Structure selection for protein kinase docking and virtual screening: homology models or crystal structures?

There is currently far more sequence information than structural information available, and the ability to use homology models for virtual screening applications is desirable in many cases where structures have not yet been solved. This review focuses on the application of protein kinase homology models for virtual screening use. In addition to reviewing previous cases in which kinase homology models have been used in inhibitor design, we present new data - useful for template selection in homology modeling applications - indicating that the template structure with the highest sequence or structural similarity with the target structure may not always be the best choice. This new work explored the simple hypothesis that better results might be obtained for docking a ligand to a target receptor using a homology model of the target created from a different kinase template co-crystallized with the ligand, than from a crystal structure of the actual kinase target that is unliganded or bound to an unrelated ligand. This hypothesis was tested in docking studies of staurosporine with eight different kinases: AutoDock was used to dock staurosporine to homology models of each kinase created from staurosporine-bound template structures, and the results were compared with docking staurosporine to crystal structures of the target kinase that were obtained in complex with a non-staurosporine ligand or no ligand. It was found that the homology models performed as well as or better than the crystal structures, suggesting that using a homology model created from a template crystallized with a representative ligand may in some cases be a preferred approach, especially in virtual screening experiments that focus on enriching for members of a particular inhibitor class.     

Predicting the accuracy of protein–ligand docking on homology models

Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large‐scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state‐of‐the‐art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Structural biology, protein conformations and drug designing

Structure based drug designing is now a popular technique used for increasing the speed of drug designing process. This was made possible by the availability of many protein structures which helped in developing tools to understand the structure function relationships, automated docking and virtual screening. Knowledge of structure based functional properties of a drug target is very essential for a successful in silico designing of drugs. However, some problems associated with the structure determination process and lack of knowledge of conformational freedom associated with available protein structures are the hurdles involved in structure based drug designing. Docking and virtual screening processes depend on the active site structure of the receptor molecule and subtle differences in the conformations of these molecules due to flexibility pose a serious threat to the drug designing process. In this review problems associated with the conformations of proteins and homology models was reviewed.     

Homology model directed alignment selection for comparative molecular field analysis: Application to photosystem II inhibitors

The use of a computational docking protocol in conjunction with a protein homology model to derive molecular alignments for Comparative Molecular Field Analysis (CoMFA) was examined. In particular, the DOCK program and a model of the herbicidal target site, photosystem II (PSII), was used to derive alignments for two PSII inhibitor training sets, a set of benzo- and napthoquinones and a set of butenanilides. The protein design software in the QUANTA molecular modeling package was used to develop a homology model of spinach PSII based on the reported amino acid sequence and the X-ray crystal structure of the purple bacterium reaction center. The model is very similar to other reported PSII protein homology models. DOCK was then used to derive alignments for CoMFA modeling by docking the inhibitors in the PSII binding pocket. The molecular alignments produced from docking yielded highly predictive CoMFA models. As a comparison, the more traditional atom-atom alignments of the same two training sets failed to produce predictive CoMFA models. The general utilities of this application for homology model refinement and as an alternative scoring method are discussed.     

A_1腺苷受体的同源模建及其结构验证

采用同源模建的方法构建了A1腺苷受体的三维结构,并与拮抗剂分子DPCPX对接,将得到的复合物结构进行5 ns的分子动力学模拟,以最后2 ns的平均结构和平衡后抽取的11帧构象共12个蛋白结构为研究对象,用包含52个活性分子和1000个诱饵分子的测试库,分别通过DOCK、VINA和GOLD三种对接软件进行评价,最终得出合理的蛋白质模型.根据top10%的富集因子(EF)和ROC曲线下面积(AU-ROC)的计算结果,我们认为GOLD是最适合A1腺苷受体的对接软件,而12个蛋白质结构中F5和Favg的三维结构模型比较合理,可以作为进一步大规模虚拟筛选的模型.     

Q‐DockLHM: Low‐resolution refinement for ligand comparative modeling

The success of ligand docking calculations typically depends on the quality of the receptor structure. Given improvements in protein structure prediction approaches, approximate protein models now can be routinely obtained for the majority of gene products in a given proteome. Structure‐based virtual screening of large combinatorial libraries of lead candidates against theoretically modeled receptor structures requires fast and reliable docking techniques capable of dealing with structural inaccuracies in protein models. Here, we present Q‐Dock^LHM, a method for low‐resolution refinement of binding poses provided by FINDSITE^LHM, a ligand homology modeling approach. We compare its performance to that of classical ligand docking approaches in ligand docking against a representative set of experimental (both holo and apo) as well as theoretically modeled receptor structures. Docking benchmarks reveal that unlike all‐atom docking, Q‐Dock^LHM exhibits the desired tolerance to the receptor's structure deformation. Our results suggest that the use of an evolution‐based approach to ligand homology modeling followed by fast low‐resolution refinement is capable of achieving satisfactory performance in ligand‐binding pose prediction with promising applicability to proteome‐scale applications. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Ligand-steered modeling and docking: A benchmarking study in class A G-protein-coupled receptors

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

人类蛋白激酶组的多靶点分子对接系统

蛋白激酶在信号转导、基因转录和蛋白翻译等生物过程起关键性作用,因而与大量人类疾病密切相关。所以,蛋白激酶的抑制剂筛选是抗肿瘤药物开发的热点,正在向基于全激酶组的高通量多靶点筛选模式发展。为了降低大规模实验筛选的成本,提高成功率,本文构建人类蛋白激酶组的多靶点分子对接系统,对抑制剂-激酶组的相互作用进行预测。我们首先利用同源模建方法,对人类激酶组约500个激酶变异体的催化域进行结构建模;接着以催化域结构模型为受体,用已知激酶抑制剂进行分子对接,对抑制剂与各激酶变异体的结合亲和力进行了定量计算。结果显示,本文所建立的多靶点分子对接系统可以准确预测抑制剂与激酶变异体的相互作用,结合自由能的计算值与实验值有很强的相关性。所以,该分子对接系统可用于多靶点激酶抑制剂的计算筛选,为激酶抑制剂开发与抗肿瘤药物设计提供理论依据。     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

Homology modeling, agonist binding site identification, and docking in octopamine receptor of Periplaneta americana

AY333178 (from Periplaneta americana, 628 AAs) was selected as a target octopamine receptor (OAR) class OAR2 for this study using Discovery Studio (DS Modeling1.1/1.2, Accelrys Inc.). Blast similarity search was performed and identified that AY333178 contains N-terminal domain of GPCR. Based upon Blast and Pfam results, Rhodopsin 1U19 (protein data bank) was considered as an ideal homologue and used as a template for homology modeling due to its higher X-ray resolution at 2.2A. Sequence alignment between AY333178 and 1U19 was done using Align123 followed by a manual modification. The final alignment was carefully evaluated and evidenced to be matching the conserved residue data for class A GPCR fairly well. The 3D model of AY333178 was generated with MODELER, and further refined using CHARMm. Superimposition of the model was done over the template 1U19. Two fairly consistent profiles were observed demonstrating AY333178 model was reasonable and could be employed for the further docking study. Agonist docking into OAR2 model was done using LigandFit. The superimposition of two top poses of representative agonists was performed with a soft surface generated. Those models are considered to be used in designing new leads for hopefully more active compounds. Further research on the comparison of models for the agonists may elucidate the mechanisms of OAR2-ligand interactions.     

A proposed model of Mycobacterium avium complex dihydrofolate reductase and its utility for drug design

A homology model of Mycobacterium avium complex dihydrofolate reductase (MAC DHFR) was constructed on the basis of the X-ray crystal structure of Mycobacterium tuberculosis (Mtb) DHFR. The homology searching of the MAC DHFR resulted in the identification of the Mtb DHFR structure (PDB 1DF7) as the template for the model building. The MAC enzyme sequence was aligned to that of the Mtb counterpart using a modified Needleman and Wunsch methodology. The initial geometry to be modeled was copied from the template, either fully or partially depending on whether the residues were conserved or not, respectively. Using a randomized modeling procedure, 10 independent models of the target protein were built. The cartesian average of all the model structures was then refined using molecular mechanics. The resulting model was assessed for stereochemical quality using a Ramachandran plot and by analyzing the consistency of the model with the experimental data. The structurally and functionally important residues were identified from the model. Further, 5-deazapteridines recently reported as inhibitors of MAC DHFR were docked into the active site of the developed model. All the seven inhibitors used in the docking study have a similar docking mode at the active site. The network of hydrogen bonds around the 2,4-diamino-5-deazapteridine ring was found to be crucial for the binding of the inhibitors with the active site residues. The 5-methyl group of the inhibitors was located in a narrow hydrophobic pocket at the bottom of the active site. The relative values of the three torsion angles of the inhibitors were found to be important for the proper orientation of the inhibitor functional groups into the active site.     

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org . © 2016 Wiley Periodicals, Inc.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟, 将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接, 形成非特异性激动和特异性激动的受体-配体复合物. 用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰胆碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns. 将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分, 以top5%富集因子(EF)作为评价依据, 用占诺美林优化后的M1受体模型的EF为8.0, 用乙酰胆碱优化后M1受体模型的EF为6.5, 非复合物的EF为1.5. 说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理, 可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选, 为找到新的选择性M1受体激动剂奠定了基础.     

Prediction of homology model quality with multivariate regression

A new method has been developed for prediction of homology model quality directly from the sequence alignment, using multivariate regression. Hence, the expected quality of future homology models can be estimated using only information about the primary structure. This method has been applied to protein kinases and can easily be extended to other protein families. Homology model quality for a reference set of homology models was verified by comparison to experimental structures, by calculation of root-mean-square deviations (RMSDs) and comparison of interresidue contact areas. The homology model quality measures were then used as dependent variables in a Partial Least Squares (PLS) regression, using a matrix of alignment score profiles found from the Point Accepted Mutation (PAM) 250 similarity matrix as independent variables. This resulted in a regression model that can be used to predict the accuracy of future homology models from the sequence alignment. Using this method, one can identify the target-template combinations that are most likely to give homology models of sufficient quality. Hence, this method can be used to effectively choose the optimal templates to use for the homology modeling. The method's ability to guide the choice of homology modeling templates was verified by comparison of success rates to those obtained using BLAST scores and target-template sequence identities, respectively. The results indicate that the method presented here performs best in choosing the optimal homology modeling templates. Using this method, the optimal template was chosen in 86% of the cases, as compared to 62% using BLAST scores, and 57% using sequence identities. The method presented here can also be used to identify regions of the protein structure that are difficult to model, as well as alignment errors. Hence, this method is a useful tool for ensuring that the best possible homology model is generated.     

Homology modeling of 5-lipoxygenase and hints for better inhibitor design

Lipoxygenases (LOXs) are a group of enzymes involved in the oxygenation of polyunsaturated fatty acids. Among these 5-lipoxygenase (5-LOX) is the key enzyme leading to the formation of pharmacologically important leukotrienes and lipoxins, the mediators of inflammatory and allergic disorders. In view of close functional similarity to mammalian lipoxygenase, potato 5-LOX is used extensively. In this study, the homology modeling technique has been used to construct the structure of potato 5-LOX. The amino acid sequence identity between the target protein and sequence of template protein 1NO3 (soybean LOX-3) searched from NCBI protein BLAST was 63%. Based on the template structure, the protein model was constructed by using the Homology program in InsightII. The protein model was briefly refined by energy minimization steps and validated using Profile-3D, ERRAT and PROCHECK. The results showed that 99.3% of the amino acids were in allowed regions of Ramachandran plot, suggesting that the model is accurate and its stereochemical quality good. Like all LOXs, 5-LOX also has a two-domain structure, the small N-terminal beta-barrel domain and a larger catalytic domain containing a single atom of non-heme iron coordinating with His525, His530, His716 and Ile864. Asn720 is present in the fifth coordination position of iron. The sixth coordination position faces the open cavity occupied here by the ligands which are docked. Our model of the enzyme is further validated by examining the interactions of earlier reported inhibitors and by energy minimization studies which were carried out using molecular mechanics calculations. Four ligands, nordihydroguaiaretic acid (NDGA) having IC(50) of 1.5 microM and analogs of benzyl propargyl ethers having IC(50) values of 760 microM, 45 microM, and no inhibition respectively were selected for our docking and energy minimization studies. Our results correlated well with the experimental data reported earlier, which proved the quality of the model. This model generated can be further used for the design and development of more potent 5-LOX inhibitors.     

Structure of the virus capsid protein VP1 of enterovirus 71 predicted by some homology modeling and molecular docking studies

The homology modeling technique has been used to construct the structure of enterovirus 71 (EV 71) capsid protein VP1. The protein is consisted of 297 amino acid residues and treated as the target. The amino acid sequence identity between the target protein and sequences of template proteins 1EAH, 1PIV, and 1D4M searched from NCBI protein BLAST and WorkBench protein tools were 38, 37, and 36%, respectively. Based on these template structures, the protein model was constructed by using the InsightII/Homology program. The protein model was briefly refined by energy minimization and molecular dynamics (MD) simulation steps. The protein model was validated using some web available servers such as ERRAT, PROCHECK, PROVE, and PROSA2003. However, an inconsistency between the docking scores and the measured activity was observed for a series of EV 71 VP1 inhibitors synthesized by Shia et al. (J Med Chem 2002, 45, 1644) and docked into the binding pocket of the protein model using the DOCK 4.0.2 program. The protein model with an EV 71 VP1 inhibitor docked and engulfed was then refined further by some MD simulation steps in the presence of water molecules. The docking scores obtained for these inhibitors after such a MD refinement were well correlated with the activities. The structure-activity relationships for the ligand-protein model system was also analyzed using the GRID-VOLSURF programs and the corresponding noncrossvalidated and crossvalidated (by leave-one-out) r2 and q2 were 0.99 and 0.61, respectively. The hydrophobic nature of the binding pocket of the protein model was also examined using the GRID21 program. The possibility of improving the potency of the current series of EV 71 VP1 inhibitors was discussed based on all the studies presented.     

Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.     

Identification of Potential Inhibitors of MurD Enzyme of Staphylococcus aureus from a Marine Natural Product Library

Staphylococcus aureus is an opportunistic pathogen that can cause fatal bacterial infections. MurD catalyzes the formation of peptide bond between UDP-N-acetylehyl-l-alanine and d-glutamic acid, which plays an important role in the synthesis of peptidoglycan and the formation of cell wall by S. aureus. Because S. aureus is resistant to most existing antibiotics, it is necessary to develop new inhibitors. In this study, Schrodinger 11.5 Prime homology modeling was selected to prepare the protein model of MurD enzyme, and its structure was optimized. We used a virtual screening program and similarity screening to screen 47163 compounds from three marine natural product libraries to explore new inhibitors of S. aureus. ADME provides analysis of the physicochemical properties of the best performing compounds during the screening process. To determine the stability of the docking effect, a 100 ns molecular dynamics was performed to verify how tightly the compound was bound to the protein. By docking analysis and molecular dynamics analysis, both 46604 and 46608 have strong interaction with the docking pocket, have good pharmacological properties, and maintain stable conformation with the target protein, so they have a chance to become drugs for S. aureus. Through virtual screening, similarity screening, ADME study and molecular dynamics simulation, 46604 and 46608 were selected as potential drug candidates for S. aureus.     

Comparison of ligand-based and receptor-based virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking

HIV infection is initiated by fusion of the virus with the target cell through binding of the viral gp120 protein with the CD4 cell surface receptor protein and the CXCR4 or CCR5 co-receptors. There is currently considerable interest in developing novel ligands that can modulate the conformations of these co-receptors and, hence, ultimately block virus-cell fusion. This article describes a detailed comparison of the performance of receptor-based and ligand-based virtual screening approaches to find CXCR4 and CCR5 antagonists that could potentially serve as HIV entry inhibitors. Because no crystal structures for these proteins are available, homology models of CXCR4 and CCR5 have been built, using bovine rhodopsin as the template. For ligand-based virtual screening, several shape-based and property-based molecular comparison approaches have been compared, using high-affinity ligands as query molecules. These methods were compared by virtually screening a library assembled by us, consisting of 602 known CXCR4 and CCR5 inhibitors and some 4700 similar presumed inactive molecules. For each receptor, the library was queried using known binders, and the enrichment factors and diversity of the resulting virtual hit lists were analyzed. Overall, ligand-based shape-matching searches yielded higher enrichments than receptor-based docking, especially for CXCR4. The results obtained for CCR5 suggest the possibility that different active scaffolds bind in different ways within the CCR5 pocket.