CLiBE: a database of computed ligand binding energy for ligand-receptor complexes

Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand-receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand-receptor interaction energy is developed from known ligand-receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand-receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.     

The emerging field of chemo- and pharmacoproteomics

The emerging field of chemo- and pharmacoproteomics studies the mechanisms of action of bioactive molecules in a systems pharmacology context. In contrast to traditional drug discovery, pharmacoproteomics integrates the mechanism of a drug's action, its side effects including toxicity, and the discovery of new drug targets in a single approach. Thus, it determines early favorable (e.g. multiple kinase target in cancer drugs) and unfavorable (e.g. side effects) polypharmacology. Target profiling is accomplished using either active site-labeling probes or immobilized drugs. This strategy identifies direct targets and has in fact enabled even the determination of binding curves and half maximum inhibitory concentrations of these targets. In addition, the enrichment greatly reduces the complexity of the proteome to be analyzed by quantitative MS. Complementary to these approaches, global proteomics profiling studying drug treatement-induced changes in protein expression levels and/or post-translational modification status have started to become possible mostly due to significant improvements in instrumentation. Particularly, when using multidimensional separations, a considerable proteome depth of up to 10 000 proteins can be achieved with current state-of-the-art mass spectrometers and bioinformatics tools. In summary, chemo- and pharmacoproteomics has already contributed significantly to the identification of novel drug targets and their mechanisms of action(s). Aided by further technological advancements, this interdisciplinary approach will likely be used more broadly in the future.     

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.     

Evaluation of various inverse docking schemes in multiple targets identification

The lack of accurate and efficient methods for target identification has been the bottleneck in drug discovery. In recent years, inverse docking has been applied as an efficient method in target identification, and several specific inverse docking strategies have been employed in academic and industrial researches. However, the effectiveness of these docking strategies in multiple targets identification is unclear. In this study, five inverse docking schemes were evaluated to find out the most effective approach in multiple targets identification. A target database containing a highly qualified dataset that is composed of 1714 entries from 1594 known drug targets covering 18 biochemical functions was collected as a testing pool for inverse docking. The inverse docking engines including GOLD, FlexX, Tarfisdock and two in-house target search schemes TarSearch-X and TarSearch-M were evaluated by eight multiple target systems in the dataset. The results show that TarSearch-X is the most effective method in multiple targets identification and validation among these five schemes, and the effectiveness of GOLD in multiple targets identification is also acceptable. Moreover, these two inverse docking strategies will also be helpful in predicting the undesirable effects of drugs, such as toxicity.     

Single-coordinate-driving method for molecular docking: application to modeling of guest inclusion in cyclodextrin

An extension of the computer program CICADA has been developed that allows us to use the single-coordinate-driving (SCD) method for flexible molecular docking. The docking procedure is composed of three independent space rotations, three independent translations, and the torsions selected by the user. One of the coordinates is driven; the other coordinates are relaxed. This procedure follows low-energy wells on the potential energy surface of the entire system. The program allows us to dock more than one ligand molecule to the receptor. We ran two test examples, docking N,N-dimethylformamide into alpha-cyclodextrin and R-phenoxypropionic acid into beta-cyclodextrin. The test examples showed that the SCD approach is able to overcome high-energy barriers and to cover the entire box within which the search is performed. The limitations of molecular dynamics docking in comparison with our approach also are discussed. The philosophy of the newly developed approach is not only to find the best dock for the receptor-ligand(s) system, but also to describe all the important binding modes and provide a good starting point for studying the dynamics within the cavity during the docking process.     

Protocol for fast screening of multi-target drug candidates: Application to Alzheimer’s disease

The treatment of many diseases may require drugs that are capable to attack multiple targets simultaneously. Obviously, the virtual screening of multi-target drug candidates is much more time consuming compared to the single-target case. This, in particular, concerns the last step of virtual screening where the binding free energy is computed by conventional molecular dynamics simulation. To overcome this difficulty we propose a simple protocol which is relied on the fast steered molecular dynamics simulation and on available experimental data on binding affinity of reference ligand to a given target. Namely, first we compute non-equilibrium works generated during pulling ligands from the binding site using the steered molecular dynamics method. Then as top leads we choose only those compounds that have the non-equilibrium work larger than that of a reference compound for which the binding free energy has been already known from experiment.Despite many efforts no cures for AD (Alzheimer’s disease) have been found. One of possible reasons for this failure is that drug candidates were developed for a single target, while there are exist many possible pathways to AD. Applying our new protocol to five targets including amyloid beta fibril, peroxisome proliferator-activated receptor γ, retinoic X receptor α, β- and γ-secretases, we have found two potential drugs (CID 16040294 and CID 9998128) for AD from the large PubChem database. We have also shown that these two ligands can interfere with the activity of popular Acetylcholinesterase target through strong binding towards it.     

A hybrid multi-loop genetic-algorithm/simplex/spatial-grid method for locating the optimum orientation of an adsorbed protein on a solid surface

Atomistic simulation of protein adsorption on a solid surface in aqueous environment is computationally demanding, therefore the determination of preferred protein orientations on the solid surface usually serves as an initial step in simulation studies. We have developed a hybrid multi-loop genetic-algorithm/simplex/spatial-grid method to search for low adsorption-energy orientations of a protein molecule on a solid surface. In this method, the surface and the protein molecule are treated as rigid bodies, whereas the bulk fluid is represented by spatial grids. For each grid point, an effective interaction region in the surface is defined by a cutoff distance, and the possible interaction energy between an atom at the grid point and the surface is calculated and recorded in a database. In searching for the optimum position and orientation, the protein molecule is translated and rotated as a rigid body with the configuration obtained from a previous Molecular Dynamic simulation. The orientation-dependent protein-surface interaction energy is obtained using the generated database of grid energies. The hybrid search procedure consists of two interlinked loops. In the first loop A, a genetic algorithm (GA) is applied to identify promising regions for the global energy minimum and a local optimizer with the derivative-free Nelder-Mead simplex method is used to search for the lowest-energy orientation within the identified regions. In the second loop B, a new population for GA is generated and competitive solution from loop A is improved. Switching between the two loops is adaptively controlled by the use of similarity analysis. We test the method for lysozyme adsorption on a hydrophobic hydrogen-terminated silicon (110) surface in implicit water (i.e., a continuum distance-dependent dielectric constant). The results show that the hybrid search method has faster convergence and better solution accuracy compared with the conventional genetic algorithm.     

从头预测蛋白质骨架的一种并行蚁群方法及其在CASP8/9中的应用

从低同源关系的氨基酸序列预测蛋白质的三维结构被称为从头预测,它是计算生物学领域中的挑战之一.蛋白质骨架预测是从头预测的必要先导步骤.本文应用一种基于共享信息素的并行蚁群优化算法,在现有能量函数指导下,通过不同能量项之间的定性互补,构建具有最低能量的蛋白质骨架结构,并通过聚类选择构象候选集合中具有最低自由能的构象.在CASP8/9所公布的从头建模目标上应用了该方法,CASP8的13个从头建模目标中,模型1中有2个目标的预测结果超过CASP8中最好的结果,7个位列前10名;CASP9的29个从头建模目标中,候选集中的最佳结果中有20个进入Server组的前10名,模型1中有11个进入前10名.本文的结果说明融合多个不同的能量函数指导并行搜索,可以更好地模拟天然蛋白质的折叠行为.同时,在本算法载体上实现了不同种类搜索策略的融合并行,对于用非确定性算法解决类似的优化问题来说也是一种新颖的方法.     

MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990’s Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space.     

Identification of tubulin drug binding sites and prediction of relative differences in binding affinities to tubulin isotypes using digital signal processing

Microtubules are involved in numerous cellular processes including chromosome segregation during mitosis and, as a result, their constituent protein, tubulin, has become a successful target of several chemotherapeutic drugs. In general, these drugs bind indiscriminately to tubulin within both cancerous and healthy cells, resulting in unwanted side effects. However, differences between beta-tubulin isotypes expressed in a wide range of cell types may aid in the development of anti-tubulin drugs having increased specificity for only certain types of cells. Here, we describe a digital signal processing (DSP) method that is capable of predicting hot spots for the tubulin family of proteins as well as determining relative differences in binding affinities to these hot spots based only on the primary sequence of 10 human tubulin isotypes. Due to the fact that several drug binding sites have already been characterized within beta-tubulin, we are able to correlate hot spots with the binding sites for known chemotherapy drugs. We have also verified the accuracy of this method using the correlation between the binding affinities of characterized drugs and the tubulin isotypes. Additionally, the DSP method enables the rapid estimation of relative differences in binding affinities within the binding sites of tubulin isotypes that are yet to be experimentally determined.     

Structure-based design of hERG-neutral antihypertensive oxazalone and imidazolone derivatives

Angiotensin II receptor type 1 (AT1) antagonists are the most recent drug class against hypertension. Recently first crystal structure of AT1 receptor is deposited to the protein data bank (PDB ID: 4YAY). In this work, several molecular screening methods such as molecular docking and de novo design studies were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. A database consisting of 3500-fragments were used to enumerate de novo designed imidazolone and oxazolone derivatives and hereby more than 50000 novel small molecules were generated. These derivatives were then used in high throughput virtual screening simulations (Glide/HTVS) to find potent hit molecules. In addition, virtual screening of around 18 million small drug-like compounds from ZINC database were screened at the binding pocket of the AT1 receptor via Glide/HTVS method. Filtered structures were then used in more sophisticated molecular docking simulations protocols (i.e., Glide/SP; Glide/XP; Glide/IFD; Glide/QPLD, and GOLD). However, the K⁺ ion channel/drug interactions should also be considered in studies implemented in molecular level against their cardiovascular risks. Thus, selected compounds with high docking scores via all diverse docking algorithms are also screened at the pore domain regions of human ether-a-go-go-related gene (hERG1) K⁺ channel to remove the high affinity hERG1 blocking compounds. High docking scored compounds at the AT1 with low hERG1 affinity is considered for long molecular dynamics (MD) simulations. Post-processing analysis of MD simulations assisted for better understanding of molecular mechanism of studied compounds at the binding cavity of AT1 receptor. Results of this study can be useful for designing of novel and safe AT1 inhibitors.     

Protein database search of hybrid alignment algorithm based on GPU parallel acceleration

In biological research, alignment of protein sequences by computer is often needed to find similarities between them. Although results can be computed in a reasonable time for alignment of two sequences, it is still very central processing unit (CPU) time-consuming when solving massive sequences alignment problems such as protein database search. In this paper, an optimized protein database search method is presented and tested with Swiss-Prot database on graphic processing unit (GPU) devices, and further, the power of CPU multi-threaded computing is also involved to realize a GPU-based heterogeneous parallelism. In our proposed method, a hybrid alignment approach is implemented by combining Smith–Waterman local alignment algorithm with Needleman–Wunsch global alignment algorithm, and parallel database search is realized with compute unified device architecture (CUDA) parallel computing framework. In the experiment, the algorithm is tested on a lower-end and a higher-end personal computers equipped with GeForce GTX 750 Ti and GeForce GTX 1070 graphics cards, respectively. The results show that the parallel method proposed in this paper can achieve a speedup up to 138.86 times over the serial counterpart, improving efficiency and convenience of protein database search significantly.     

一种基于免疫遗传算法的分子对接构象搜索策略

分子对接是计算机辅助药物分子优化设计中的一种重要方法,搜索算法和评分函数是当前分子对接研究的难点与热点.在借鉴当前分子对接构象搜索策略的基础上,提出了一个基于免疫遗传算法的分子对接药物设计方法AutoDockIGA.首先建立了基于最优化方法的分子对接数学模型AutoDockModel,并设计了基于免疫遗传算法的构象搜索策略,运用此方法对布克海文蛋白质数据库中(Brookhaven Protein Data Bank)的6种蛋白质一配体复合物进行了实验测试,并将实验结果与AutoDock3.0、模拟退火算法的对接时间和精度进行比较和分析,实例测试表明AutoDockIGA具有更高的寻优能力.     

小分子配体模仿结合位点水分子: 相同配体与不同蛋白靶点之间作用模式的计算研究

在计算化学和药物设计领域中,精确预测小分子配体的蛋白质靶标是一件极具挑战性工作,特别是那些与相同小分子配体相互作用,但蛋白间并不具有显著序列或结构相似性的靶标蛋白。为了研究相同小分子配体与不相关的蛋白质靶标之间的结合特性,我们使用蛋白水合效应分析 (SPA) 程序研究了 33 对药物靶蛋白结合位点水分子的结构特性。每个蛋白对中的两个蛋白质,均由可与相同配体小分子相结合的两个不相关的蛋白质组成。通过计算两蛋白质间的水化位点替代重叠率,我们发现共有高达 73% 的蛋白对中有着显著的水分子替代重叠率值。特别是我们发现相同小分子与不同蛋白相互作用时,或许存在一种特别的“识别代码”,即小分子配体的功能基团与结合位点的水分子之间存在着较好的几何构型相似性。在无法确定小分子与蛋白相互作用模式时,可以依靠活性结合腔穴中水分子的结合构型特征,进行粗略估算与预测,对蛋白-配体相互作用模式研究有重要的实用价值。     

因特网上交互式化学信息数据库的设计与建设

我们正在戈一个提供化学分子检索／匹配功能的因特网上的化学数据库。该库设计为客户／服务模式。在服务端,我们用ＡＳＰ技术建造Ｗｅｂ服务器。在客户端,则使用自行开发的ＡｃｔｉｖｅＸ插件以便能在因特网浏览器上ＩＥ中显示三维分子结构及其有关数据。     

Homology models of the mutated EGFR and their response towards quinazoline analogues

One of the most intensely studied tyrosine kinases is the epidermal growth factor receptor (EGFR). The tyrosine kinase receptors are known to be over expressed in some solid tumors and non-small cell lung cancers, causing differential susceptibility to the quinazoline inhibitors. In this study we have taken SYK tyrosine kinase coordinates from PDB database to model two new EGFR receptors with these mutations G695S and L834R and conducted all the docking studies of the inhibitors, also evaluated these two models for quality of structure using PROCHECK. Seven quinazoline analogues (gefitinib, erlotinib, CI-1033, and EKB-569 and other analogues) were selected for comparisons among the two new models. This study determined the receptor/inhibitor interactions, at that active domain binding sites consisting of 15 amino acids. We were able to calculate the energy data for each of the seven inhibitors. This data has been important in interpreting the affinity between the inhibitors evaluated against the three models of EGFR (wild-type and two mutated types). "Affinity"-based studies have indicated the order of response based on docking energy levels (Van der Waals and electrostatic interactions). The active ATP binding sites consisting of 15 amino acid residues were identified and the total energy (E(total)) which showed the affinity between the inhibitor molecules and the receptor (Van der Waals and electrostatic interactions). The selection of the quinazoline analogues was purely on their emergence as possible candidates in the drug discovery areas. This study describes the successful application of these models that we constructed for molecular docking studies to rationally design compounds predicted to bind favorably to the modeled EGFR catalytic sites.     

Monitoring Protein-Drug Interactions with Mass Spectrometry and Proteolysis Protein Mass Mapping as a Drug Assay

Presented is a new approach for assaying the effects of drug candidates against protein targets. Human rhinovirus, HRV14, has been analyzed in the presence of both known and potential antiviral drug agents using enzymatically active mass spectrometer sample plates. The combination of automated mass spectrometry with proteolytic digestion provides for the rapid, high-throughput, and sensitive screening of protein-drug interactions. Multiple drug candidates can be assayed from MALDI sample plates containing 100 wells and, to further increase the number of drugs assayed, multiple drugs can be pooled into each solution (10 candidates per well, 1000 candidates per plate). Wells producing spectra indicative of drug activity can be further analyzed to discern the active compound or be tested with alternative assays.