Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint.

Conformational sampling of complex biomolecules is an emerging frontier in drug discovery. Advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures for biologically relevant targets. However, biology is in constant motion, and many important biological processes rely on conformationally driven events. Conventional molecular dynamics (MD) simulations run on standard hardware are impractical for many drug design projects, where conformationally driven biological events can take microseconds to milliseconds or longer. An alternative approach is to focus the search on a limited region of conformational space defined by a putative reaction coordinate (i.e., path collective variable). The search space is typically limited by applying restraints, which can be guided by insights about the underlying biological process of interest. The challenge is striking a balance between the degree to which the system is constrained and still allowing for natural motions along the path. A plethora of restraints exist to limit the size of conformational search space, although each has drawbacks when simulating complex biological motions. In this work, we present a three-stage procedure to construct realistic path collective variables (PCVs) and introduce a new kind of barrier restraint that is particularly well suited for complex conformationally driven biological events, such as allosteric modulations and conformational signaling. The PCV presented here is all-atom (as opposed to C-alpha or backbone only) and is derived from all-atom MD trajectory frames. The new restraint relies on a barrier function (specifically, the scaled reciprocal function), which we show is particularly beneficial in the context of molecular dynamics, where near-hard-wall restraints are needed with zero tolerance to restraint violation. We have implemented our PCV and barrier restraint within a hybrid sampling framework that combines well-tempered metadynamics and extended-Lagrangian adaptive biasing force (meta-eABF). We use three particular examples of high pharmaceutical interest to demonstrate the value of this approach: (1) sampling the distance from ubiquitin to a protein of interest within the supramolecular cullin-RING ligase complex, (2) stabilizing the wild-type conformation of the oncogenic mutant JAK2-V617F pseudokinase domain, and (3) inducing an activated state of the stimulator of interferon genes (STING) protein observed upon ligand binding. For examples 2 and 3, we present statistical analysis of meta-eABF free energy estimates and, for each case, code for reproducing this work.

A Water-Bridged Cysteine-Cysteine Redox Regulation Mechanism in Bacterial Protein Tyrosine Phosphatases.

The emergence of multidrug-resistant Mycobacterium tuberculosis (Mtb) strains highlights the need to develop more efficacious and potent drugs. However, this goal is dependent on a comprehensive understanding of Mtb virulence protein effectors at the molecular level. Here, we used a post-expression cysteine (Cys)-to-dehydrolanine (Dha) chemical editing strategy to identify a water-mediated motif that modulates accessibility of the protein tyrosine phosphatase A (PtpA) catalytic pocket. Importantly, this water-mediated Cys-Cys non-covalent motif is also present in the phosphatase SptpA from Staphylococcus aureus, which suggests a potentially preserved structural feature among bacterial tyrosine phosphatases. The identification of this structural water provides insight into the known resistance of Mtb PtpA to the oxidative conditions that prevail within an infected host macrophage. This strategy could be applied to extend the understanding of the dynamics and function(s) of proteins in their native state and ultimately aid in the design of small-molecule modulators.

In search of novel ligands using a structure-based approach: a case study on the adenosine A2A receptor.

In this work, we present a case study to explore the challenges associated with finding novel molecules for a receptor that has been studied in depth and has a wealth of chemical information available. Specifically, we apply a previously described protocol that incorporates explicit water molecules in the ligand binding site to prospectively screen over 2.5 million drug-like and lead-like compounds from the commercially available eMolecules database in search of novel binders to the adenosine A2A receptor (A2AAR). A total of seventy-one compounds were selected for purchase and biochemical assaying based on high ligand efficiency and high novelty (Tanimoto coefficient ≤0.25 to any A2AAR tested compound). These molecules were then tested for their affinity to the adenosine A2A receptor in a radioligand binding assay. We identified two hits that fulfilled the criterion of ~50 % radioligand displacement at a concentration of 10 µM. Next we selected an additional eight novel molecules that were predicted to make a bidentate interaction with Asn2536.55, a key interacting residue in the binding pocket of the A2AAR. None of these eight molecules were found to be active. Based on these results we discuss the advantages of structure-based methods and the challenges associated with finding chemically novel molecules for well-explored targets.

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

AIM: We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. METHODOLOGY/RESULTS: The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. CONCLUSION: AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

Differential Water Thermodynamics Determine PI3K-Beta/Delta Selectivity for Solvent-Exposed Ligand Modifications.

Phosphoinositide 3-kinases (PI3Ks) are involved in important cellular functions and represent desirable targets for drug discovery efforts, especially related to oncology; however, the four PI3K subtypes (α, ß, γ, and δ) have highly similar binding sites, making the design of selective inhibitors challenging. A series of inhibitors with selectivity toward the ß subtype over δ resulted in compound 3(S), which has entered a phase I/Ib clinical trial for patients with advanced PTEN-deficient cancer. Interestingly, X-ray crystallography revealed that the modifications making inhibitor 3(S) and related compounds selective toward the ß-isoform do not interact directly with either PI3Kß or PI3Kδ, thereby confounding rationalization of the SAR. Here, we apply explicit solvent molecular dynamics and solvent thermodynamic analysis using WaterMap in an effort to understand the unusual affinity and selectivity trends. We find that differences in solvent energetics and water networks, which are modulated upon binding of different ligands, explain the experimental affinity and selectivity trends. This study highlights the critical role of water molecules in molecular recognition and the importance of considering water networks in drug discovery efforts to rationalize and improve selectivity.

Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation.

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, ß1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.

Chemical basis for the recognition of trimethyllysine by epigenetic reader proteins.

A large number of structurally diverse epigenetic reader proteins specifically recognize methylated lysine residues on histone proteins. Here we describe comparative thermodynamic, structural and computational studies on recognition of the positively charged natural trimethyllysine and its neutral analogues by reader proteins. This work provides experimental and theoretical evidence that reader proteins predominantly recognize trimethyllysine via a combination of favourable cation-π interactions and the release of the high-energy water molecules that occupy the aromatic cage of reader proteins on the association with the trimethyllysine side chain. These results have implications in rational drug design by specifically targeting the aromatic cage of readers of trimethyllysine.

Structure-based approach to the prediction of disulfide bonds in proteins.

Protein engineering remains an area of growing importance in pharmaceutical and biotechnology research. Stabilization of a folded protein conformation is a frequent goal in projects that deal with affinity optimization, enzyme design, protein construct design, and reducing the size of functional proteins. Indeed, it can be desirable to assess and improve protein stability in order to avoid liabilities such as aggregation, degradation, and immunogenic response that may arise during development. One way to stabilize a protein is through the introduction of disulfide bonds. Here, we describe a method to predict pairs of protein residues that can be mutated to form a disulfide bond. We combine a physics-based approach that incorporates implicit solvent molecular mechanics with a knowledge-based approach. We first assign relative weights to the terms that comprise our scoring function using a genetic algorithm applied to a set of 75 wild-type structures that each contains a disulfide bond. The method is then tested on a separate set of 13 engineered proteins comprising 15 artificial stabilizing disulfides introduced via site-directed mutagenesis. We find that the native disulfide in the wild-type proteins is scored well, on average (within the top 6% of the reasonable pairs of residues that could form a disulfide bond) while 6 out of the 15 artificial stabilizing disulfides scored within the top 13% of ranked predictions. Overall, this suggests that the physics-based approach presented here can be useful for triaging possible pairs of mutations for disulfide bond formation to improve protein stability.

Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor.

A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.

Synthesis, biological evaluation, hydration site thermodynamics, and chemical reactivity analysis of α-keto substituted peptidomimetics for the inhibition of Plasmodium falciparum.

A new series of peptidomimetic pseudo-prolyl-homophenylalanylketones were designed, synthesized and evaluated for inhibition of the Plasmodium falciparum cysteine proteases falcipain-2 (FP-2) and falcipain-3 (FP-3). In addition, the parasite killing activity of these compounds in human blood-cultured P. falciparum was examined. Of twenty-two (22) compounds synthesized, one peptidomimetic comprising a homophenylalanine-based α-hydroxyketone linked Cbz-protected hydroxyproline (39) showed the most potency (IC50 80 nM against FP-2 and 60 nM against FP-3). In silico analysis of these peptidomimetic analogs offered important protein-ligand structural insights including the role, by WaterMap, of water molecules in the active sites of these protease isoforms. The pseudo-dipeptide 39 and related compounds may serve as a promising direction forward in the design of competitive inhibitors of falcipains for the effective treatment of malaria.

Allosteric inhibition of the NS2B-NS3 protease from dengue virus.

Dengue virus is the flavivirus that causes dengue fever, dengue hemorrhagic disease, and dengue shock syndrome, which are currently increasing in incidence worldwide. Dengue virus protease (NS2B-NS3pro) is essential for dengue virus infection and is thus a target of therapeutic interest. To date, attention has focused on developing active-site inhibitors of NS2B-NS3pro. The flat and charged nature of the NS2B-NS3pro active site may contribute to difficulties in developing inhibitors and suggests that a strategy of identifying allosteric sites may be useful. We report an approach that allowed us to scan the NS2B-NS3pro surface by cysteine mutagenesis and use cysteine reactive probes to identify regions of the protein that are susceptible to allosteric inhibition. This method identified a new allosteric site utilizing a circumscribed panel of just eight cysteine variants and only five cysteine reactive probes. The allosterically sensitive site is centered at Ala125, between the 120s loop and the 150s loop. The crystal structures of WT and modified NS2B-NS3pro demonstrate that the 120s loop is flexible. Our work suggests that binding at this site prevents a conformational rearrangement of the NS2B region of the protein, which is required for activation. Preventing this movement locks the protein into the open, inactive conformation, suggesting that this site may be useful in the future development of therapeutic allosteric inhibitors.

Improved docking of polypeptides with Glide.

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.

Current assessment of docking into GPCR crystal structures and homology models: successes, challenges, and guidelines.

The growing availability of novel structures for several G protein-coupled receptors (GPCRs) has provided new opportunities for structure-based drug design of ligands against this important class of targets. Here, we report a systematic analysis of the accuracy of docking small molecules into GPCR structures and homology models using both rigid receptor (Glide SP and Glide XP) and flexible receptor (Induced Fit Docking; IFD) methods. The ability to dock ligands into different structures of the same target (cross-docking) is evaluated for both agonist and inverse agonist structures of the A2A receptor and the ß1- and ß2-adrenergic receptors. In addition, we have produced homology models for the ß1-adrenergic, ß2-adrenergic, D3 dopamine, H1 histamine, M2 muscarine, M3 muscarine, A2A adenosine, S1P1, κ-opioid, and C-X-C chemokine 4 receptors using multiple templates and investigated the ability of docking to predict the binding mode of ligands in these models. Clear correlations are observed between the docking accuracy and the similarity of the sequence of interest to the template, suggesting regimes in which docking can correctly identify ligand binding modes.

Consensus Induced Fit Docking (cIFD): methodology, validation, and application to the discovery of novel Crm1 inhibitors.

We present the Consensus Induced Fit Docking (cIFD) approach for adapting a protein binding site to accommodate multiple diverse ligands for virtual screening. This novel approach results in a single binding site structure that can bind diverse chemotypes and is thus highly useful for efficient structure-based virtual screening. We first describe the cIFD method and its validation on three targets that were previously shown to be challenging for docking programs (COX-2, estrogen receptor, and HIV reverse transcriptase). We then demonstrate the application of cIFD to the challenging discovery of irreversible Crm1 inhibitors. We report the identification of 33 novel Crm1 inhibitors, which resulted from the testing of 402 purchased compounds selected from a screening set containing 261,680 compounds. This corresponds to a hit rate of 8.2 %. The novel Crm1 inhibitors reveal diverse chemical structures, validating the utility of the cIFD method in a real-world drug discovery project. This approach offers a pragmatic way to implicitly account for protein flexibility without the additional computational costs of ensemble docking or including full protein flexibility during virtual screening.

Computer-aided drug design of falcipain inhibitors: virtual screening, structure-activity relationships, hydration site thermodynamics, and reactivity analysis.

Falcipains (FPs) are hemoglobinases of Plasmodium falciparum that are validated targets for the development of antimalarial chemotherapy. A combined ligand- and structure-based virtual screening of commercial databases was performed to identify structural analogs of virtual screening hits previously discovered in our laboratory. A total of 28 low micromolar inhibitors of FP-2 and FP-3 were identified and the structure-activity relationship (SAR) in each series was elaborated. The SAR of the compounds was unusually steep in some cases and could not be explained by a traditional analysis of the ligand-protein interactions (van der Waals, electrostatics, and hydrogen bonds). To gain further insights, a statistical thermodynamic analysis of explicit solvent in the ligand binding domains of FP-2 and FP-3 was carried out to understand the roles played by water molecules in binding of these inhibitors. Indeed, the energetics associated with the displacement of water molecules upon ligand binding explained some of the complex trends in the SAR. Furthermore, low potency of a subset of FP-2 inhibitors that could not be understood by the water energetics was explained in the context of poor chemical reactivity of the reactive centers of these compounds. The present study highlights the importance of considering energetic contributors to binding beyond traditional ligand-protein interactions.

Probing the alpha-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis.

Reactivation of the p53 cell apoptosis pathway through inhibition of the p53-hDM2 interaction is a viable approach to suppress tumor growth in many human cancers and stabilization of the helical structure of synthetic p53 analogs via a hydrocarbon cross-link (staple) has been found to lead to increased potency and inhibition of protein-protein binding (J. Am. Chem. Soc. 129: 5298). However, details of the structure and dynamic stability of the stapled peptides are not well understood. Here, we use extensive all-atom molecular dynamics simulations to study a series of stapled alpha-helical peptides over a range of temperatures in solution. The peptides are found to exhibit substantial variations in predicted alpha-helical propensities that are in good agreement with the experimental observations. In addition, we find significant variation in local structural flexibility of the peptides with the position of the linker, which appears to be more closely related to the observed differences in activity than the absolute alpha-helical stability. These simulations provide new insights into the design of alpha-helical stapled peptides and the development of potent inhibitors of alpha-helical protein-protein interfaces.

Understanding kinase selectivity through energetic analysis of binding site waters.

Kinases remain an important drug target class within the pharmaceutical industry; however, the rational design of kinase inhibitors is plagued by the complexity of gaining selectivity for a small number of proteins within a family of more than 500 related enzymes. Herein we show how a computational method for identifying the location and thermodynamic properties of water molecules within a protein binding site can yield insight into previously inexplicable selectivity and structure-activity relationships. Four kinase systems (Src family, Abl/c-Kit, Syk/ZAP-70, and CDK2/4) were investigated, and differences in predicted water molecule locations and energetics were able to explain the experimentally observed binding selectivity profiles. The successful predictions across the range of kinases studied here suggest that this methodology could be generally applicable for predicting selectivity profiles in related targets.

Hydration Site Thermodynamics Explain SARs for Triazolylpurines Analogues Binding to the A2A Receptor.

A series of triazolylpurine analogues show interesting and unintuitive structure-activity relationships against the A2A adenosine receptor. As the 2-substituted aliphatic group is initially increased to methyl and isopropyl, there is a decrease in potency; however, extending the substituent to n-butyl and n-pentyl results in a significant gain in potency. This trend cannot be readily explained by ligand-receptor interactions, steric effects, or differences in ligand desolvation. Here, we show that a novel method for characterizing solvent thermodynamics in protein binding sites correctly predicts the trend in binding affinity for this series based on the differential water displacement patterns. In brief, small unfavorable substituents occupy a region in the A2A adenosine receptor binding site predicted to contain stable waters, while the longer favorable substituents extend to a region that contains several unstable waters. The predicted binding energies associated with displacing water within these hydration sites correlate well with the experimental activities.

High-energy water sites determine peptide binding affinity and specificity of PDZ domains.

PDZ domains have well known binding preferences for distinct C-terminal peptide motifs. For most PDZ domains, these motifs are of the form [S/T]-W-[I/L/V]. Although the preference for S/T has been explained by a specific hydrogen bond interaction with a histidine in the PDZ domain and the (I/L/V) is buried in a hydrophobic pocket, the mechanism for Trp specificity at the second to last position has thus far remained unknown. Here, we apply a method to compute the free energies of explicit water molecules and predict that potency gained by Trp binding is due to a favorable release of high-energy water molecules into bulk. The affinities of a series of peptides for both wild-type and mutant forms of the PDZ domain of Erbin correlate very well with the computed free energy of binding of displaced waters, suggesting a direct relationship between water displacement and peptide affinity. Finally, we show a correlation between the magnitude of the displaced water free energy and the degree of Trp-sensitivity among subtypes of the HTRA PDZ family, indicating a water-mediated mechanism for specificity of peptide binding.