Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery

Fragment-based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high-affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency.     

Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase

The identification of hot spots, i.e., binding regions that contribute substantially to the free energy of ligand binding, is a critical step for structure-based drug design. Here we present the application of two fragment-based methods to the detection of hot spots for DJ-1 and glucocerebrosidase (GCase), targets for the development of therapeutics for Parkinson’s and Gaucher’s diseases, respectively. While the structures of these two proteins are known, binding information is lacking. In this study we employ the experimental multiple solvent crystal structures (MSCS) method and computational fragment mapping (FTMap) to identify regions suitable for the development of pharmacological chaperones for DJ-1 and GCase. Comparison of data derived via MSCS and FTMap also shows that FTMap, a computational method for the identification of fragment binding hot spots, is an accurate and robust alternative to the performance of expensive and difficult crystallographic experiments.     

Diverse fragment clustering and water exclusion identify protein hot spots

Simulated annealing of chemical potential located the highest affinity positions of eight organic probes and water on eight static structures of hen egg white lysozyme (HEWL) in various conformational states. In all HELW conformations, a diverse set of organic probes clustered in the known binding site (hot spot). Fragment clusters at other locations were excluded by tightly-bound waters so that only the hot-spot cluster remained in each case. The location of the hot spot was correctly predicted irrespective of the protein conformation and without accounting for protein flexibility during the simulations. Any one of the static structures could have been used to locate the hot spot. A site on a protein where a diversity of organic probes is calculated to cluster, but where water specifically does not bind, identifies a potential small-molecule binding site or protein-protein interaction hot spot.     

Focused grid‐based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein‐protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near‐native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands. © 2016 Wiley Periodicals, Inc.     

Hot spot occlusion from bulk water: a comprehensive study of the complex between the lysozyme HEL and the antibody FVD1.3

Alanine scanning of protein-protein interfaces has shown that there are some residues in the protein-protein interfaces, responsible for most of the binding free energy, which are called hot spots. Hot spots tend to exist in densely packed central clusters, and a hypothesis has been proposed that considers that inaccessibility to the solvent must be a necessary condition to define a residue as a binding hot spot. This O-ring hypothesis is mainly based on the analysis of the accessible surface area (ASA) of 23 static, crystallographic structures of protein complexes. It is known, however, that protein flexibility allows for temporary exposures of buried interfacial groups, and even though the ASA provides a general trend of the propensity for hydration, protein/solvent-specific interactions or hydrogen bonding cannot be considered here. Therefore, a microscopic level, atomistic picture of hot spot solvation is needed to support the O-ring hypothesis. In this study, we began by applying a computational alanine-scanning mutagenesis technique, which reproduces the experimental results and allows for decomposing the binding free energy difference in its different energetic factors. Subsequently, we calculated the radial distribution function and residence times of the water molecules near the hot/warm spots to study the importance of the water environment around those energetically important amino acid residues. This study shows that within a flexible, dynamic protein framework, the warm/hot spot residues are, indeed, kept sheltered from the bulk solvent during the whole simulation, which allows a better interacting microenvironment.     

Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces

In the context of protein-protein interactions, the term "hot spot" refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as determined by alanine scanning mutagenesis. In contrast, in pharmaceutical research, a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery. Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small molecules. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or experimental fragment screening are almost always themselves hot spot residues as defined by alanine scanning experiments. Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however. In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are additional topological requirements to be a hot spot for small molecule binding. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by experimental or computational fragment screening.     

Effect of the Solute Cavity on the Solvation Energy and its Derivatives within the Framework of the Gaussian Charge Scheme

The treatment of the solvation charges using Gaussian functions in the polarizable continuum model results in a smooth potential energy surface. These charges are placed on top of the surface of the solute cavity. In this article, we study the effect of the solute cavity (van der Waals-type or solvent-excluded surface-type) using the Gaussian charge scheme within the framework of the conductor-like polarizable continuum model on (a) the accuracy and computational cost of the self-consistent field (SCF) energy and its gradient and on (b) the calculation of free energies of solvation. For that purpose, we have considered a large set of systems ranging from few atoms to more than 200 atoms in different solvents. Our results at the DFT level using the B3LYP functional and the def2-TZVP basis set show that the choice of the solute cavity does neither affect the accuracy nor the cost of calculations for small systems (< 100 atoms). For larger systems, the use of a vdW-type cavity is recommended, as it prevents small oscillations in the gradient (present when using a SES-type cavity), which affect the convergence of the SCF energy gradient. Regarding the free energies of solvation, we consider a solvent-dependent probe sphere to construct the solvent-accessible surface area required to calculate the nonelectrostatic contribution to the free energy of solvation. For this part, our results for a large set of organic molecules in different solvents agree with available experimental data with an accuracy lower than 1 kcal/mol for both polar and nonpolar solvents.     

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.     

Aromatic superclusters from all-metal aromatic and antiaromatic monomers, [Al4]2- and [Al4]4-

Calculations on the structures of dimers of all-metal aromatic and anti-aromatic molecules such as (Al4(2-)) and (Al4(4-)) reveal that, unlike their organic counterparts such as benzene and cyclobutadiene which form pi-stacked complexes, these molecules form new clusters with no reminiscence of the original units. These clusters have a very large binding energy and can be further stabilized through charge-balance by counterions and solvents.     

Unraveling the importance of protein-protein interaction: application of a computational alanine-scanning mutagenesis to the study of the IgG1 streptococcal protein G (C2 fragment) complex

Alanine-scanning mutagenesis of protein-protein interfacial residues is a very important process for rational drug design. In this study, we have used the improved MM-PBSA approach that combining molecular mechanics and continuum solvent permits one to calculate the free energy differences through alanine mutation. To identify the binding determinants of the complex formed between the IgG1 (immunoglobulin-binding protein G) and protein G, we have extended the experimental alanine scanning mutagenesis study to both proteins of this complex and, therefore, to all interfacial residues of this binding complex. As a result, we present new residues that can be characterized as warm spots and, therefore, are important for complex formation. We have further increased the understanding of the functionality of this improved computational alanine-scanning mutagenesis approach testing its sensitivity to a protein-protein complex with an interface made up of residues mainly polar. In this study, we also have improved the method for the detection of an important amino acid residue that frequently constitutes a hot spot--tryptophan.     

On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy

Implicit solvent hydration free energy models are an important component of most modern computational methods aimed at protein structure prediction, binding affinity prediction, and modeling of conformational equilibria. The nonpolar component of the hydration free energy, consisting of a repulsive cavity term and an attractive van der Waals solute-solvent interaction term, is often modeled using estimators based on the solvent exposed solute surface area. In this paper, we analyze the accuracy of linear surface area models for predicting the van der Waals solute-solvent interaction energies of native and non-native protein conformations, peptides and small molecules, and the desolvation penalty of protein-protein and protein-ligand binding complexes. The target values are obtained from explicit solvent simulations and from a continuum solvent van der Waals interaction energy model. The results indicate that the standard surface area model, while useful on a coarse-grained scale, may not be accurate or transferable enough for high resolution modeling studies of protein folding and binding. The continuum model constructed in the course of this study provides one path for the development of a computationally efficient implicit solvent nonpolar hydration free energy estimator suitable for high-resolution structural and thermodynamic modeling of biological macromolecules.     

Nonorthogonal Tight-binding Study of the Geometries and Electronic Properties of Ge_n(n=2—20)Clusters

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Protein–protein recognition: a computational mutagenesis study of the MDM2–P53 complex

Protein P53 is involved in more than 50% of the human cancers and the P53–MDM2 complex is a target for anticancer drug design. It is possible to engineer small P53 mimics that would be expected to disrupt the P53–MDM2 complex, and release P53 to initiate cell-cycle arrest or apoptosis. These small peptides should bind to the functional epitopes of the protein–protein interface, and prevent the interaction between P53 and MDM2. Here, we apply an improved computational alanine scanning mutagenesis method, which allows the determination of the hot spots present in both monomers, P53 and MDM2, of three protein complexes (the P53-binding domain of human MDM2, its analogue from Xenopus laevis, and the structure of human MDM2 in complex with an optimized P53 peptide). The importance of the hydrogen bonds formed by the protein backbone has been neglected due to the difficulty of measuring experimentally their contribution to the binding free energy. In this study we present a computational approach that allows the estimation of the contribution to the binding free energy of the C=O and N–H groups in the backbone of the P53 and MDM2 proteins. We have noticed that the hydrogen bond between the HE1 atom of the hot spot Trp23 and the O atom of the residue Leu54, as well as the NH-pi hydrogen bond between the Ile57 and Met58 were observed in the Molecular dynamics simulation, and their contribution to the binding free energy measured. This study not only shows the reliability of the computational mutagenesis method to detect hot spots but also demonstrates an excellent correlation between the quantitative calculated binding free energy contribution of the C=O and N–H backbone groups of the interfacial residues and the qualitative values expected for this kind of interaction. The study also increases our understanding of the P53–MDM2 interaction.     

Do active site conformations of small ligands correspond to low free-energy solution structures?

We compare the low free energy structures of ten small, polar ligands in solution to their conformations in their respective receptor active sites. The solution conformations are generated by a systematic search and the free energies of representative structures are computed with a continuum solvation model. Based on the values of torsion angles, we find little similarity between low energy solution structures of small ligands and their active site conformations. However, in nine out of ten cases, the positions of 'anchor points' (key atoms responsible for tight binding) in the lowest energy solution structures are very similar to the positions of these atoms in the active site conformations. A metric that more closely captures the essentials of binding supports the basic premise underlying pharmacophore mapping, namely that active site conformations of small flexible ligands correspond to their low energy structures in solution. This work supports the efforts of building pharmacophore models based on the information present in solution structures of small isolated ligands.     

Improved mapping of protein binding sites

Computational mapping methods place molecular probes – small molecules or functional groups – on a protein surface in order to identify the most favorable binding positions by calculating an interaction potential. Mapping is an important step in a number of flexible docking and drug design algorithms. We have developed improved algorithms for mapping protein surfaces using small organic molecules as molecular probes. The calculations reproduce the binding of eight organic solvents to lysozyme as observed by NMR, as well as the binding of four solvents to thermolysin, in good agreement with x-ray data. Application to protein tyrosine phosphatase 1B shows that the information provided by the mapping can be very useful for drug design. We also studied why the organic solvents bind in the active site of proteins, in spite of the availability of alternative pockets that can very tightly accommodate some of the probes. A possible explanation is that the binding in the relatively large active site retains a number of rotational states, and hence leads to smaller entropy loss than the binding elsewhere else. Indeed, the mapping reveals that the clusters of the ligand molecules in the protein's active site contain different rotational-translational conformers, which represent different local minima of the free energy surface. In order to study the transitions between different conformers, reaction path and molecular dynamics calculations were performed. Results show that most of the rotational states are separated by low free energy barriers at the experimental temperature, and hence the entropy of binding in the active site is expected to be high.     

Application of molecular dynamics simulations in molecular property prediction II: diffusion coefficient

In this work, we have evaluated how well the general assisted model building with energy refinement (AMBER) force field performs in studying the dynamic properties of liquids. Diffusion coefficients (D) have been predicted for 17 solvents, five organic compounds in aqueous solutions, four proteins in aqueous solutions, and nine organic compounds in nonaqueous solutions. An efficient sampling strategy has been proposed and tested in the calculation of the diffusion coefficients of solutes in solutions. There are two major findings of this study. First of all, the diffusion coefficients of organic solutes in aqueous solution can be well predicted: the average unsigned errors and the root mean square errors are 0.137 and 0.171 × 10(-5) cm(-2) s(-1), respectively. Second, although the absolute values of D cannot be predicted, good correlations have been achieved for eight organic solvents with experimental data (R(2) = 0.784), four proteins in aqueous solutions (R(2) = 0.996), and nine organic compounds in nonaqueous solutions (R(2) = 0.834). The temperature dependent behaviors of three solvents, namely, TIP3P water, dimethyl sulfoxide, and cyclohexane have been studied. The major molecular dynamics (MD) settings, such as the sizes of simulation boxes and with/without wrapping the coordinates of MD snapshots into the primary simulation boxes have been explored. We have concluded that our sampling strategy that averaging the mean square displacement collected in multiple short-MD simulations is efficient in predicting diffusion coefficients of solutes at infinite dilution.     

Comprehensive identification of the binding sites of cisplatin in hen egg white lysozyme

Platinum drugs have become one of the most important kinds of chemotherapy agents, and the interactions of these drugs with proteins play very important roles in their side effects and drug resistance. However, it is still a challenge to determine the binding sites of platinum drugs in proteins with multiple disulfide bonds and stable three-dimensional structures using mass spectrometry. Here, the interaction between cisplatin and hen egg white lysozyme (HEWL), a multi-disulfide-bond-containing protein with a stable three-dimensional structure, was investigated using Fourier transform ion cyclotron resonance mass spectrometry. Typical disulfide bond reduction with dithiothreitol/tris(2-carboxyethyl)phosphine before trypsin digestion destroyed the binding of cisplatin to HEWL, and no platination sites were found. Efficient trypsin digestion methods for HEWL–cisplatin adducts were developed to avoid the loss of platinum binding to protein. At 55 °C, platinated HEWL was digested directly by trypsin in 6 h, and multiple platinated peptides were observed. In 60 % acetonitrile, the digestion time of platinated HEWL was shortened to 2 h, and most of the platinated peptides were observed. In addition, the reduction of the disulfide bonds of HEWL greatly accelerated the reaction between HEWL and cisplatin, and the potential binding sites of cisplatin in reduced HEWL could be easily recognized. On the basis of the above-mentioned methods, multiple binding sites of cisplatin in HEWL were first identified by mass spectrometry.

5-巯基-1, 3, 4-噻二唑-2-硫酮微溶剂团簇的光谱和理论计算研究

微溶剂作用(即溶剂化过程)广泛存在于所有物理、 化学和生命过程中. 在液相化学反应体系中, 几乎是一切化学反应的基础. 通过傅里叶变换拉曼光谱(FT-Raman)并结合密度泛函理论(DFT), 表征了固态5-巯基- 1, 3, 4-噻二唑-2-硫酮(MTT)的结构, 并进一步确认了MTT在乙腈、 甲醇和水中微溶剂团簇的大小和氢键位点. 通过探究MTT在不同溶剂及pH条件下的紫外-可见吸收光谱(UV-Vis), 结合含时密度泛函理论(TD-DFT)计算, 揭示了溶剂和pH对MTT电子跃迁带的影响, 进一步解释了其光谱位移. 结合能量计算可以得出, MTT分别与1个乙腈、 2个甲醇和2个水分子形成MTT(CH₃CN), MTT(CH₃OH)₂ 和MTT(H₂O)₂团簇.     

Binding free energies in the SAMPL5 octa-acid host–guest challenge calculated with DFT-D3 and CCSD(T)

We have tried to calculate the free energy for the binding of six small ligands to two variants of the octa-acid deep cavitand host in the SAMPL5 blind challenge. We employed structures minimised with dispersion-corrected density-functional theory with small basis sets and energies were calculated using large basis sets. Solvation energies were calculated with continuum methods and thermostatistical corrections were obtained from frequencies calculated at the HF-3c level. Care was taken to minimise the effects of the flexibility of the host by keeping the complexes as symmetric and similar as possible. In some calculations, the large net charge of the host was reduced by removing the propionate and benzoate groups. In addition, the effect of a restricted molecular dynamics sampling of structures was tested. Finally, we tried to improve the energies by using the DLPNO–CCSD(T) approach. Unfortunately, results of quite poor quality were obtained, with no correlation to the experimental data, systematically too positive affinities (by ~50 kJ/mol) and a mean absolute error (after removal of the systematic error) of 11–16 kJ/mol. DLPNO–CCSD(T) did not improve the results, so the accuracy is not limited by the energy function. Instead, four likely sources of errors were identified: first, the minimised structures were often incorrect, owing to the omission of explicit solvent. They could be partly improved by performing the minimisations in a continuum solvent with four water molecules around the charged groups of the ligands. Second, some ligands could bind in several different conformations, requiring sampling of reasonable structures. Third, there is an indication the continuum-solvation model has problems to accurately describe the binding of both the negatively and positively charged guest molecules. Fourth, different methods to calculate the thermostatistical corrections gave results that differed by up to 30 kJ/mol and there is an indication that HF-3c overestimates the entropy term. In conclusion, it is a challenge to calculate binding affinities for this octa-acid system with quantum–mechanical methods.