CYP 2D6 Binding Affinity Predictions Using Multiple Ligand and Protein Conformations

Because of the large flexibility and malleability of Cytochrome P450 enzymes (CYPs), in silico prediction of CYP binding affinities to drugs and other xenobiotic compounds is a true challenge. In the current work, we use an iterative linear interaction energy (LIE) approach to compute CYP binding affinities from molecular dynamics (MD) simulation. In order to improve sampling of conformational space, we combine results from simulations starting with different relevant protein-ligand geometries. For calculated binding free energies of a set of thiourea compounds binding to the flexible CYP 2D6 isoform, improved correlation with experiment was obtained by combining results ofMDruns starting from distinct protein conformations and ligand-binding orientations. This accuracy was obtained from relatively short MD simulations, which makes our approach computationally attractive for automated calculations of ligand-binding affinities to flexible proteins such as CYPs.     

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical ‘Geometry, Frequency, Noncovalent, eXtended Tight Binding’ (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent ‘Statistical Assessment of the Modeling of Proteins and Ligands’ (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.     

Exploring Large Domain Motions in Proteins Using Atomistic Molecular Dynamics with Enhanced Conformational Sampling

Conformational transitions in multidomain proteins are essential for biological functions. The Apo conformations are typically open and flexible, while the Holo states form more compact conformations stabilized by protein-ligand interactions. Unfortunately, the atomically detailed mechanisms for such open-closed conformational changes are difficult to be accessed experimentally as well as computationally. To simulate the transitions using atomistic molecular dynamics (MD) simulations, efficient conformational sampling algorithms are required. In this work, we propose a new approach based on generalized replica-exchange with solute tempering (gREST) for exploring the open-closed conformational changes in multidomain proteins. Wherein, selected surface charged residues in a target protein are defined as the solute region in gREST simulation and the solute temperatures are different in replicas and exchanged between them to enhance the domain motions. This approach is called gREST selected surface charged residues (gREST_SSCR) and is applied to the Apo and Holo states of ribose binding protein (RBP) in solution. The conformational spaces sampled with gREST_SSCR are much wider than those with the conventional MD, sampling open-closed conformational changes while maintaining RBP domains’ stability. The free-energy landscapes of RBP in the Apo and Holo states are drawn along with twist and hinge angles of the two moving domains. The inter-domain salt-bridges that are not observed in the experimental structures are also important in the intermediate states during the conformational changes.     

Prediction of protein loop structures using a local move Monte Carlo approach and a grid-based force field

We have developed an improved local move Monte Carlo (LMMC) loop sampling approach for loop predictions. The method generates loop conformations based on simple moves of the torsion angles of side chains and local moves of backbone of loops. To reduce the computational costs for energy evaluations, we developed a grid-based force field to represent the protein environment and solvation effect. Simulated annealing has been used to enhance the efficiency of the LMMC loop sampling and identify low-energy loop conformations. The prediction quality is evaluated on a set of protein loops with known crystal structure that has been previously used by others to test different loop prediction methods. The results show that this approach can reproduce the experimental results with the root mean square deviation within 1.8 A for all the test cases. The LMMC loop prediction approach developed here could be useful for improvement in the quality the loop regions in homology models, flexible protein-ligand and protein-protein docking studies.     

NMR Data-Driven Docking of HDM2-Inhibitor Complexes

We present an automated NMR-guided docking workflow that can be used to generate models of protein-ligand complexes based on data from NOE NMR experiments. The first step is to generate a number of intermolecular distance constraints from experimental NOE data. Then, the ligand is docked on an ensemble of receptor structures to account for protein flexibility, and multiple poses are generated. Finally, we use the NOE-based constraints to filter and score docking poses based on the percentage of NOE constraints that are consistent with protein-ligand interatomic distances. This workflow was successfully used during a lead optimization project to generate models of synthetic protein-protein interaction (PPI) inhibitors bound to the HDM2 protein.     

Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations

Computational methods, namely molecular dynamics (MD) simulations in combination with inhomogeneous fluid solvation theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein–ligand interaction energy, ligand solvation free energies, and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein–ligand interaction energies and entropies. In two of the seven cases, this estimation led to large errors, implying that accurate predictions of relative binding free energies based on solvent thermodynamics is challenging. Nevertheless, MD simulations can provide insight regarding which water molecules can be targeted for displacement.     

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.     

Ligand binding affinities from MD simulations

Simplified free energy calculations based on force field energy estimates of ligand-receptor interactions and thermal conformational sampling have emerged as a useful tool in structure-based ligand design. Here we give an overview of the linear interaction energy (LIE) method for calculating ligand binding free energies from molecular dynamics simulations. A notable feature is that the binding energetics can be predicted by considering only the intermolecular interactions of the ligand in the associated and dissociated states. The approximations behind this approach are examined, and different parametrizations of the model are discussed. LIE-type methods appear particularly promising for computational "lead optimization". Recent applications to protein-protein interactions and ion channel blocking are also discussed.     

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.     

Computational design of an endo-1,4-beta-xylanase ligand binding site

The field of computational protein design has experienced important recent success. However, the de novo computational design of high-affinity protein-ligand interfaces is still largely an open challenge. Using the Rosetta program, we attempted the in silico design of a high-affinity protein interface to a small peptide ligand. We chose the thermophilic endo-1,4-β-xylanase from Nonomuraea flexuosa as the protein scaffold on which to perform our designs. Over the course of the study, 12 proteins derived from this scaffold were produced and assayed for binding to the target ligand. Unfortunately, none of the designed proteins displayed evidence of high-affinity binding. Structural characterization of four designed proteins revealed that although the predicted structure of the protein model was highly accurate, this structural accuracy did not translate into accurate prediction of binding affinity. Crystallographic analyses indicate that the lack of binding affinity is possibly due to unaccounted for protein dynamics in the 'thumb' region of our design scaffold intrinsic to the family 11 β-xylanase fold. Further computational analysis revealed two specific, single amino acid substitutions responsible for an observed change in backbone conformation, and decreased dynamic stability of the catalytic cleft. These findings offer new insight into the dynamic and structural determinants of the β-xylanase proteins.     

Structural Transformation Detection Contributes to Screening of Behaviorally Active Compounds: Dynamic Binding Process Analysis of DhelOBP21 from <Emphasis Type="Italic">Dastarcus helophoroides</Emphasis>

In light of reverse chemical ecology, the fluorescence competitive binding assays of functional odorant binding proteins (OBPs) is a recent advanced approach for screening behaviorally active compounds of insects. Previous research on Dastareus helophoroides identified a minus-C OBP, DhelOBP21, which preferably binds to several ligands. In this study, only (+)-β-pinene proved attractive to unmated adult beetles. To obtain a more in-depth explanation of the lack of behavioral activity of other ligands we selected compounds with high (camphor) and low (β-caryophyllene) binding affinities. The structural transformation of OBPs was investigated using well-established approaches for studying binding processes, such as fluorescent quenching assays, circular dichroism, and molecular dynamics. The dynamic binding process revealed that the flexibility of DhelOBP21 seems conducive to binding specific ligands, as opposed to broad substrate binding. The compound (+)-β-pinene and DhelOBP21 formed a stable complex through a secondary structural transformation of DhelOBP21, in which its amino-terminus transformed from random coil to an α-helix to cover the binding pocket. On the other hand, camphor could not efficiently induce a stable structural transformation, and its high binding affinities were due to strong hydrogen-bonding, compromising the structure of the protein. The other compound, β-caryophyllene, only collided with DhelOBP21 and could not be positioned in the binding pocket. Studying structural transformation of these proteins through examining the dynamic binding process rather than using approaches that just measure binding affinities such as fluorescence competitive binding assays can provide a more efficient and reliable approach for screening behaviorally active compounds.     

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.     

An experimental and molecular-modeling study of the binding of linked sulfated tetracyclitols to FGF-1 and FGF-2

The experimental binding affinities of a series of linked sulfated tetracyclitols [Cyc2N-R-NCyc2, where Cyc = C6H6(OSO3Na)3 and R = (CH2)n (n = 2-10), p-xylyl or (C2H4)2-Ncyc] for the fibroblast growth factors FGF-1 and FGF-2 have been measured by using a surface plasmon resonance assay. The KD values range from 7.0 nM to 1.1 microM for the alkyl-linked ligands. The binding affinity is independent of the flexibility of the linker, as replacement of the alkyl linker with a rigid p-xylyl group did not affect the KD. Calculations suggest that binding modes for the p-xylyl-linked ligand are similar to those calculated for the flexible alkyl-linked tetracyclitols. The possible formation of cross-linked FGF:cyclitol complexes was examined by determining KD values at increasing protein concentrations. No changes in KD were observed; this suggesting that only 1:1 complexes are formed under these assay conditions. Monte Carlo multiple-minima calculations of low-energy conformers of the FGF-bound ligands showed that all of the sulfated tetracyclitol ligands can bind effectively in the heparan sulfate-binding sites of FGF-1 and FGF-2. Binding affinities of these complexes were estimated by the Linear Interaction Energy (LIE) method to within a root-mean-square deviation of 1 kcal mol(-1) of the observed values. The effect of incorporating cations to balance the overall charge of the complexes during the LIE calculations was also explored.     

From knowledge-based potentials to combinatorial lead design in silico

Computational methods are becoming increasingly used in the drug discovery process. In this Account, we review a novel computational method for lead discovery. This method, called CombiSMoG for "combinatorial small molecule growth", is based on two components: a fast and accurate knowledge-based scoring function used to predict binding affinities of protein-ligand complexes, and a Monte Carlo combinatorial growth algorithm that generates large numbers of low-free-energy ligands in the binding site of a protein. We illustrate the advantages of the method by describing its application in the design of picomolar inhibitors for human carbonic anhydrase.     

Promiscuity and the Conformational Rearrangement of Drug‐Like Molecules: Insight from the Protein Data Bank

Selectivity is a central aspect of lead optimization in the drug discovery process. Medicinal chemists often try to decrease molecular flexibility to improve selectivity, given the common belief that the two are interdependent. To investigate the relationship between polypharmacology and conformational flexibility, we mined the Protein Data Bank and constructed a dataset of pharmaceutically relevant ligands that crystallized in more than one protein target while binding to each co‐crystallized receptor with similar in vitro affinities. After analyzing the molecular conformations of these 100 ligands, we found that 59 ligands bound to different protein targets without significantly changing conformation, suggesting that there is no distinct correlation between conformational flexibility and polypharmacology within our dataset. Ligands crystallized in similar proteins and highly ligand‐efficient compounds with five or fewer rotatable bonds were less likely to adjust conformation when binding.     

Systematic placement of structural water molecules for improved scoring of protein-ligand interactions

Structural water molecules are found in many protein-ligand complexes. They are known to be vital in mediating hydrogen-bonding interactions and, in some cases, key for facilitating tight binding. It is thus very important to consider water molecules when attempting to model protein-ligand interactions for cognate ligand identification, virtual screening and drug design. While the rigid treatment of water molecules present in structures is feasible, the more relevant task of treating all possible positions and orientations of water molecules with each possible ligand pose is computationally daunting. Current methods in molecular docking provide partial treatment for such water molecules, with modest success. Here we describe a new method employing dead-end elimination to place water molecules within a binding site, bridging interactions between protein and ligand. Dead-end elimination permits a thorough, though still incomplete, treatment of water placement. The results show that this method is able to place water molecules correctly within known complexes and to create physically reasonable hydrogen bonds. The approach has also been incorporated within an inverse molecular design approach, to model a variety of compounds in the process of de novo ligand design. The inclusion of structural water molecules, combined with ranking based on the electrostatic contribution to binding affinity, improves a number of otherwise poor energetic predictions.     

Multipose Binding in Molecular Docking

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.     

Molecular Dynamics Simulation of Tryptophan Hydroxylase-1: Binding Modes and Free Energy Analysis to Phenylalanine Derivative Inhibitors

Serotonin is a neurotransmitter that modulates many central and peripheral functions. Tryptophan hydroxylase-1 (TPH1) is a key enzyme of serotonin synthesis. In the current study, the interaction mechanism of phenylalanine derivative TPH1 inhibitors was investigated using molecular dynamics (MD) simulations, free energy calculations, free energy decomposition analysis and computational alanine scanning. The predicted binding free energies of these complexes are consistent with the experimental data. The analysis of the individual energy terms indicates that although the van der Waals and electrostatics interaction contributions are important in distinguishing the binding affinities of these inhibitors, the electrostatic contribution plays a more crucial role in that. Moreover, it is observed that different configurations of the naphthalene substituent could form different binding patterns with protein, yet lead to similar inhibitory potency. The combination of different molecular modeling techniques is an efficient way to interpret the interaction mechanism of inhibitors and our work could provide valuable information for the TPH1 inhibitor design in the future.     

Measurement of Nanomolar Dissociation Constants by Titration Calorimetry and Thermal Shift Assay �C Radicicol Binding to Hsp90 and Ethoxzolamide Binding to CAII

The analysis of tight protein-ligand binding reactions by isothermal titration calorimetry (ITC) and thermal shift assay (TSA) is presented. The binding of radicicol to the N-terminal domain of human heat shock protein 90 (Hsp90αN) and the binding of ethoxzolamide to human carbonic anhydrase (hCAII) were too strong to be measured accurately by direct ITC titration and therefore were measured by displacement ITC and by observing the temperature-denaturation transitions of ligand-free and ligand-bound protein. Stabilization of both proteins by their ligands was profound, increasing the melting temperature by more than 10 ºC, depending on ligand concentration. Analysis of the melting temperature dependence on the protein and ligand concentrations yielded dissociation constants equal to 1 nM and 2 nM for Hsp90αN-radicicol and hCAII-ethoxzolamide, respectively. The ligand-free and ligand-bound protein fractions melt separately, and two melting transitions are observed. This phenomenon is especially pronounced when the ligand concentration is equal to about half the protein concentration. The analysis compares ITC and TSA data, accounts for two transitions and yields the ligand binding constant and the parameters of protein stability, including the Gibbs free energy and the enthalpy of unfolding.