Probing the human estrogen receptor-α binding requirements for phenolic mono- and di-hydroxyl compounds: A combined synthesis,binding and docking study

Various estrogen analogs were synthesized and tested for binding to human ERα using a fluorescence polarization displacement assay. Binding affinity and orientation were also predicted using docking calculations. Docking was able to accurately predict relative binding affinity and orientation for estradiol, but only if a tightly bound water molecule bridging Arg394/Glu353 is present. Di-hydroxyl compounds sometimes bind in two orientations, which are flipped in terms of relative positioning of their hydroxyl groups. Di-hydroxyl compounds were predicted to bind with their aliphatic hydroxyl group interacting with His524 in ERα. One nonsteroid-based dihdroxyl compound was 1000-fold specific for ERβ over ERα, and was also 25-fold specific for agonist ERβ versus antagonist activity. Docking predictions suggest this specificity may be due to interaction of the aliphatic hydroxyl with His475 in the agonist form of ERβ, versus with Thr299 in the antagonist form. But, the presence of this aliphatic hydroxyl is not required in all compounds, since mono-hydroxyl (phenolic) compounds bind ERα with high affinity, via hydroxyl hydrogen bonding interactions with the ERα Arg394/Glu353/water triad, and van der Waals interactions with the rest of the molecule.     

Structural prediction of peptides binding to MHC class I molecules

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.     

Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein?ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein?protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Validation of the doubly labeled water method under low and high humidity to estimate metabolic rate and water flux in a tropical snake (Boiga irregularis).

This study uses indirect calorimetry to assess the effects of humidity on the accuracy of the doubly labeled water (DLW) technique to predict metabolic rate and water flux in brown treesnakes (Boiga irregularis). The DLW technique accurately predicted total water efflux in brown treesnakes under low-humidity conditions and found that the total number of water molecules exchanged with the environment under humid conditions was not significantly different than maximum net total evaporative water loss under low humidity conditions plus fecal water loss. Because of changes of total body water of >12%, the DLW technique overestimated metabolic rate by a factor of 2.2 under low-humidity conditions. Under high-humidity conditions, the DLW technique overestimated metabolic rate in brown treesnakes by a factor of 4.6. Researchers using the DLW technique in humid or moist environments should be cautious because this study indicates that DLW estimates of metabolic rate may be inflated when large amounts of water vapor are exchanged through the skin or respiratory passages.     

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.     

Effect of trehalose and sucrose on the hydration and dipole potential of lipid bilayers

The water activity in dimyristoylphosphatidylcholine (DMPC) decreases by 60% when the lipid is dehydrated in the presence of trehalose concentrations higher than 0.02 M. In contrast, sucrose in concentrations 10 times higher produced only a 20% decrease in the water activity in the sample. Titrations of a DMPC solution in chloroform yielded 14 water molecules per lipid when pure water was added and seven water molecules per lipid when the titration was done with 0.025 M trehalose. The same concentrations of sucrose produced a turbid solution, which made it impossible to quantify the number of water molecules per lipid. Lipid monolayers spread on an air/water interface showed a decrease from 480 mV in pure water to 425 mV in 0.1 M trehalose. However, the same concentrations of sucrose produced an increase of less than 100 mV. Results obtained with Fourier transform infrared spectroscopy (FTIR) under the same conditions denoted that trehalose binds to the carbonyl groups, while sucrose showed no specific binding. It is concluded that per lipid molecule, 11 of 14 water molecules can be replaced by three trehalose molecules. About four are displaced by changes in the water activity of the bulk solution, and seven by specific interactions with the phospholipids. In this last case, at least two of them are linked to the carbonyls, and this appears to be the cause of the decrease in the dipole potential of the membrane. In contrast, four sucrose molecules displace only three water molecules per lipid, with no effect on the dipole potential or the carbonyl groups.     

Dowser++, a new method of hydrating protein structures

A new method of hydrating protein structures, which we call Dowser++, is presented. The method is based on a semi‐empirical modification of a popular program for protein hydration Dowser, and the usage of protocols AutoDock Vina, and WaterDock. The positions of water molecules predicted by Dowser++ were compared with experimental data for a set of 14 high‐resolution crystal structures of oligopeptide‐binding protein (OppA) containing a large number of resolved internal water molecules, as well as for the D‐ and K‐channels of cytochrome c oxidase, and the recent data on PSII. Comparison is also made with the predictions of the original Dowser, and its improved version, Dowser+, described in our previous publication. We also present a model for quantitative estimation of the quality of water molecules placement made by a program, which includes an assumption of possible false negative data from the crystallographic analysis. The comparison of predictions made by Dowser++, Dowser and Dowser+ demonstrates significant improvement of predictive power of the new method. Proteins 2016; 84:1347–1357. © 2016 Wiley Periodicals, Inc.     

NetMHCpan,a method for MHC class I binding prediction beyond humans

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method’s ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at . Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Global analysis of small molecule binding to related protein targets

We report on the integration of pharmacological data and homology information for a large scale analysis of small molecule binding to related targets. Differences in small molecule binding have been assessed for curated pairs of human to rat orthologs and also for recently diverged human paralogs. Our analysis shows that in general, small molecule binding is conserved for pairs of human to rat orthologs. Using statistical tests, we identified a small number of cases where small molecule binding is different between human and rat, some of which had previously been reported in the literature. Knowledge of species specific pharmacology can be advantageous for drug discovery, where rats are frequently used as a model system. For human paralogs, we demonstrate a global correlation between sequence identity and the binding of small molecules with equivalent affinity. Our findings provide an initial general model relating small molecule binding and sequence divergence, containing the foundations for a general model to anticipate and predict within-target-family selectivity.     

Prediction of secondary structures of 16S and 23S rRNA fromEscherichia coli

Small and large subunits ofEscherichia coli ribosome have three different rRNAs, the sequences of which are known. However, attempts by three groups to predict secondary structures of 16S and 23S rRNAs have certain common limitations namely, these structures are predicted assuming no interactions among various domains of the molecule and only 40% residues are involved in base pairing as against the experimental observation of 60 % residues in base paired state. Recent experimental studies have shown that there is a specific interaction between naked 16S and 23S rRNA molecules. This is significant because we have observed that the regions (oligonucleotides of length 9–10 residues), in 16S rRNA which are complementary to those in 23S rRNA do not have internal complementary sequences. Therefore, we have developed a simple graph theoretical approach to predict secondary structures of 16S and 23S rRNAs. Our method for model building not only uses complete sequence of 16S or 23S rRNA molecule along with other experimental observations but also takes into account the observation that specific recognition is possible through the complementary sequences between 16S and 23S rRNA molecules and, therefore, these parts of the molecules are not used for internal base pairing. The method used to predict secondary structures is discussed. A typical secondary structure of the complex between 16S and 23S rRNA molecules, obtained using our method, is presented and compared Briefly with earlier model Building studies.     

Single-channel permeability and glycerol affinity of human aquaglyceroporin AQP3

For its fundamental relevance, transport of water and glycerol across the erythrocyte membrane has long been investigated before and after the discovery of aquaporins (AQPs), the membrane proteins responsible for water and glycerol transport. AQP1 is abundantly expressed in the human erythrocyte for maintaining its hydrohomeostasis where AQP3 is also expressed (at a level ~30-folds lower than AQP1) facilitating glycerol transport. This research is focused on two of the remaining questions: How permeable is AQP3 to water? What is the glycerol-AQP3 affinity under near-physiological conditions? Through atomistic modelling and large-scale simulations, we found that AQP3 is two to three times more permeable to water than AQP1 and that the glycerol-AQP3 affinity is approximately 500/M. Using these computed values along with the data from the latest literature on AQP1 and on erythrocyte proteomics, we estimated the water and glycerol transport rates across the membrane of an entire erythrocyte. We used these rates to predict the time courses of erythrocyte swelling-shrinking in response to inward and outward osmotic gradients. Experimentally, we monitored the time course of human erythrocytes when subject to an osmotic or glycerol gradient with light scattering in a stopped-flow spectrometer. We observed close agreement between the experimentally measured and the computationally predicted time courses of erythrocytes, which corroborated our computational conclusions on the AQP3 water-permeability and the glycerol-AQP3 affinity.     

Mapping the energetics of water-protein and water-ligand interactions with the "natural" HINT forcefield: predictive tools for characterizing the roles of water in biomolecules

The energetics and hydrogen bonding pattern of water molecules bound to proteins were mapped by analyzing structural data (resolution better than 2.3A) for sets of uncomplexed and ligand-complexed proteins. Water-protein and water-ligand interactions were evaluated using hydropatic interactions (HINT), a non-Newtonian forcefield based on experimentally determined logP(octanol/water) values. Potential water hydrogen bonding ability was assessed by a new Rank algorithm. The HINT-derived binding energies and Ranks for second shell water molecules were -0.04 kcal mol(-1) and 0.0, respectively, for first shell water molecules -0.38 kcal mol(-1) and 1.6, for active site water molecules -0.45 kcal mol(-1) and 2.3, for cavity water molecules -0.55 kcal mol(-1) and 3.3, and for buried water molecules -0.56 kcal mol(-1) and 4.4. For the last four classes, similar energies indicate that internal and external water molecules interact with protein almost equally, despite different degrees of hydrogen bonding. The binding energies and Ranks for water molecules bridging ligand-protein were -1.13 kcal mol(-1) and 4.5, respectively. This energetic contribution is shared equally between protein and ligand, whereas Rank favors the protein. Lastly, by comparing the uncomplexed and complexed forms of proteins, guidelines were developed for prediction of the roles played by active site water molecules in ligand binding. A water molecule with high Rank and HINT score is unlikely to make further interactions with the ligand and is largely irrelevant to the binding process, while a water molecule with moderate Rank and high HINT score is available for ligand interaction. Water molecule displaced for steric reasons were characterized by lower Rank and HINT score. These guidelines, tested by calculating HINT score and Rank for 50 water molecules bound in the active site of four uncomplexed proteins (for which the structures of the liganded forms were also available), correctly predicted the ultimate roles (in the complex) for 76% of water molecules. Some failures were likely due to ambiguities in the structural data.     

Enantioseparation of phenylglycinol in chiral-modified zeolite HY: a molecular simulation study

A mechanism has been proposed for the separation of valinol enantiomers using a chiral-modified zeolite HY (i.e., zeolite HY containing (+)-(1R;2R)-hydrobenzoin) Molecular modeling of chiral-modified zeolite HY employed in enantioselective separation. Jirapongphan SS, Warzywoda J, Budil DE, Sacco A Jr. Chirality 2007; in press, which accurately predicted the experimentally measured enantioseparation. This methodology has been applied to predict the separation of an enantiomeric pair of phenylglycinol molecules (a precursor in the synthesis of HIV-1 protease inhibitors) using the modified zeolite HY as a CSP. Phenylglycinol and valinol molecules are similar in terms of the presence of polar (i.e., amine and hydroxyl) groups. These functional groups are important in the proposed chiral discrimination. Supercage-based docking simulations yielded an enantioselectivity of 1.3 with (+)-(S)-phenylglycinol molecule better retained in the zeolite. Also, the simulations predicted two binding modes that were the same as those in the valinol system. This suggests that specific structural features (i.e., number and type of polar groups), which generate the hypothesized binding modes, are required in an enantioseparation utilizing the chiral-modified zeolite HY.     

Beyond Profiling: Using ADMET Models to Guide Decisions

ADMET Models, whether in silico or in vitro, are commonly used to ‘profile’ molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here, we will show how models may be used to go ‘beyond profiling’ to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties. To prioritise molecules and chemical series, the success criteria for properties and their relative importance to a project's objective must be defined. Data from models (experimental or predicted) may then be used to assess each molecule's balance of properties against those requirements. However, to make decisions with confidence, the uncertainties in all of the data must also be considered. In silico models encode information regarding the relationship between molecular structure and properties. This is used to predict the property value of a novel molecule. However, further interpretation can yield information on the contributions of different groups in a molecule to the property and the sensitivity of the property to structural changes. Visualising this information can guide the redesign process. In this article, we describe methods to achieve these goals and drive drug‐discovery decisions and illustrate the results with practical examples.     

Statistical and molecular dynamics studies of buried waters in globular proteins

Buried solvent molecules are common in the core of globular proteins and contribute to structural stability. Folding necessitates the burial of polar backbone atoms in the protein core, whose hydrogen-bonding capacities should be satisfied on average. Whereas the residues in alpha-helices and beta-sheets form systematic main-chain hydrogen bonds, the residues in turns, coils and loops often contain polar atoms that fail to form intramolecular hydrogen bonds. The statistical analysis of 842 high resolution protein structures shows that well-resolved, internal water molecules preferentially reside near residues without alpha-helical and beta-sheet secondary structures. These buried waters most often form primary hydrogen bonds to main-chain atoms not involved in intramolecular hydrogen bonds, providing strong evidence that hydrating main-chain atoms is a key structural role of buried water molecules. Additionally, the average B-factor of protein atoms hydrogen-bonded to waters is smaller than that of protein atoms forming intramolecular hydrogen bonds, and the average B-factor of water molecules involved in primary hydrogen bonds with main-chain atoms is smaller than the average B-factor of water molecules involved in secondary hydrogen bonds to protein atoms that form concurrent intramolecular hydrogen bonds. To study the structural coupling between internal waters and buried polar atoms in detail we simulated the dynamics of wild-type FKBP12, in which a buried water, Wat137, forms one side-chain and multiple main-chain hydrogen bonds. We mutated E60, whose side-chain hydrogen bonds with Wat137, to Q, N, S or A, to modulate the multiplicity and geometry of hydrogen bonds to the water. Mutating E60 to a residue that is unable to form a hydrogen bond with Wat137 results in reorientation of the water molecule and leads to a structural readjustment of residues that are both near and distant to the water. We predict that the E60A mutation will result in a significantly reduced affinity of FKBP12 for its ligand FK506. The propensity of internal waters to hydrogen bond to buried polar atoms suggests that ordered water molecules may constitute fundamental structural components of proteins, particularly in regions where alpha-helical or beta-sheet secondary structure is not present.     

Integrating structure-based approaches in generative molecular design

Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.     

Computer-aided prediction of gangrenous and perforating appendicitis.

The clinical details of 100 patients with proved acute appendicitis were compared with those of 100 patients with perforating or gangrenous appendicitis. Twenty features were found to be significantly different between the two groups. This information was incorporated into a computer data base and used in the differential diagnosis of abdominal pain. A program written to predict the probability that gangrene or perforation was present in patients with appendicitis gave a diagnostic accuracy over 91%. A clinical scoring index, which accurately predicted the state of the appendix in 88% of patients, was constructed from the significant differences between the two groups. When clinical scoring or computer analysis predicts a high probability of perforation or gangrene in patients with appendicitis, surgery should be performed without delay.     

Solvent structure in crystals of trypsin determined by X-ray and neutron diffraction.

The solvent structure in orthorhombic crystals of bovine trypsin has been independently determined by X-ray diffraction to 1.35 A resolution and by neutron diffraction to 2.1 A resolution. A consensus model of the water molecule positions was obtained using oxygen positions identified in the electron density map determined by X-ray diffraction, which were verified by comparison to D2O-H2O difference neutron scattering density. Six of 184 water molecules in the X-ray structure, all with B-factors greater than 50 A2, were found to be spurious after comparison with neutron results. Roughly two-thirds of the water of hydration expected from thermodynamic data for proteins was localized by neutron diffraction; approximately one-half of the water of hydration was located by X-ray diffraction. Polar regions of the protein are well hydrated, and significant D2O-H2O difference density is seen for a small number of water molecules in a second shell of hydration. Hydrogen bond lengths and angles calculated from unconstrained refinement of water positions are distributed about values typically seen in small molecule structures. Solvent models found in seven other bovine trypsin and trypsinogen and rat trypsin structures determined by X-ray diffraction were compared. Internal water molecules are well conserved in all trypsin structures including anionic rat trypsin, which is 65% homologous to bovine trypsin. Of the 22 conserved waters in trypsin, 19 were also found in trypsinogen, suggesting that they are located in regions of the apoprotein that are structurally conserved in the transition to the mature protein. Seven waters were displaced upon activation of trypsinogen. Water structure at crystal contacts is not generally conserved in different crystal forms. Three groups of integral structural water molecules are highly conserved in all solvent structures, including a spline of water molecules inserted between two beta-strands, which may resemble an intermediate in the formation of beta sheets during the folding of a protein.     

Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity.