Automated docking of ligands to antibodies: methods and applications

Many approaches to studying protein-ligand interactions by computational docking are currently available. Given the structures of a protein and a ligand, the ultimate goal of all docking methods is to predict the structure of the resulting complex. This requires a suitable representation of molecular structures and properties, search algorithms to efficiently scan the configuration space for favorable interaction geometries, and accurate scoring functions to evaluate and rank the generated orientations. For many of the available methods, tests on experimentally known antibody-antigen or antibody-hapten complexes have appeared in the literature. In addition, some of them have been used in predictive studies on antibody-ligand interactions to provide structural insights where adequate experimental information is missing. The AutoDock program is presented as example of a method for flexibly docking ligands to antibodies. Applying parameters of the second-generation AMBER force field, three antibody-hapten complexes (AN02, DB3, NC6.8) are used as new test cases to analyze the ability of the method to reproduce experimental findings. The X-ray structures could be reconstituted and the corresponding solutions were ranked with best energy score in all cases. Docking to the free instead of the complexed NC6.8 structure indicated the limits of the rigid protein treatment, although fairly good guesses about the location of the binding site and the contact residues could still be obtained if conformational flexibility was allowed at least in the ligand.     

Efficient docking of peptides to proteins without prior knowledge of the binding site

Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

ProPose: a docking engine based on a fully configurable protein–ligand interaction model

Virtual high-throughput screening of molecular databases and in particular high-throughput protein–ligand docking are both common methodologies that identify and enrich hits in the early stages of the drug design process. Current protein–ligand docking algorithms often implement a program-specific model for protein–ligand interaction geometries. However, in order to create a platform for arbitrary queries in molecular databases, a new program is desirable that allows more manual control of the modeling of molecular interactions.For that reason, ProPose, an advanced incremental construction docking engine, is presented here that implements a fast and fully configurable molecular interaction and scoring model. This program uses user-defined, discrete, pharmacophore-like representations of molecular interactions that are transformed on-the-fly into a continuous potential energy surface, allowing for the incorporation of target specific interaction mechanisms into docking protocols in a straightforward manner. A torsion angle library, based on semi-empirical quantum chemistry calculations, is used to provide minimum energy torsion angles for the incremental construction algorithm. Docking results of a diverse set of protein–ligand complexes from the Protein Data Bank demonstrate the feasibility of this new approach.As a result, the seamless integration of pharmacophore-like interaction types into the docking and scoring scheme implemented in ProPose opens new opportunities for efficient, receptor-specific screening protocols. Figure ProPose — a fully configurable protein-ligand docking program — transforms pharmacophores into a smooth potential energy surface.This revised version was published online in October 2004 with corrections to the Graphical Abstract.     

Molecular insights into the binding selectivity of a synthetic ligand DAAG to Fc fragment of IgG

Affinity chromatography with synthetic ligands has been focused as the potential alternative to protein A‐based chromatography for antibody capture because of its comparable selectivity and efficiency. Better understanding on the molecular interactions between synthetic ligand and antibody is crucial for improving and designing novel ligands. In this work, the molecular interaction mechanism between Fc fragment of IgG and a synthetic ligand (DAAG) was studied with molecular docking and dynamics simulation. The docking results on the consensus binding site (CBS) indicated that DAAG could bind to the CBS with the favorable orientation like a tripod for the top‐ranked binding complexes. The ligand‐Fc fragment complexes were then tested by molecular dynamics simulation at neutral condition (pH 7.0) for 10 ns. The results indicated that the binding of DAAG on the CBS of Fc fragment was achieved by the multimodal interactions, combining the hydrophobic interaction, electrostatic interaction, hydrogen bond, and so on. It was also found that multiple secondary interactions endowed DAAG with an excellent selectivity to Fc fragment. In addition, molecular dynamics simulation conducted at acidic condition (pH 3.0) showed that the departure of DAAG ligand from the surface of Fc fragment was the result of reduced interaction energies. The binding modes between DAAG and CBS not only shed light on the molecular mechanisms of DAAG for antibody purification but also provide useful information for the improvement of ligand design. Copyright © 2014 John Wiley & Sons, Ltd.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

An application of machine learning methods to structural interaction fingerprints—a case study of kinase inhibitors

In this Letter, we present a novel methodology of searching for biologically active compounds, which is based on the combination of docking experiments and analysis of the results by machine learning methods. The study was performed for 5 different protein kinases, and several sets of compounds (active, inactive and assumed inactives) were docked into their targets. The resulting ligand–protein complexes were represented by the means of structural interaction fingerprints profiles (SIFts profiles) that constituted an input for ML methods. The developed protocol was found to be superior to the combination of classification algorithms with the standard fingerprint MACCSFP.     

Computational Insights into the Inhibitory Mechanism of Human AKT1 by an Orally Active Inhibitor,MK-2206

The AKT signaling pathway has been identified as an important target for cancer therapy. Among small-molecule inhibitors of AKT that have shown tremendous potential in inhibiting cancer, MK-2206 is a highly potent, selective and orally active allosteric inhibitor. Promising preclinical anticancer results have led to entry of MK-2206 into Phase I/II clinical trials. Despite such importance, the exact binding mechanism and the molecular interactions of MK-2206 with human AKT are not available. The current study investigated the exact binding mode and the molecular interactions of MK-2206 with human AKT isoforms using molecular docking and (un)binding simulation analyses. The study also involved the docking analyses of the structural analogs of MK-2206 to AKT1 and proposed one as better inhibitor. The Dock was used for docking simulations of MK-2206 into the allosteric site of AKT isoforms. The Ligplot+ was used for analyses of polar and hydrophobic interactions between AKT isoforms and the ligands. The MoMa-LigPath web server was used to simulate the ligand (un)binding from the binding site to the surface of the protein. In the docking and (un)binding simulation analyses of MK-2206 with human AKT1, the Trp-80 was the key residue and showed highest decrease in the solvent accessibility, highest number of hydrophobic interactions, and the most consistent involvement in all (un)binding simulation phases. The number of molecular interactions identified and calculated binding energies and dissociation constants from the co-complex structures of these isoforms, clearly explained the varying affinity of MK-2206 towards these isoforms. The (un)binding simulation analyses identified various additional residues which despite being away from the binding site, play important role in initial binding of the ligand. Thus, the docking and (un)binding simulation analyses of MK-2206 with AKT isoforms and its structure analogs will provide a suitable model for studying drug-protein interaction and will help in designing better drugs.     

Comparative docking studies of CYP1b1 and its PCG-associated mutant forms

Molecular docking has been used to compare and contrast the binding modes of oestradiol with the wild-type and some disease-associated mutant forms of the human CYP1b1 protein. The receptor structures used for docking were derived from molecular dynamics simulations of homology-modelled structures. Earlier studies involving molecular dynamics and principal component analysis indicated that mutations could have a disruptive effect on function, by destabilizing the native properties of the functionally important regions, especially those of the haem-binding and substrate-binding regions, which constitute the site of catalytic activity of the enzyme. In order to gain more insights into the possible differences in substrate-binding and catalysis between the wild-type and mutant proteins, molecular docking studies were carried out. Mutants showed altered protein-ligand interactions compared with the wild-type as a consequence of changes in the geometry of the substrate-binding region and in the position of haem relative to the active site. An important difference in ligand-protein interactions between the wild-type and mutants is the presence of stacking interaction with phenyl residues in the wild-type, which is either completely absent or considerably weaker in mutants. The present study revealed essential differences in the interactions between ligand and protein in wild-type and disease mutants, and helped in understanding the deleterious nature of disease mutations at the level of molecular function.     

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.     

Docking multiple conformations of a flexible ligand into a protein binding site using NMR restraints

A method is described for docking a large, flexible ligand using intra-ligand conformational restraints from exchange-transferred NOE (etNOE) data. Numerous conformations of the ligand are generated in isolation, and a subset of representative conformations is selected. A crude model of the protein-ligand complex is used as a template for overlaying the selected ligand structures, and each complex is conformationally relaxed by molecular mechanics to optimize the interaction. Finally, the complexes were assessed for structural quality. Alternative approaches are described for the three steps of the method: generation of the initial docking template; selection of a subset of ligand conformations; and conformational sampling of the complex. The template is generated either by manual docking using interactive graphics or by a computational grid-based search of the binding site. A subset of conformations from the total number of peptides calculated in isolation is selected based on either low energy and satisfaction of the etNOE restraints, or a cluster analysis of the full set. To optimize the interactions in the complex, either a restrained Monte Carlo-energy minimization (MCM) protocol or a restrained simulated annealing (SA) protocol were used. This work produced 53 initial complexes of which 8 were assessed in detail. With the etNOE conformational restraints, all of the approaches provide reasonable models. The grid-based approach to generate an initial docking template allows a large volume to be sampled, and as a result, two distinct binding modes were identified for a fifteen-residue peptide binding to an enzyme active site.     

A new test set for validating predictions of protein-ligand interaction

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk).     

A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity

With the rapid development of structural determination of target proteins for human diseases, high throughout virtual screening based drug discovery is gaining popularity gradually. In this paper, a fast docking algorithm (H-DOCK) based on hydrogen bond matching and surface shape complementarity was developed. In H-DOCK, firstly a divide-and-conquer strategy based enumeration approach is applied to rank the intermolecular modes between protein and ligand by maximizing their hydrogen bonds matching, then each docked conformation of the ligand is calculated according to the matched hydrogen bonding geometry, finally a simple but effective scoring function reflecting mainly the van der Waals interaction is used to evaluate the docked conformations of the ligand. H-DOCK is tested for rigid ligand docking and flexible one, the latter is implemented by repeating rigid docking for multiple conformations of a small molecule and ranking all together. For rigid ligands, H-DOCK was tested on a set of 271 complexes where there is at least one intermolecular hydrogen bond, and H-DOCK achieved success rate (RMSD<2.0?Å) of 91.1%. For flexible ligands, H-DOCK was tested on another set of 93 complexes, where each case was a conformation ensemble containing native ligand conformation as well as 100 decoy ones generated by AutoDock [1], and the success rate reached 81.7%. The high success rate of H-DOCK indicates that the hydrogen bonding and steric hindrance can grasp the key interaction between protein and ligand. H-DOCK is quite efficient compared with the conventional docking algorithms, and it takes only about 0.14 seconds for a rigid ligand docking and about 8.25 seconds for a flexible one on average. According to the preliminary docking results, it implies that H-DOCK can be potentially used for large scale virtual screening as a pre-filter for a more accurate but less efficient docking algorithm.     

Towards Ligand Docking Including Explicit Interface Water Molecules

Small molecule docking predicts the interaction of a small molecule ligand with a protein at atomic-detail accuracy including position and conformation the ligand but also conformational changes of the protein upon ligand binding. While successful in the majority of cases, docking algorithms including RosettaLigand fail in some cases to predict the correct protein/ligand complex structure. In this study we show that simultaneous docking of explicit interface water molecules greatly improves Rosetta’s ability to distinguish correct from incorrect ligand poses. This result holds true for both protein-centric water docking wherein waters are located relative to the protein binding site and ligand-centric water docking wherein waters move with the ligand during docking. Protein-centric docking is used to model 99 HIV-1 protease/protease inhibitor structures. We find protease inhibitor placement improving at a ratio of 9∶1 when one critical interface water molecule is included in the docking simulation. Ligand-centric docking is applied to 341 structures from the CSAR benchmark of diverse protein/ligand complexes [1]. Across this diverse dataset we see up to 56% recovery of failed docking studies, when waters are included in the docking simulation.     

Nucleotide docking: prediction of reactant state complexes for ribonuclease enzymes

Ribonuclease enzymes (RNases) play key roles in the maturation and metabolism of all RNA molecules. Computational simulations of the processes involved can help to elucidate the underlying enzymatic mechanism and is often employed in a synergistic approach together with biochemical experiments. Theoretical calculations require atomistic details regarding the starting geometries of the molecules involved, which, in the absence of crystallographic data, can only be achieved from computational docking studies. Fortunately, docking algorithms have improved tremendously in recent years, so that reliable structures of enzyme–ligand complexes can now be successfully obtained from computation. However, most docking programs are not particularly optimized for nucleotide docking. In order to assist our studies on the cleavage of RNA by the two most important ribonuclease enzymes, RNase A and RNase H, we evaluated four docking tools—MOE2009, Glide 5.5, QXP-Flo+0802, and Autodock 4.0—for their ability to simulate complexes between these enzymes and RNA oligomers. To validate our results, we analyzed the docking results with respect to the known key interactions between the protein and the nucleotide. In addition, we compared the predicted complexes with X-ray structures of the mutated enzyme as well as with structures obtained from previous calculations. In this manner, we were able to prepare the desired reaction state complex so that it could be used as the starting structure for further DFT/B3LYP QM/MM reaction mechanism studies.     

Analysis of binding modes of ligands to multiple conformations of CYP3A4

Cytochromes P450 (CYPs) are extremely versatile enzymes capable of catalyzing a vast number of compounds, and CYP3A4 is no exception metabolizing approximately half of the currently marketed drugs, besides endogenous compounds. To metabolize such a variety of compounds, CYP3A4 has to be extremely flexible, which makes interaction studies difficult. We employ a multi-conformational docking setup where conformations are generated by several molecular dynamics simulations to analyze the binding modes of various ligands, and the docking is considered successful if the ligand site of catalysis (SOC) is within 6.0 Å of the haem Fe. While docking with the X-ray structure proved unsuccessful with all ligands, the multi-conformational docking achieved successful binding of each ligand to at least one protein conformation. Analysis of the docked solutions highlights residues in the active site cavity that may have an important role in access, binding and stabilization of the ligand.     

Inhibition mechanism of CDK-2 and GSK-3β by a sulfamoylphenyl derivative of indoline—a molecular dynamics study

A good understanding of the inhibition mechanism of enzymes exhibiting high levels of similarity is the first step to the discovery of new drugs with selective potential. Examples of such proteins include glycogen synthase kinase-3 (GSK-3β) and cyclin-dependent kinase 2 (CDK-2). This article reports the mechanism of such enzyme inhibition as analyzed by an indoline sulfamylophenyl derivative (CHEMBL410072). Previous work has shown that such compounds exhibit selective properties towards their biological targets. This study used a combined procedure involving docking and molecular dynamics simulations, which allowed identification of interactions responsible for stabilization of complexes, and analysis of the dynamic stability of the systems obtained. The initial data obtained during the molecular docking stage show that the ligand molecule exhibits a similar affinity towards both active sites, which was confirmed by quantification of identified interactions and energy values. However, the data do not cover dynamic aspects of the considered systems. Molecular dynamics simulations realized for both complexes indicate significant dissimilarities in dynamics properties of both side chains of the considered ligands, especially in the case of the part containing the sulfamide group. Such increased mobility of the analyzed systems disrupts the stability of binding in the stabilized complex with GSK-3β protein, which finally affects also the binding affinity of the ligand molecule towards this enzyme.     

Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: a computational approach towards targeting protein-protein interaction as a futuristic therapeutic intervention strategy for breast cancer impediment

Protein–protein interactions (PPI) are a new emerging class of novel therapeutic targets. In order to probe these interactions, computational tools provide a convenient and quick method towards the development of therapeutics. Keeping this in view the present study was initiated to analyse interaction of tumour suppressor protein p53 (TP53) and breast cancer associated protein (BRCA1) as promising target against breast cancer. Using computational approaches such as protein–protein docking, hot spot analyses, molecular docking and molecular dynamics simulation (MDS), stepwise analyses of the interactions of the wild type and mutant TP53 with that of wild type BRCA1 and their modulation by alkaloids were done. Protein–protein docking method was used to generate both wild type and mutant complexes of TP53-BRCA1. Subsequently, the complexes were docked using sixteen different alkaloids, fulfilling ADMET and Lipinski’s rule of five criteria, and were compared with that of a well-known inhibitor of PPI, namely nutlin. The alkaloid dicentrine was found to be the best docked alkaloid among all the docked alklaloids as well as that of nutlin. Furthermore, MDS analyses of both wild type and mutant complexes with the best docked alkaloid i.e. dicentrine, revealed higher stability of mutant complex than that of the wild one, in terms of average RMSD, RMSF and binding free energy, corroborating the results of docking. Results suggested more pronounced interaction of BRCA1 with mutant TP53 leading to increased expression of mutated TP53 thus showing a dominant negative gain of function and hampering wild type TP53 function leading to tumour progression.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.