise potentials could characterize appropriately the distance and angle profiles of halogen bonding, which is superior to one-dimensional pairwise potentials. With comparison to six widely used scoring functions, XBPMF was evaluated to have moderate power for predicting protein-ligand interactions in terms of “docking power”, “ranking power” and “scoring power”. Especially, it has a rather satisfactory performance for the systems with typical halogen bonds. To the best of our knowledge, XBPMF is the first halogen bonding scoring function that is not dependent on any dummy atom, and is practical for high-throughput virtual screening. Therefore, this scoring function should be useful for the study and application of halogen bonding interactions like molecular docking and lead optimization.

Structure prediction of protein complexes by an NMR-based protein docking algorithm

Protein docking algorithms can be used to study the driving forces and reaction mechanisms of docking processes. They are also able to speed up the lengthy process of experimental structure elucidation of protein complexes by proposing potential structures. In this paper, we are discussing a variant of the protein-protein docking problem, where the input consists of the tertiary structures of proteins A and B plus an unassigned one-dimensional ¹H-NMR spectrum of the complex AB. We present a new scoring function for evaluating and ranking potential complex structures produced by a docking algorithm. The scoring function computes a `theoretical' ¹H-NMR spectrum for each tentative complex structure and subtracts the calculated spectrum from the experimental one. The absolute areas of the difference spectra are then used to rank the potential complex structures. In contrast to formerly published approaches (e.g. [Morelli et al. (2000) Biochemistry, 39, 2530–2537]) we do not use distance constraints (intermolecular NOE constraints). We have tested the approach with four protein complexes whose three-dimensional structures are stored in the PDB data bank [Bernstein et al. (1977)] and whose ¹H-NMR shift assignments are available from the BMRB database. The best result was obtained for an example, where all standard scoring functions failed completely. Here, our new scoring function achieved an almost perfect separation between good approximations of the true complex structure and false positives.     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

A collaborative filtering approach for protein-protein docking scoring functions

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.     

Identification of near-native structures by clustering protein docking conformations

Most state-of-the-art protein-protein docking algorithms use the Fast Fourier Transform (FFT) technique to sample the six-dimensional translational and rotational space. Scoring functions including shape complementarity, electrostatics, and desolvation are usually exploited in ranking the docking conformations. While these rigid-body docking methods provide good performance in bound docking, using unbound structures as input frequently leads to a high number of false positive hits. For the purpose of better selecting correct docking conformations, we structurally cluster the docking decoys generated by four widely-used FFT-based protein-protein docking methods. In all cases, the selection based on cluster size outperforms the ranking based on the inherent scoring function. If we cluster decoys from different servers together, only marginal improvement is obtained in comparison with clustering decoys from the best individual server. A collection of multiple decoy sets of comparable quality will be the key to improve the clustering result from meta-docking servers.     

Flexible relaxation of rigid-body docking solutions

Molecular Dynamics (MD) simulations have been performed on a set of rigid-body docking poses, carried out over 25 protein-protein complexes. The results show that fully flexible relaxation increases the fraction of native contacts (NC) by up to 70% for certain docking poses. The largest increase in the fraction of NC is observed for docking poses where anchor residues are able to sample their bound conformation. For each MD simulation, structural snap-shots were clustered and the centre of each cluster used as the MD-relaxed docking pose. A comparison between two energy-based scoring schemes, the first calculated for the MD-relaxed poses, the second for energy minimized poses, shows that the former are better in ranking complexes with large hydrophobic interfaces. Furthermore, complexes with large interfaces are generally ranked well, regardless of the type of relaxation method chosen, whereas complexes with small hydrophobic interfaces remain difficult to rank. In general, the results indicate that current force-fields are able to correctly describe direct intermolecular interactions between receptor and ligand molecules. However, these force-fields still fail in cases where protein-protein complexes are stabilized by subtle energy contributions.     

Structural studies of human purine nucleoside phosphorylase: towards a new specific empirical scoring function

Human purine nucleoside phosphorylase (HsPNP) is a target for inhibitor development aiming at T-cell immune response modulation. In this work, we report the development of a new set of empirical scoring functions and its application to evaluate binding affinities and docking results. To test these new functions, we solved the structure of HsPNP and 2-mercapto-4(3H)-quinazolinone (HsPNP:MQU) binary complex at 2.7 Å resolution using synchrotron radiation, and used these functions to predict ligand position obtained in docking simulations. We also employed molecular dynamics simulations to analyze HsPNP in two conditions, as apoenzyme and in the binary complex form, in order to assess the structural features responsible for stability. Analysis of the structural differences between systems provides explanation for inhibitor binding. The use of these scoring functions to evaluate binding affinities and molecular docking results may be used to guide future efforts on virtual screening focused on HsPNP.     

Viper venom hyaluronidase and its potential inhibitor analysis: a multipronged computational investigation

Viper venom hyaluronidase (VV-HYA) inhibitors have long been used as therapeutic agents for arresting the local and systemic effects caused during its envenomation. Henceforth, to understand its structural features and also to identify the best potential inhibitor against it the present computational study was undertaken. Structure-based homology modeling of VV-HYA followed by its docking and free energy-based ranking analysis of ligand, the MD simulations of the lead complex was also performed. The sequence analysis and homology modeling of VV-HYA revealed a distorted (β/α)₈ folding as in the case of hydrolases family of proteins. Molecular docking of the resultant 3D structure of VV-HYA with known inhibitors (compounds 1–25) revealed the importance of molecular recognition of hotspot residues (Tyr 75, Arg 288, and Trp 321) other than that of the active site residues. It also revealed that Trp 321 of VV-HYA is highly important for mediating π–π interactions with ligands. In addition, the molecular docking and comparative free energy binding analysis was investigated for the VV-HYA inhibitors (compounds 1–25). Both molecular docking and relative free energy binding analysis clearly confirmed the identification of sodium chromoglycate (compound 1) as the best potential inhibitor against VV-HYA. Molecular dynamics simulations additionally confirmed the stability of their binding interactions. Further, the information obtained from this work is believed to serve as an impetus for future rational designing of new novel VV-HYA inhibitors with improved activity and selectivity.     

Progress in molecular docking

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.     

In silico binding free energy predictability with π-π interaction energy-augmented scoring function: benzimidazole Raf inhibitors as a case study

The ability to estimate binding affinities of ligands precisely is of paramount importance in designing drugs. Docking programs are used primarily to predict the binding mode of ligands to receptors. However, current scoring functions as used in docking programs are not reliable enough to predict binding affinities of ligands without any further calculations. In the present study, we investigate the usefulness of adding π-π interaction energies between ring groups of residues and ligands to the scoring function for docking. It is found that such addition helps ranking ligand activities more correctly. LMP2 calculation is used to measure π-π interaction energies between ring groups. The result of this simple addition shows possibility of π-π interaction generalization in scoring functions.     

Principles of docking: An overview of search algorithms and a guide to scoring functions

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.     

Efficacy and selectivity in flexible database docking

Flexible database docking with DOCK 4.0 has been evaluated for its ability to retrieve biologically active molecules from a database of approximately 1,000 compounds with known activities against thrombin and the progesterone receptor. The retrieval of known actives and chemically similar but inactive molecules was monitored as a function of conformational and orientational sampling. The largest enrichment of actives among the 10% highest ranking molecules is obtained when only five conformations are used to seed the next round of ligand reconstruction and limited sampling is applied to place the base fragment in the binding site. The performance of energy and chemical scoring, as implemented in DOCK 4.0, was found to depend on the protein used for docking. For the progesterone receptor, energy scoring yields the largest enrichments (64%) in terms of actives retrieved among the 10% top scoring molecules, while chemical scoring performs best for thrombin (94%). With the exception of the application of energy scoring to the progesterone receptor, both energy-based scoring schemes applied in this study do not discriminate well between true actives and chemically similar but inactive compounds. In conclusion, flexible docking is able to effectively prioritize high-throughput screening databases, using less conformational sampling than normally required for appropriate reconstruction of protein-ligand complexes. The more subtle discrimination between chemically similar classes of active and inactive compounds remains, however, problematic.