Structural and dynamic roles of permanent water molecules in ligand molecular recognition by chicken liver bile acid binding protein

Chicken liver bile acid binding protein (cL-BABP) crystallizes with water molecules in its binding site. To obtain insights on the role of internal water, we performed two 100 ns molecular dynamics (MD) simulations in explicit solvent for cL-BABP, as apo form and as a complex with two molecules of cholic acid, and analyzed in detail the dynamics properties of all water molecules. The diffusion coefficients of the more persistent internal water molecules are significantly different from the bulk, but similar between the two protein forms. A different number of molecules and a different organization are observed for apo- and holo-cL-BABP. Most water molecules identified in the binding site of the apo-crystal diffuse to the bulk during the simulation. In contrast, almost all the internal waters of the holo-crystal maintain the same interactions with internal sidechains and ligands, which suggests they have a relevant role in protein-ligand molecular recognition. Only in the presence of these water molecules we were able to reproduce, by a classical molecular docking approach, the structure of the complex cL-BABP::cholic acid with a low ligand root mean square deviation (RMSD) with respect to its reference positioning. Literature data reported a conserved pattern of hydrogen bonds between a single water molecule and three amino acid residues of the binding site in a series of crystallized FABP. In cL-BABP, the interactions between this conserved water molecule and the three residues are present in the crystal of both apo- and holo-cL-BABP but are lost immediately after the start of molecular dynamics. Copyright (c) 2008 John Wiley & Sons, Ltd.     

Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect

Solvation effect is an important factor for protein–ligand binding in aqueous water. Previous scoring function of protein–ligand interactions rarely incorporates the solvation model into the quantification of protein–ligand interactions, mainly due to the immense computational cost, especially in the structure‐based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge‐based atom–pair potentials and the atomic solvation energy of charge‐independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA‐SE). The performance of SPA‐SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA‐SE outperforms all other scoring functions in binding affinity prediction and “native” pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein–ligand binding. The development strategy of SPA‐SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions. Proteins 2015; 83:1632–1642. © 2015 Wiley Periodicals, Inc.     

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.     

Compound prioritization from inverse docking experiment using receptor‐centric and ligand‐centric methods: a case study on Plasmodium falciparum Fab enzymes

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor‐centric and ligand‐centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge‐driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative‐structure activity relationship and multilayer perceptron models. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Intuitive,but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high‐resolution crystallographically characterized “dry” protein–protein complexes and was shown to reliably identify native‐like models. However, most current protein–protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the “truly” bridging waters at the 30 protein–protein interfaces and we utilized them in “solvated” docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ～24% in the average hit‐count within the top‐10 predictions the protein–protein dataset was seen, compared to standard “dry” docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native‐like structure predictions. Proteins 2014; 82:916–932. © 2013 Wiley Periodicals, Inc.     

Structure‐guided screening of chemical database to identify NS3‐NS2B inhibitors for effective therapeutic application in dengue infection

Dengue infection is the most common arthropod‐borne disease caused by dengue viruses, predominantly affecting millions of human beings annually. To find out promising chemical entities for therapeutic application in Dengue, in the current research, a multi‐step virtual screening effort was conceived to screen out the entire “screening library” of the Asinex database. Initially, through “Lipinski rule of five” filtration criterion almost 0.6 million compounds were collected and docked with NS3‐NS2B protein. Thereby, the chemical space was reduced to about 3500 compounds through the analysis of binding affinity obtained from molecular docking study in AutoDock Vina. Further, the “Virtual Screening Workflow” (VSW) utility of Schrödinger suite was used, which follows a stepwise multiple docking programs such as ‐ high‐throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP) docking, and in postprocessing analysis the MM‐GBSA based free binding energy calculation. Finally, five potent molecules were proposed as potential inhibitors for the dengue NS3‐NS2B protein based on the investigation of molecular interactions map and protein‐ligand fingerprint analyses. Different pharmacokinetics and drug‐likeness parameters were also checked, which favour the potentiality of selected molecules for being drug‐like candidates. The molecular dynamics (MD) simulation analyses of protein‐ligand complexes were explained that NS3‐NS2B bound with proposed molecules quite stable in dynamic states as observed from the root means square deviation (RMSD) and root means square fluctuation (RMSF) parameters. The binding free energy was calculated using MM‐GBSA method from the MD simulation trajectories revealed that all proposed molecules possess such a strong binding affinity towards the dengue NS3‐NS2B protein. Therefore, proposed molecules may be potential chemical components for effective inhibition of dengue NS3‐NS2B protein subjected to experimental validation.     

Ligand‐based pharmacophore modelling and screening of DNA minor groove binders targeting Staphylococcus aureus

The recognition of DNA by small molecules is of special importance in the design of new drugs. Many natural and synthetic compounds have the ability to interact with the minor groove of DNA. In the present study, identification of minor groove binding compounds was attained by the combined approach of pharmacophore modelling, virtual screening and molecular dynamics approach. Experimentally reported 32 minor groove binding compounds were used to develop the pharmacophore model. Based on the fitness score, best three pharmacophore hypotheses were selected and used as template for screening the compounds from drug bank database. This pharmacophore‐based screening provides many compounds with the same pharmacological properties. All these compounds were subjected to four phases of docking protocols with combined Glide‐quantum‐polarized ligand docking approach. Molecular dynamics results indicated that selected compounds are more active and showed good interaction in the binding site of DNA. Based on the scoring parameters and energy values, the best compounds were selected, and antibacterial activity of these compounds was identified using in vitro antimicrobial techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

Hydration energy landscape of the active site cavity in cytochrome P450cam

Hydration of protein cavities influences protein stability, dynamics, and function. Protein active sites usually contain water molecules that, upon ligand binding, are either displaced into bulk solvent or retained to mediate protein–ligand interactions. The contribution of water molecules to ligand binding must be accounted for to compute accurate values of binding affinities. This requires estimation of the extent of hydration of the binding site. However, it is often difficult to identify the water molecules involved in the binding process when ligands bind on the surface of a protein. Cytochrome P450cam is, therefore, an ideal model system because its substrate binds in a buried active site, displacing partially disordered solvent, and the protein is well characterized experimentally. We calculated the free energy differences for having five to eight water molecules in the active site cavity of the unliganded enzyme from molecular dynamics simulations by thermodynamic integration employing a three-stage perturbation scheme. The computed free energy differences between the hydration states are small (within 12 kJ mol⁻¹) but distinct. Consistent with the crystallographic determination and studies employing hydrostatic pressure, we calculated that, although ten water molecules could in principle occupy the volume of the active site, occupation by five to six water molecules is thermodynamically most favorable. Proteins 32:381–396, 1998. © 1998 Wiley-Liss, Inc.     

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.     

Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

Ligand solvation in molecular docking

Solvation plays an important role in ligand‐protein association and has a strong impact on comparisons of binding energies for dissimilar molecules. When databases of such molecules are screened for complementarity to receptors of known structure, as often occurs in structure‐based inhibitor discovery, failure to consider ligand solvation often leads to putative ligands that are too highly charged or too large. To correct for the different charge states and sizes of the ligands, we calculated electrostatic and non‐polar solvation free energies for molecules in a widely used molecular database, the Available Chemicals Directory (ACD). A modified Born equation treatment was used to calculate the electrostatic component of ligand solvation. The non‐polar component of ligand solvation was calculated based on the surface area of the ligand and parameters derived from the hydration energies of apolar ligands. These solvation energies were subtracted from the ligand‐receptor interaction energies. We tested the usefulness of these corrections by screening the ACD for molecules that complemented three proteins of known structure, using a molecular docking program. Correcting for ligand solvation improved the rankings of known ligands and discriminated against molecules with inappropriate charge states and sizes. Proteins 1999;34:4–16. © 1999 Wiley‐Liss, Inc.     

Probing molecular docking in a charged model binding site

A model binding site was used to investigate charge-charge interactions in molecular docking. This simple site, a small (180A(3)) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre"ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20microM and 60microM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the "naked" site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.     

DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein–ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein–ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.     

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.     

WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes

We have performed a multivariate logistic regression analysis to establish a statistical correlation between the structural properties of water molecules in the binding site of a free protein crystal structure, with the probability of observing the water molecules in the same location in the crystal structure of the ligand-complexed form. The temperature B-factor, the solvent-contact surface area, the total hydrogen bond energy and the number of protein-water contacts were found to discriminate between bound and displaceable water molecules in the best regression functions obtained. These functions may be used to identify those bound water molecules that should be included in structure-based drug design and ligand docking algorithms.FIGURE The binding site ( thin sticks) of penicillopepsin (3app) with its crystallographically determined water molecules ( spheres) and superimposed ligand (in thick sticks, from complexed structure 1ppk). Water molecules sterically displaced by the ligand upon complexation are shown in cyan. Bound water molecules are shown in blue. Displaced water molecules are shown in yellow. Water molecules removed from the analysis due to a lack of hydrogen bonds to the protein are shown in white. WaterScore correctly predicted waters in blue as Probability=1 to remain bound and waters in yellow as Probability<1x10(-20) to remain bound.     

Molecular simulations of β‐lactoglobulin complexed with fatty acids reveal the structural basis of ligand affinity to internal and possible external binding sites

The interaction of saturated fatty acids of different length (C8:0 to C18:0) with β‐lactoglobulin (βLG) was investigated by molecular dynamics simulation and docking approaches. The results show that the presence of such ligands in the hydrophobic central cavity of βLG, known as the protein calyx, determines an enhancement of atomic fluctuations compared with the unliganded form, especially for loops at the entrance of the binding site. Concerted motions are evidenced for protein regions that could favor the binding of ligands. The mechanism of anchoring of fatty acids of different length is similar for the carboxylate head‐group, through electrostatic interactions with the side chains of Lys60/Lys69. The key protein residues to secure the hydrocarbon chain are Phe105/Met107, which adapt their conformation upon ligand binding. In particular, Phe105 provides an additional hydrophobic clamp only for the tail of the two fatty acids with the longest chains, palmitic, and stearic acid, which are known to bind βLG with a high affinity. The search of additional external binding sites for fatty acids, distinct from the calyx, was also carried out for palmitic acid. Two external sites with a lower affinity were identified as secondary sites, one consisting in a hydrophobic cavity allowing two distinct binding modes for the fatty acid, and the other corresponding to a surface crevice close to the protein α‐helix. The overall results provide a comprehensive picture of the dynamical behavior of βLG in complex with fatty acids, and elucidate the structural basis of the binding of these physiological ligands. Proteins 2014; 82:2609–2619. © 2014 Wiley Periodicals, Inc.     

Predicting binding modes of reversible peptide‐based inhibitors of falcipain‐2 consistent with structure–activity relationships

Falcipain‐2 (FP‐2) is a major hemoglobinase of Plasmodium falciparum, considered an important drug target for the development of antimalarials. A previous study reported a novel series of 20 reversible peptide‐based inhibitors of FP‐2. However, the lack of tridimensional structures of the complexes hinders further optimization strategies to enhance the inhibitory activity of the compounds. Here we report the prediction of the binding modes of the aforementioned inhibitors to FP‐2. A computational approach combining previous knowledge on the determinants of binding to the enzyme, docking, and postdocking refinement steps, is employed. The latter steps comprise molecular dynamics simulations and free energy calculations. Remarkably, this approach leads to the identification of near‐native ligand conformations when applied to a validation set of protein‐ligand structures. Overall, we proposed substrate‐like binding modes of the studied compounds fulfilling the structural requirements for FP‐2 binding and yielding free energy values that correlated well with the experimental data. Proteins 2017; 85:1666–1683. © 2017 Wiley Periodicals, Inc.