Diverse Fragment Clustering and Water Exclusion Identify Protein Hot Spots

A water structure indicator suitable for generic contexts: Two-liquid behavior at hydration and nanoconfinement conditions and a molecular approach to hydrophobicity and wetting

In a recent work, we have briefly introduced a new structural index for water that, unlike previous indicators, was devised specifically for generic contexts beyond bulk conditions, making it suitable for hydration and nanoconfinement settings. In this work, we shall study this metric in detail, demonstrating its ability to reveal the existence of a fine-tuned interplay between the local structure and energetics in liquid water. This molecular principle enables the establishment of an extended hydrogen bond network, while simultaneously allowing for the existence of network defects by compensating for uncoordinated sites. By studying different water models and different temperatures encompassing both the normal liquid and the supercooled regime, this molecular mechanism will be shown to underlie the two-state behavior of bulk water. In addition, by studying functionalized self-assembled monolayers and diverse graphene-like surfaces, we shall show that this principle is also operative at hydration and nanoconfinement conditions, thus generalizing the validity of the two-liquid scenario of water to these contexts. This approach will allow us to define conditions for wettability, providing an accurate measure of hydrophobicity and a reliable predictor of filling and drying transitions. Hence, it might open the possibility of elucidating the active role of water in the broad fields of biophysics and materials science. As a preliminary step, we shall study the hydration structure and hydrophilicity of graphene-like systems (parallel graphene sheets and carbon nanotubes) as a function of the confinement dimensionality.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Fragment-based design of small molecule PCSK9 inhibitors using simulated annealing of chemical potential simulations

PCSK9 is a protein secreted by the liver that binds to the low-density lipoprotein receptor (LDLR), causing LDLR internalization, decreasing the clearance of circulating LDL particles. Mutations in PCSK9 that strengthen its interactions with LDLR result in familial hypercholesterolemia (FH) and early onset atherosclerosis, while nonsense mutations of PCSK9 result in cardio-protective hypocholesterolemia. These observations led to PCSK9 inhibition for cholesterol lowering becoming a high-interest therapeutic target, with antibody drugs reaching the market. An orally-available small molecule drug is highly desirable, but inhibiting the PCSK9/LDLR protein-protein interaction (PPI) has proven challenging. Alternate approaches to finding good lead candidates are needed. Motivated by the FH mutation data on PCSK9, we found that modeling the PCSK9/LDLR interface revealed extensive electron delocalization between and within the protein partners. Based on this, we hypothesized that compounds assembled from chemical fragments could achieve the affinity required to inhibit the PCSK9/LDLR PPI if they were selected to interact with PCSK9 in a way that, like LDLR, also involves significant fractional charge transfer to form partially covalent bonds. To identify such fragments, Simulated Annealing of Chemical Potential (SACP) fragment simulations were run on multiple PCSK9 structures, using optimized partial charges for the protein. We designed a small molecule, composed of several fragments, predicted to interact at two sites on the PCSK9. This compound inhibits the PPI with 1 μM affinity. Further, we designed two similar small molecules where one allows charge delocalization though a linker and the other doesn’t. The first inhibitor with charge delocalization enhances LDLR surface expression by 60% at 10 nM, two orders of magnitude more potent than the EGF domain of LDLR. The other enhances LDLR expression by only 50% at 1 μM. This supports our conjecture that fragments can have surprisingly outsized efficacy in breaking PPI’s by achieving fractional charge transfer leading to partially covalent bonding.

Studies on electrostatic interactions within model nano-confined aqueous environments of different chemical nature

We study the potential of mean force for pairs of parallel flat surfaces with attractive electrostatic interactions by employing model systems functionalized with different charged, hydrophobic and hydrophilic groups. We study the way in which the local environment (hydrophobic or hydrophilic moieties) modulates the interaction between the attractive charged groups on the plates by removing or attracting nearby water and thus screening or not the electrostatic interaction. To explicitly account for the role of the solvent and the local hydrophobicity, we also perform studies in vacuo. Additionally, the results are compared to that for non-charged plates in order to single out and rationalize the non-additivity of the different non-covalent interactions. Our simulations demonstrate that the presence of neighboring hydrophobic groups promote water removal in the vicinity of the charged groups, thus enhancing charge attraction upon self-assembly. This role of the local hydrophobicity modulating electrostatic interactions is consistent with recent qualitative descriptions in the protein binding context.

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition.

Genomes, structural biology and drug discovery: combating the impacts of mutations in genetic disease and antibiotic resistance

For over four decades structural biology has been used to understand the mechanisms of disease, and structure-guided approaches have demonstrated clearly that they can contribute to many aspects of early drug discovery, both computationally and experimentally. Structure can also inform our understanding of impacts of mutations in human genetic diseases and drug resistance in cancers and infectious diseases. We discuss the ways that structural insights might be useful in both repurposing off-licence drugs and guide the design of new molecules that might be less susceptible to drug resistance in the future.

Hydrogen Bond Dynamic Propensity Studies for Protein Binding and Drug Design

We study the dynamic propensity of the backbone hydrogen bonds of the protein MDM2 (the natural regulator of the tumor suppressor p53) in order to determine its binding properties. This approach is fostered by the observation that certain backbone hydrogen bonds at the p53-binding site exhibit a dynamical propensity in simulations that differs markedly form their state-value (that is, formed/not formed) in the PDB structure of the apo protein. To this end, we conduct a series of hydrogen bond propensity calculations in different contexts: 1) computational alanine-scanning studies of the MDM2-p53 interface; 2) the formation of the complex of MDM2 with the disruptive small molecule Nutlin-3a (dissecting the contribution of the different molecular fragments) and 3) the binding of a series of small molecules (drugs) with different affinities for MDM2. Thus, the relevance of the hydrogen bond propensity analysis for protein binding studies and as a useful tool to complement existing methods for drug design and optimization will be made evident.

Identifying Interactions that Determine Fragment Binding at Protein Hotspots

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods do little to suggest which interactions within a pocket are the most important for binding. Here we illustrate a method that samples atomic hotspots with simple molecular probes to produce fragment hotspot maps. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. The fragment hotspot map calculation is validated using experimental binding positions of 21 fragments and subsequent lead molecules. The ligands are found in high scoring areas of the fragment hotspot maps, with fragment atoms having a median percentage rank of 97%. Protein kinase B and pantothenate synthetase are examined in detail. In each case, the fragment hotspot maps are able to rationalize a Free-Wilson analysis of SAR data from a fragment-based drug design project.

Synthesis and evaluation of orally active small molecule HIV-1 Nef antagonists

The HIV-1 Nef accessory factor enhances viral replication and promotes immune system evasion of HIV-infected cells, making it an attractive target for drug discovery. Recently we described a novel class of diphenylpyrazolodiazene compounds that bind directly to Nef in vitro and inhibit Nef-dependent HIV-1 infectivity and replication in cell culture. However, these first-generation Nef antagonists have several structural liabilities, including an azo linkage that led to poor oral bioavailability. The azo group was therefore replaced with either a one- or two-carbon linker. The resulting set of non-azo analogs retained nanomolar binding affinity for Nef by surface plasmon resonance, while inhibiting HIV-1 replication with micromolar potency in cell-based assays without cytotoxicity. Computational docking studies show that these non-azo analogs occupy the same predicted binding site within the HIV-1 Nef dimer interface as the original azo compound. Computational methods also identified a hot spot for inhibitor binding within this site that is defined by conserved HIV-1 Nef residues Asp108, Leu112, and Pro122. Pharmacokinetic evaluation of the non-azo B9 analogs in mice showed that replacement of the azo linkage dramatically enhanced oral bioavailability without substantially affecting plasma half-life or clearance. The improved oral bioavailability of non-azo diphenylpyrazolo Nef antagonists provides a starting point for further drug lead optimization in support of future efficacy testing in animal models of HIV/AIDS.

Computational solvent mapping in structure-based drug design

Over the past two decades, solvent mapping has emerged as a useful tool for identifying hot spots within binding sites on proteins for drug-like molecules and suggesting properties of potential binders. While the experimental technique requires solving multiple crystal structures of a protein in different solvents, computational solvent mapping allows for fast analysis of a protein for potential binding sites and their druggability. Recent advances in genomics, systems biology and interactomics provide a multitude of potential targets for drug development and solvent mapping can provide useful information to help prioritize targets for drug discovery projects. Here, we review various approaches to computational solvent mapping, highlight some key advances and provide our opinion on future directions in the field.

Designing a small molecule erythropoietin mimetic

Erythropoietin (EPO) is a protein made by the kidneys in response to low red blood cell count that is secreted into the bloodstream and binds to a receptor on hematopoietic stem cells in the bone marrow inducing them to become new red blood cells. EPO made with recombinant DNA technology was brought to market in the 1980s to treat anemia caused by kidney disease and cancer chemotherapy. Because EPO infusion was able to replace blood transfusions in many cases, it rapidly became a multibillion dollar per year drug and as the first biologic created with recombinant technology it launched the biotech industry. For many years intense research was focused on creating a small molecule orally available EPO mimetic. The Robert Wood Johnson (RWJ) group seemed to definitively establish that only large peptides with a minimum of 60 residues could replace EPO, as anything less was not a full agonist. An intense study of the published work led me to hypothesize that the size of the mimetic is not the real issue, but the symmetry making and breaking of the EPO receptor induced by the ligand is the key to activating the stem cells. This analysis meant that residues in the binding site of the receptor deemed absolutely essential for ligand binding and activation from mutagenesis experiments, were probably not really that important. My fundamental hypotheses were: (a) the symmetric state of the homodimeric receptor is the most stable state and thus must be the off-state, (b) a highly localized binding site exists at a pivot point where the two halves of the receptor meet, (c) small molecules can be created that have high potency for this site that will be competitive with EPO and thus can displace the protein-protein interaction, (d) small symmetric molecules will stabilize the symmetric off-state of the receptor, and (e) a key asymmetry in the small molecule will stabilize a mirror image asymmetry in the receptor resulting in the stabilization of the on-state and proliferation of the stem cells into red blood cells. Researchers at Amgen published a co-crystal structure of EPO bound to the EPO receptor, which has a beautiful twofold symmetry-it was argued that this is the active state of the receptor. Activating the EPO receptor with EPO induces an almost instantaneous shutdown mechanism to sharply curtail any proliferative signal transduction, and thus, my hypotheses lead to the conclusion that the Amgen co-crystal is actually the state after receptor downregulation and thus an off-state. To put these hypotheses to the test, my computational method of Simulated Annealing of Chemical Potential was run using the co-crystal created at RWJ, which is the receptor trapped in a partial agonist state. The simulations predicted a previously unknown high affinity binding site at the pivot point where the two halves of the dimeric receptor meet, and detailed analysis of the fragment patterns led to the prediction of a molecule less than 300 MW that is basically twofold symmetric with a chiral center on one side and not the other. Thus, to the degree that computer simulations can be taken seriously, these results support my hypotheses on small molecule receptor activation. When this small molecule was synthesized and tested it indeed induced human hematopoietic stems cells to become red blood cells. When the predicted chiral center of this molecule was removed eliminating its one asymmetric feature, the resulting molecule was an antagonist-it could potently displace hot EPO but could no longer induce stem cell proliferation and differentiation. These results provided strong support for my theories on how to create potent small molecule EPO agonists and were used to launch the new company Locus Pharmaceuticals. These molecules, however, required significant chemical changes in order to make them stable in other in vitro assays and to be in vivo active, but these alterations had to be done in a way that maintained the symmetry-asymmetry considerations that led to the creation of an in vitro active molecule. The combination of changing functional groups to enable good pharmacokinetics, while not changing the key intrinsic symmetry properties were never seriously pursued at Locus and the program died. Investigations into how red blood cells are created have occupied many prominent researchers for the entire twentieth century. In the second half of the century EPO was discovered and by the end of the century it became a blockbuster commercial product that launched the biotech revolution.

Designing an orally available nontoxic p38 inhibitor with a fragment-based strategy

The MAPK p38 became a focal point of inflammatory research when it was recognized that it played a key role in the production of the pro-inflammatory molecules TNF-alpha, IL-beta, and cyclooxygenase-2 (COX-2). The pharmaceutical industry devoted enormous efforts to creating p38 inhibitors, because blocking p38 had the potential of downregulating a group of pro-inflammatory mediators, and thus, one drug could have a cocktail effect. The market potential seemed to be clearly established (Bonafede et al., Clinicoecon Outcomes Res 6:381-388, 2014) with a multiplicity of TNF-alpha antibodies and a soluble receptor (Mewar and Wilson, Br J Pharmacol 162:785-791, 2011) already on the market, although the relationship between TNF-alpha production and p38 activation is a complicated two-way (Sabio and Davis, Semin Immunol 26:237-245, 2014) signal transduction process. With the discovery that activated p38 stabilizes (Mancini and Di Battista, Inflamm Res 60:1083-1092, 2011) COX-2 mRNA and upregulates expression of IL-beta (Bachstetter and Van Eldik, Aging Dis 1:199-211, 2010) probably in a similar manner, inhibiting p38 appeared to be a way of blocking TNF-alpha, COX-2, and IL-beta simultaneously. At Locus Pharmaceuticals we jumped on this opportunity, because we believed that our fragment-based drug discovery approach was ideally suited for making a potent small molecule p38 inhibitor that did not bind in the ATP site, but also had the solubility, lack of planarity, and low molecular weight required of a clinical candidate. Just to be clear, in our experience highly planar compounds often result in poor pharmacokinetics, because they tend to bind strongly to plasma proteins. At Locus we typically repeated assays by adding increasing amounts of plasma to check for potency degradation in the presence of blood. We found this to be a useful early indicator of pharmacokinetics and in vivo behavior. It became clear from our work and the work of others that binding to the ATP site resulted in nonspecific isoform toxicities, but binding in the adjacent allosteric DFG-site resulted in molecules that were too planar and too hydrophobic. Applying the computational method of Simulated Annealing of Chemical Potential (SACP) to this problem, we at Locus were able to come up with surprising fragment substitution patterns that led to potent non-ATP p38 inhibitors with the solubility and lack of planarity that resulted in potent in vivo efficacy in rodents with 33 % oral bioavailability. By using the simulations, we made only a small number of molecules and created a high quality clinical candidate. We also did extensive co-crystallography work, which demonstrated that the compounds bound in the mode predicted by the simulations. Unfortunately, all p38 programs ultimately shut down, because compelling evidence emerged that inhibiting p38 had no long-term clinical (Genovese, Arthritis Rheum 60:317-320, 2009) benefit. Devoting a large amount of limited resources to a target that ultimately turns out to be a mistake because it was not properly validated is a fatal error for a small company, and this is one of the reasons that Locus ultimately failed.

"Chameleonic" backbone hydrogen bonds in protein binding and as drug targets

We carry out a time-averaged contact matrix study to reveal the existence of protein backbone hydrogen bonds (BHBs) whose net persistence in time differs markedly form their corresponding PDB-reported state. We term such interactions as "chameleonic" BHBs, CBHBs, precisely to account for their tendency to change the structural prescription of the PDB for the opposite bonding propensity in solution. We also find a significant enrichment of protein binding sites in CBHBs, relate them to local water exposure and analyze their behavior as ligand/drug targets. Thus, the dynamic analysis of hydrogen bond propensity might lay the foundations for new tools of interest in protein binding-site prediction and in lead optimization for drug design.

Hydration and Nanoconfined Water: Insights from Computer Simulations

The comprehension of the structure and behavior of water at interfaces and under nanoconfinement represents an issue of major concern in several central research areas like hydration, reaction dynamics and biology. From one side, water is known to play a dominant role in the structuring, the dynamics and the functionality of biological molecules, governing main processes like protein folding, protein binding and biological function. In turn, the same principles that rule biological organization at the molecular level are also operative for materials science processes that take place within a water environment, being responsible for the self-assembly of molecular structures to create synthetic supramolecular nanometrically-sized materials. Thus, the understanding of the principles of water hydration, including the development of a theory of hydrophobicity at the nanoscale, is imperative both from a fundamental and an applied standpoint. In this work we present some molecular dynamics studies of the structure and dynamics of water at different interfaces or confinement conditions, ranging from simple model hydrophobic interfaces with different geometrical constraints (in order to single out curvature effects), to self-assembled monolayers, proteins and phospholipid membranes. The tendency of the water molecules to sacrifice the lowest hydrogen bond (HB) coordination as possible at extended interfaces is revealed. This fact makes the first hydration layers to be highly oriented, in some situations even resembling the structure of hexagonal ice. A similar trend to maximize the number of HBs is shown to hold in cavity filling, with small subnanometric hydrophobic cavities remaining empty while larger cavities display an alternation of filled and dry states with a significant inner HB network. We also study interfaces with complex chemical and geometrical nature in order to determine how different conditions affect the local hydration properties. Thus, we show some results for protein hydration and, particularly, some preliminary studies on membrane hydration. Finally, calculations of a local hydrophobicity measure of relevance for binding and self-assembly are also presented. We then conclude with a few words of further emphasis on the relevance of this kind of knowledge to biology and to the design of new materials by highlighting the context-dependent and non-additive nature of different non-covalent interactions in an aqueous nanoenvironment, an issue that is usually greatly overlooked.

The importance of hydration thermodynamics in fragment-to-lead optimization

Using a computational approach to assess changes in solvation thermodynamics upon ligand binding, we investigated the effects of water molecules on the binding energetics of over 20 fragment hits and their corresponding optimized lead compounds. Binding activity and X-ray crystallographic data of published fragment-to-lead optimization studies from various therapeutically relevant targets were studied. The analysis reveals a distinct difference between the thermodynamic profile of water molecules displaced by fragment hits and those displaced by the corresponding optimized lead compounds. Specifically, fragment hits tend to displace water molecules with notably unfavorable excess entropies-configurationally constrained water molecules-relative to those displaced by the newly added moieties of the lead compound during the course of fragment-to-lead optimization. Herein we describe the details of this analysis with the goal of providing practical guidelines for exploiting thermodynamic signatures of binding site water molecules in the context of fragment-to-lead optimization.

Sampling of Organic Solutes in Aqueous and Heterogeneous Environments Using Oscillating Excess Chemical Potentials in Grand Canonical-like Monte Carlo-Molecular Dynamics Simulations

Solute sampling of explicit bulk-phase aqueous environments in grand canonical (GC) ensemble simulations suffer from poor convergence due to low insertion probabilities of the solutes. To address this, we developed an iterative procedure involving Grand Canonical-like Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Each iteration involves GCMC of both the solutes and water followed by MD, with the excess chemical potential (μ_ex) of both the solute and the water oscillated to attain their target concentrations in the simulation system. By periodically varying the μ_ex of the water and solutes over the GCMC-MD iterations, solute exchange probabilities and the spatial distributions of the solutes improved. The utility of the oscillating-μ_ex GCMC-MD method is indicated by its ability to approximate the hydration free energy (HFE) of the individual solutes in aqueous solution as well as in dilute aqueous mixtures of multiple solutes. For seven organic solutes: benzene, propane, acetaldehyde, methanol, formamide, acetate, and methylammonium, the average μ_ex of the solutes and the water converged close to their respective HFEs in both 1 M standard state and dilute aqueous mixture systems. The oscillating-μ_ex GCMC methodology is also able to drive solute sampling in proteins in aqueous environments as shown using the occluded binding pocket of the T4 lysozyme L99A mutant as a model system. The approach was shown to satisfactorily reproduce the free energy of binding of benzene as well as sample the functional group requirements of the occluded pocket consistent with the crystal structures of known ligands bound to the L99A mutant as well as their relative binding affinities.

Rapid estimation of hydration thermodynamics of macromolecular regions

This work describes a novel protocol to efficiently calculate the local free energy of hydration of specific regions in macromolecules. The method employs Monte Carlo simulations in the grand canonical ensemble to generate water configurations in a selected spherical region in the macromolecule. Excess energy and entropy of hydration are calculated by analyzing the water configurational distributions following the recently published grid inhomogeneous solvation theory method [C. N. Nguyen, T. K. Young, and M. K. Gilson, J. Chem. Phys. 137, 044101 (2012)]. Our method involves the approximations of treating the macromolecule and distant solvent as rigid and performing calculations on multiple such conformations to account for conformational diversity. These approximations are tested against water configurations obtained from a molecular dynamics simulation. The method is validated by predicting the number and location of water molecules in 5 pockets in the protein Interleukin-1β for which experimental water occupancy data are available. Free energy values are validated against decoupling free energy perturbation calculations. The results indicate that the approximations used in the method enable efficient prediction of free energies of water displacement.

Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules

Intermolecular interactions in the aqueous phase must compete with the interactions between the two binding partners and their solvating water molecules. In biological systems, water molecules in protein binding sites cluster at well-defined hydration sites and can form strong hydrogen-bonding interactions with backbone and side-chain atoms. Displacement of such water molecules is only favorable when the ligand can form strong compensating hydrogen bonds. Conversely, water molecules in hydrophobic regions of protein binding sites make only weak interactions, and the requirements for favorable displacement are less stringent. The propensity of water molecules for displacement can be identified using inhomogeneous fluid solvation theory (IFST), a statistical mechanical method that decomposes the solvation free energy of a solute into the contributions from different spatial regions and identifies potential binding hotspots. In this study, we employed IFST to study the displacement of water molecules from the ATP binding site of Hsp90, using a test set of 103 ligands. The predicted contribution of a hydration site to the hydration free energy was found to correlate well with the observed displacement. Additionally, we investigated if this correlation could be improved by using the energetic scores of favorable probe groups binding at the location of hydration sites, derived from a multiple copy simultaneous search (MCSS) method. The probe binding scores were not highly predictive of the observed displacement and did not improve the predictivity when used in combination with IFST-based hydration free energies. The results show that IFST alone can be used to reliably predict the observed displacement of water molecules in Hsp90. However, MCSS can augment IFST calculations by suggesting which functional groups should be used to replace highly displaceable water molecules. Such an approach could be very useful in improving the hit-to-lead process for new drug targets.

Protein packing defects "heat up" interfacial water

Ligands must displace water molecules from their corresponding protein surface binding site during association. Thus, protein binding sites are expected to be surrounded by non-tightly-bound, easily removable water molecules. In turn, the existence of packing defects at protein binding sites has been also established. At such structural motifs, named dehydrons, the protein backbone is exposed to the solvent since the intramolecular interactions are incompletely wrapped by non-polar groups. Hence, dehydrons are sticky since they depend on additional intermolecular wrapping in order to properly protect the structure from water attack. Thus, a picture of protein binding is emerging wherein binding sites should be both dehydrons rich and surrounded by easily removable water. In this work we shall indeed confirm such a link between structure and dynamics by showing the existence of a firm correlation between the degree of underwrapping of the protein chain and the mobility of the corresponding hydration water molecules. In other words, we shall show that protein packing defects promote their local dehydration, thus producing a region of "hot" interfacial water which might be easily removed by a ligand upon association.

A fragment-based approach to the SAMPL3 Challenge

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands--regardless of the charged state in the active site--produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.

Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules

Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of druggability would allow informed decisions on the risk of investing in a particular target. Here, we define “druggability” as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as murine double mutant-2, protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein kinase provides examples for which the method correctly captures the location and binding affinities of known drugs. It also provides insights into novel druggable sites and the target’s structural changes that would accommodate, if not promote and stabilize, drug binding. Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the druggability of protein targets and identifying allosteric or novel sites for drug binding.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

Robust identification of binding hot spots using continuum electrostatics: application to hen egg-white lysozyme

Binding hot spots, protein regions with high binding affinity, can be identified by using X-ray crystallography or NMR spectroscopy to screen libraries of small organic molecules that tend to cluster at such hot spots. FTMap, a direct computational analogue of the experimental screening approaches, uses 16 different probe molecules for global sampling of the surface of a target protein on a dense grid and evaluates the energy of interaction using an empirical energy function that includes a continuum electrostatic term. Energy evaluation is based on the fast Fourier transform correlation approach, which allows for the sampling of billions of probe positions. The grid sampling is followed by off-grid minimization that uses a more detailed energy expression with a continuum electrostatics term. FTMap identifies the hot spots as consensus clusters formed by overlapping clusters of several probes. The hot spots are ranked on the basis of the number of probe clusters, which predicts their binding propensity. We applied FTMap to nine structures of hen egg-white lysozyme (HEWL), whose hot spots have been extensively studied by both experimental and computational methods. FTMap found the primary hot spot in site C of all nine structures, in spite of conformational differences. In addition, secondary hot spots in sites B and D that are known to be important for the binding of polysaccharide substrates were found. The predicted probe–protein interactions agree well with those seen in the complexes of HEWL with various ligands and also agree with an NMR-based study of HEWL in aqueous solutions of eight organic solvents. We argue that FTMap provides more complete information on the HEWL binding site than previous computational methods and yields fewer false-positive binding locations than the X-ray structures of HEWL from crystals soaked in organic solvents.

Molecular modeling and functional confirmation of a predicted fatty acid binding site of mitochondrial aspartate aminotransferase

Molecular interactions are necessary for proteins to perform their functions. The identification of a putative plasma membrane fatty acid transporter as mitochondrial aspartate aminotransferase (mAsp-AT) indicated that the protein must have a fatty acid binding site. Molecular modeling suggests that such a site exists in the form of a 500-Å(3) hydrophobic cleft on the surface of the molecule and identifies specific amino acid residues that are likely to be important for binding. The modeling and comparison with the cytosolic isoform indicated that two residues (Arg201 and Ala219) were likely to be important to the structure and function of the binding site. These residues were mutated to determine if they were essential to that function. Expression constructs with wild-type or mutated cDNAs were produced for bacteria and eukaryotic cells. Proteins expressed in Escherichia coli were tested for oleate binding affinity, which was decreased in the mutant proteins. 3T3 fibroblasts were transfected with expression constructs for both normal and mutated forms. Plasma membrane expression was documented by indirect immunofluorescence before [(3)H]oleic acid uptake kinetics were assayed. The V(max) for uptake was significantly increased by overexpression of the wild-type protein but changed little after transfection with mutated proteins, despite their presence on the plasma membrane. The hydrophobic cleft in mAsp-AT can serve as a fatty acid binding site. Specific residues are essential for normal fatty acid binding, without which fatty acid uptake is compromised. These results confirm the function of this protein as a fatty acid binding protein.