Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes

Despite its central role in structure based drug design the determination of the binding mode (position, orientation and conformation in addition to protonation and tautomeric states) of small heteromolecular ligands in protein:ligand complexes based on medium resolution X-ray diffraction data is highly challenging. In this perspective we demonstrate how a combination of molecular dynamics simulations and free energy (FE) calculations can be used to correct and identify thermodynamically stable binding modes of ligands in X-ray crystal complexes. The consequences of inappropriate ligand structure, force field and the absence of electrostatics during X-ray refinement are highlighted. The implications of such uncertainties and errors for the validation of virtual screening and fragment-based drug design based on high throughput X-ray crystallography are discussed with possible solutions and guidelines.     

FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function

The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.     

BALLDock/SLICK: a new method for protein-carbohydrate docking

Protein-ligand docking is an essential technique in computer-aided drug design. While generally available docking programs work well for most drug classes, carbohydrates and carbohydrate-like compounds are often problematic for docking. We present a new docking method specifically designed to handle docking of carbohydrate-like compounds. BALLDock/SLICK combines an evolutionary docking algorithm for flexible ligands and flexible receptor side chains with carbohydrate-specific scoring and energy functions. The scoring function has been designed to identify accurate ligand poses, while the energy function yields accurate estimates of the binding free energies of these poses. On a test set of known protein-sugar complexes we demonstrate the ability of the approach to generate correct poses for almost all of the structures and achieve very low mean errors for the predicted binding free energies.     

Statistical potential for modeling and ranking of protein-ligand interactions

Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF(1)) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 ? from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScore(CSD) and ITScore/SE and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp) and the LigScore Web server (http://salilab.org/ligscore/).     

Pattern recognition strategies for molecular surfaces: III. Binding site prediction with a neural network

An algorithm for the identification of possible binding sites of biomolecules, which are represented as regions of the molecular surface, is introduced. The algorithm is based on the segmentation of the molecular surface into overlapping patches as described in the first article of this series.1 The properties of these patches (calculated on the basis of physical and chemical properties) are used for the analysis of the molecular surfaces of 7821 proteins and protein complexes. Special attention is drawn to known protein binding sites. A binding site identification algorithm is realized on the basis of the calculated data using a neural network strategy. The neural network is able to classify surface patches as protein-protein, protein-DNA, protein-ligand, or nonbinding sites. To show the capability of the algorithm, results of the surface analysis and the predictions are presented and discussed with representative examples.     

Selectivity in supramolecular host-guest complexes

The background of possible selectivity-affinity correlations and their limitations is reviewed, with typical crown ether and cryptand complexes, ionic associations, hydrogen bonded complexes and complexes driven by van der Waals, stacking or hydrophobic interactions, with some additional topics including associations based on metal coordination as supplementary material. This tutorial review is addressed to students and researchers interested in molecular recognition, and relates to the design of sensors, of discriminators for separation processes, of supramolecular devices and of drug compounds. A theoretical analysis of selectivity in supramolecular host-guest complexes, defined as a difference in binding free energies for structurally related guests, as a function of total binding free energy shows that for certain types of intermolecular interactions one may observe a correlation between selectivity and affinity. Such correlation fails however if the selectivity is due to additional interactions at a secondary binding sites, which is expected in complexes with anisotropic guest molecules. Several clear examples of theoretically expected selectivity-affinity correlations are found. The influence of reaction conditions on the experimentally observed selectivity, defined as a difference in complexation degrees with different guests in the presence of added receptor, is illustrated. The importance of often neglected solvent effects on selectivity is exemplified with ionophore and hydrogen bonded complexes.     

Customizing scoring functions for docking

Empirical scoring functions used in protein-ligand docking calculations are typically trained on a dataset of complexes with known affinities with the aim of generalizing across different docking applications. We report a novel method of scoring-function optimization that supports the use of additional information to constrain scoring function parameters, which can be used to focus a scoring function’s training towards a particular application, such as screening enrichment. The approach combines multiple instance learning, positive data in the form of ligands of protein binding sites of known and unknown affinity and binding geometry, and negative (decoy) data of ligands thought not to bind particular protein binding sites or known not to bind in particular geometries. Performance of the method for the Surflex-Dock scoring function is shown in cross-validation studies and in eight blind test cases. Tuned functions optimized with a sufficient amount of data exhibited either improved or undiminished screening performance relative to the original function across all eight complexes. Analysis of the changes to the scoring function suggest that modifications can be learned that are related to protein-specific features such as active-site mobility.     

Bimodal Complexations of Steroids with Cyclodextrins by a Flexible Docking Algorithm

A flexible docking algorithm was developed for studying the inclusion complexes of cyclodextrins with steroids in aqueous solution by an optimization method and an empirical function. The function is used to estimate the binding free energy including intermolecular interaction energy, the conformational energy change, and the solvation energy. The bimodal complexations of twelve steroids in β- and γ-CD cavities were studied by the algorithm. For the two orientations of the guests in the cavity, the possible binding regions were investigated, and the lowest energies for the inclusion complexes in the binding regions were obtained. The stability constant for each orientation was estimated from the optimized energy components by a quantitative model. Therefore, the preferential orientations of the guests were found out from the results finally.     

Bimodal Complexations of Steroids with Cyclodextrins by a Flexible Docking Algorithm

A flexible docking algorithm was developed for studying the inclusion complexes of cyclodextrins with steroids in aqueous solution by an optimization method and an empirical function. The function is used to estimate the binding free energy including intermolecular interaction energy, the conformational energy change, and the solvation energy. The bimodal complexations of twelve steroids in β- and γ-CD cavities were studied by the algorithm. For the two orientations of the guests in the cavity, the possible binding regions were investigated, and the lowest energies for the inclusion complexes in the binding regions were obtained. The stability constant for each orientation was estimated from the optimized energy components by a quantitative model. Therefore, the preferential orientations of the guests were found out from the results finally.This revised version was published online in July 2005 with a corrected issue number.     

Hydrogen-bonded assembly and binding affinity of the multi-point acceptor and isophthalic acid

Supermolecular complexes formed by oligophenyleneethynylene derivatives and isophthalic acid were studied using AM1 method to obtain binding energy. Electronic spectra and IR spectra of the complexes were calculated by INDO/CIS and AM1 methods based on AM1 geometries. Results indicated that the dimer could be formed by the monomers via hydrogen bonding because of the negative binding energy. Binding energy of the complexes was affected by electronegativity and steric effects of the substituents. The first UV absorptions and IR frequencies of N-H bonds of the complexes were both red-shifted compared with those of the monomers. The complexes could bind small molecules via hydrogen bonds, resulting in the change in UV absorptions and an increase in IR frequencies of N-H bonds.     

Theoretical studies on the binding affinities of β-cyclodextrin to small molecules and monosaccharides

Equilibrium geometries and electronic structures of complexes between β-cyclodextrin (β-CD) and some small molecules as well as monosaccharides were investigated by Austin Model 1 (AM1) to obtain binding energy of the complexes. It was indicated that β-CD could bind the structurally similar solvent molecules and monosaccharides because of the negative binding energy of the complexes, and especially could show the chiral binding ability to monosaccharides with more hydroxyl groups, due to its chiral characteristics. The complexes were stabilized by the hydrogen bonding between β-CD and guests. Based on the AM1 optimized geometries, the IR spectra were calculated by AM1 method. Vibration frequencies of O-H bonds in the guests were red-shifted owing to the weakening of the O-H bonds with the formation of the complexes.     

Flexible protein‐protein docking based on Best‐First search algorithm

We developed a new high resolution protein‐protein docking method based on Best‐First search algorithm that loosely imitates protein‐protein associations. The method operates in two stages: first, we perform a rigid search on the unbound proteins. Second, we search alternately on rigid and flexible degrees of freedom starting from multiple configurations from the rigid search. Both stages use heuristics added to the energy function, which causes the proteins to rapidly approach each other and remain adjacent, while optimizing on the energy. The method deals with backbone flexibility explicitly by searching over ensembles of conformations generated before docking. We ran the rigid docking stage on 66 complexes and grouped the results into four classes according to evaluation criteria used in Critical Assessment of Predicted Interactions (CAPRI; “high,” “medium,” “acceptable,” and “incorrect”). Our method found medium binding conformations for 26% of the complexes and acceptable for additional 44% among the top 10 configurations. Considering all the configurations, we found medium binding conformations for 55% of the complexes and acceptable for additional 39% of the complexes. Introducing side‐chains flexibility in the second stage improves the best found binding conformation but harms the ranking. However, introducing side‐chains and backbone flexibility improve both the best found binding conformation and the best found conformation in the top 10. Our approach is a basis for incorporating multiple flexible motions into protein‐protein docking and is of interest even with the current use of a simple energy function. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Are AM1 ligand‐protein binding enthalpies good enough for use in the rational design of new drugs?

We have examined the performance of semiempirical quantum mechanical methods in solving the problem of accurately predicting protein-ligand binding energies and geometries. Firstly, AM1 and PM3 geometries and binding enthalpies between small molecules that simulate typical ligand-protein interactions were compared with high level quantum mechanical techniques that include electronic correlation (e.g., MP2 or B3LYP). Species studied include alkanes, aromatic systems, molecules including groups with hypervalent sulfur or with donor or acceptor hydrogen bonding capability, as well as ammonium or carboxylate ions. B3LYP/6-311+G(2d,p) binding energies correlated very well with the BSSE corrected MP2/6-31G(d) values. AM1 binding enthalpies also showed good correlation with MP2 values, and their systematic deviation is acceptable when enthalpies are used for the comparison of interaction energies between ligands and a target. PM3 otherwise gave erratic energy differences in comparison to the B3LYP or MP2 approaches. As one would expect, the geometries of the binding complexes showed the known limitations of the semiempirical and DFT methods. AM1 calculations were subsequently applied to a test set consisting of "real" protein active site-ligand complexes. Preliminary results indicate that AM1 could be a valuable tool for the design of new drugs using proteins as templates. This approach also has a reasonable computational cost. The ligand-protein X-ray structures were reasonably reproduced by AM1 calculations and the corresponding AM1 binding enthalpies are in agreement with the results from the "small molecules" test set.     

蛋白质相互作用: 界面分析, 结合自由能计算与相互作用设计

蛋白质相互作用在生命活动中起着重要作用. 研究蛋白质间相互作用的本质有助于了解生命活动中这些基本单元的作用. 本文主要综述了近期蛋白质相互作用研究的进展, 包括蛋白质相互作用界面的基本性质, 蛋白质结合自由能的计算方法, 不同相互作用在蛋白质结合/解离中的角色和差异, 以及上述知识在蛋白质相互作用设计中的应用. 蛋白质相互作用界面的特性, 例如界面大小、保守性以及结构的动态性质, 使得具有生物功能的蛋白质相互作用界面区别于非特异性的晶体堆积界面. 生物功能界面的一个重要结构特征是界面上存在着关键残基以及相对独立的相互作用模块. 利用多种方法, 如MM-PBSA、统计平均势以及不同的相互作用自由能模型, 可以在不同的精度上计算蛋白质相互作用自由能. 利用蛋白质相互作用界面的特点, 从不同的角度进行蛋白质相互作用对的设计与改造, 近年来已经有了不少成功的例子, 但还存在着很大的挑战. 我们认为在今后的蛋白质相互作用设计中, 考虑各种因素对蛋白质结合与解离的动力学过程的影响将有助于提高人类控制蛋白质相互作用的能力.     

Comparative evaluation of MMPBSA and XSCORE to compute binding free energy in XIAP-peptide complexes

Evaluation of binding free energy in receptor-ligand complexes is one of the most important challenges in theoretical drug design. Free energy is directly correlated to the thermodynamic affinity constant, and, as a first step in druglikeness, a lead compound must have this constant in the range of micro- to nanomolar activity. Many efforts have been made to calculate it by rigorous computational approaches, such as free energy perturbation or linear response approximation. However, these methods are still computationally expensive. We focus our work on XIAP, an antiapoptotic protein whose inhibition can lead to new drugs against cancer disease. We report here a comparative evaluation of two completely different methodologies to estimate binding free energy, MMPBSA (a force field based function) and XSCORE (an empirical scoring function), in seven XIAP-peptide complexes using a representative set of structures generated by previous molecular dynamics simulations. Both methods are able to predict the experimental binding free energy with acceptable errors, but if one needs to identify slight differences upon binding, MMPBSA performs better, although XSCORE is not a bad choice taking into account the low computational cost of this method.     

Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function

The HYDE scoring function consistently describes hydrogen bonding, the hydrophobic effect and desolvation. It relies on HYdration and DEsolvation terms which are calibrated using octanol/water partition coefficients of small molecules. We do not use affinity data for calibration, therefore HYDE is generally applicable to all protein targets. HYDE reflects the Gibbs free energy of binding while only considering the essential interactions of protein-ligand complexes. The greatest benefit of HYDE is that it yields a very intuitive atom-based score, which can be mapped onto the ligand and protein atoms. This allows the direct visualization of the score and consequently facilitates analysis of protein-ligand complexes during the lead optimization process. In this study, we validated our new scoring function by applying it in large-scale docking experiments. We could successfully predict the correct binding mode in 93% of complexes in redocking calculations on the Astex diverse set, while our performance in virtual screening experiments using the DUD dataset showed significant enrichment values with a mean AUC of 0.77 across all protein targets with little or no structural defects. As part of these studies, we also carried out a very detailed analysis of the data that revealed interesting pitfalls, which we highlight here and which should be addressed in future benchmark datasets.     

A Molecular‐Mechanics Approach for the Prediction of the Geometry of High‐Spin FeIII Complexes with Oxygen and Nitrogen as Coordinating Atoms

A method for the prediction of geometries and the de novo design of oligodentate ligands for octahedral high‐spin Fe^III complexes with chemically diverse coordinating functions is described. Based on a set of 23 complexes with two nitrogens and four oxygens as coordinating atoms, a computational method was elaborated that describes and predicts the geometries of high‐spin Fe^III complexes, including small variations in bond length and angles. The method uses partial atomic charges of the ligand, which are obtained from ab initio calculations, and empirically derived angular and dihedral constraints, which are added to a molecular‐mechanics force field. Conformational analyses of the complex geometries were performed. The method was iteratively optimized by fitting calculated geometries into the corresponding crystal structures of the Fe^III complexes. Three representative examples of calculated structures superimposed on the crystal structure are shown to illustrate the accuracy of the method.     

New angle‐dependent potential energy function for backbone–backbone hydrogen bond in protein–protein interactions

Backbone–backbone hydrogen bonds (BBHBs) are one of the most abundant interactions at the interface of protein–protein complex. Here, we propose an angle‐dependent potential energy function for BBHB based on density functional theory (DFT) calculations and the operation of a genetic algorithm to find the optimal parameters in the potential energy function. The angular part of the energy funtion is assumed to be the product of the power series of sine and cosine functions with respect to the two angles associated with BBHB. Two radial functions are taken into account in this study: Morse and Leonard‐Jones 12‐10 potential functions. Of these two functions under consideration, the former is found to be more accurate than the latter in terms of predicting the binding energies obtained from DFT calculations. The new HB potential function also compares well with the knowledge‐based potential derived by applying Boltzmann statistics for a variety of protein–protein complexes in protein data bank. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Direct detection of structurally resolved dynamics in a multiconformation receptor-ligand complex

Structure-based drug design relies on static protein structures despite significant evidence for the need to include protein dynamics as a serious consideration. In practice, dynamic motions are neglected because they are not understood well enough to model, a situation resulting from a lack of explicit experimental examples of dynamic receptor-ligand complexes. Here, we report high-resolution details of pronounced ~1 ms time scale motions of a receptor-small molecule complex using a combination of NMR and X-ray crystallography. Large conformational dynamics in Escherichia coli dihydrofolate reductase are driven by internal switching motions of the drug-like, nanomolar-affinity inhibitor. Carr-Purcell-Meiboom-Gill relaxation dispersion experiments and NOEs revealed the crystal structure to contain critical elements of the high energy protein-ligand conformation. The availability of accurate, structurally resolved dynamics in a protein-ligand complex should serve as a valuable benchmark for modeling dynamics in other receptor-ligand complexes and prediction of binding affinities.