Docking and DFT Studies to explore the Topoisomerase II ATP Pocket employing 3-Substituted 2,6-Piperazindiones for drug design

Topoisomerases (Topos) are very important protein targets for drug design in cancer treatment. Human Topo type IIα (hTopo IIα) has been widely studied experimentally and theoretically. Here, we performed protein rigid/flexible side-chain docking to study a set of thirty-nine 3-substituted-2,6-piperazindiones (labelled 1a, (R)-[(2–20)a] and (S)-[(2–20)b]) derived from α-amino acids. To explain the ligand–protein complexes at the electronic level [using the highest occupied and the lowest unoccupied molecular orbitals (HOMO and LUMO) energies], density functional theory calculations were carried out. Finally, to show adenosine triphosphate (ATP) binding-site constituents, the Q-SiteFinder program was used. The docking results showed that all of the test compounds bind to the ATP-binding site on hTopo IIα. Recognition is mediated by the formation of several hydrogen bond acceptors or donators. This site was the largest (631 Å³) according to the Q-SiteFinder program. When using the protein rigid docking protocol, compound 13a derived from (R)-Lys showed the highest affinity. However, when a flexible side-chain docking protocol was used, the compound with the highest affinity was 16a, derived from (R)-Trp. Frontier molecular orbital studies showed that the HOMO of the ligand interacts with the LUMO located at side-chain residues from the protein-binding site. The HOMO of the binding site interacts with the LUMO of the ligand. We conclude that some ligand properties including the hindrance effect, hydrogen bonds, π–π interactions and stereogenic centres are important for the ligand to be recognised by the ATP-binding site of hTopo IIα.     

Exploration of human serum albumin binding sites by docking and molecular dynamics flexible ligand–protein interactions

Five‐nanosecond molecular dynamics (MD) simulations were performed on human serum albumin (HSA) to study the conformational features of its primary ligand binding sites (I and II). Additionally, 11 HSA snapshots were extracted every 0.5 ns to explore the binding affinity (K_d) of 94 known HSA binding drugs using a blind docking procedure. MD simulations indicate that there is considerable flexibility for the protein, including the known sites I and II. Movements at HSA sites I and II were evidenced by structural analyses and docking simulations. The latter enabled the study and analysis of the HSA–ligand interactions of warfarin and ketoprofen (ligands binding to sites I and II, respectively) in greater detail. Our results indicate that the free energy values by docking (K_d observed) depend upon the conformations of both HSA and the ligand. The 94 HSA–ligand binding K_d values, obtained by the docking procedure, were subjected to a quantitative structure‐activity relationship (QSAR) study by multiple regression analysis. The best correlation between the observed and QSAR theoretical (K_d predicted) data was displayed at 2.5 ns. This study provides evidence that HSA binding sites I and II interact specifically with a variety of compounds through conformational adjustments of the protein structure in conjunction with ligand conformational adaptation to these sites. These results serve to explain the high ligand‐promiscuity of HSA. © 2009 Wiley Periodicals, Inc. Biopolymers 93: 161–170, 2010. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

Insight into the structural requirements of thiophene-3-carbonitriles-based MurF inhibitors by 3D-QSAR,molecular docking and molecular dynamics study

The discovery of clinically relevant inhibitors against MurF enzyme has proven to be a challenging task. In order to get further insight into the structural features required for the MurF inhibitory activity, we performed pharmacophore and atom-based three-dimensional quantitative structure–activity relationship studies for novel thiophene-3-carbonitriles based MurF inhibitors. The five-feature pharmacophore model was generated using 48 inhibitors having IC₅₀ values ranging from 0.18 to 663?μm. The best-fitted model showed a higher coefficient of determination (R²?=?0.978), cross-validation coefficient (Q²?=?0.8835) and Pearson coefficient (0.9406) at four component partial least-squares factor. The model was validated with external data set and enrichment study. The effectiveness of the docking protocol was validated by docking the co-crystallized ligand into the catalytic pocket of MurF enzyme. Further, binding free energy calculated by the molecular mechanics generalized Born surface area approach showed that van der Waals and non-polar solvation energy terms are the main contributors to ligand binding in the active site of MurF enzyme. A 10-ns molecular dynamic simulation was performed to confirm the stability of the 3ZM6-ligand complex. Four new molecules are also designed as potent MurF inhibitors. These results provide insights regarding the development of novel MurF inhibitors with better binding affinity.     

Shape-based virtual screening,docking, and molecular dynamics simulations to identify Mtb-ASADH inhibitors

Aspartate β-semialdehyde dehydrogenase (ASADH) is a key enzyme for the biosynthesis of essential amino acids and several important metabolites in microbes. Inhibition of ASADH enzyme is a promising drug target strategy against Mycobacterium tuberculosis (Mtb). In this work, in silico approach was used to identify potent inhibitors of Mtb-ASADH. Aspartyl β-difluorophosphonate (β-AFP), a known lead compound, was used to understand the molecular recognition interactions (using molecular docking and molecular dynamics analysis). This analysis helped in validating the computational protocol and established the participation of Arg99, Glu224, Cys130, Arg249, and His256 amino acids as the key amino acids in stabilizing ligand–enzyme interactions for effective binding, an essential feature is H-bonding interactions with the two arginyl residues at the two ends of the ligand. Best binding conformation of β-AFP was selected as a template for shape-based virtual screening (ZINC and NCI databases) to identify compounds that competitively inhibit the Mtb-ASADH. The top rank hits were further subjected to ADME and toxicity filters. Final filter was based on molecular docking analysis. Each screened molecule carries the characteristics of the highly electronegative groups on both sides separated by an average distance of 6?Å. Finally, the best predicted 20 compounds exhibited minimum three H-bonding interactions with Arg99 and Arg249. These identified hits can be further used for designing the more potent inhibitors against ASADH family. MD simulations were also performed on two selected compounds (NSC4862 and ZINC02534243) for further validation. During the MD simulations, both compounds showed same H-bonding interactions and remained bound to key active residues of Mtb-ASADH.     

DINC: A new AutoDock-based protocol for docking large ligands

Background

Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow.

Results

In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC.

Conclusions

Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.

     

Computational Studies of Ligand-Receptor Interactions in Bitter Taste Receptors

Phenylthiocarbamide tastes intensely bitter to some individuals, but others find it completely tasteless. Recently, it was suggested that phenylthiocarbamide elicits bitter taste by interacting with a human G protein-coupled receptor (hTAS2R38) encoded by the PTC gene. The phenylthiocarbamide nontaster trait was linked to three single nucleotide polymorphisms occurring in the PTC gene. Using the crystal structure of bovine rhodopsin as template, we generated the 3D structure of hTAS2R38 bitter taste receptor. We were able to map on the receptor structure the amino acids affected by the genetic polymorphisms and to propose molecular functions for two of them that explained the emergence of the nontaster trait. We used molecular docking simulations to find that phenylthiocarbamide exhibited a higher affinity for the target receptor than the structurally similar molecule 6-n-propylthiouracil, in line with recent experimental studies. A 3D model was constructed for the hTAS2R16 bitter taste receptor as well, by applying the same protocol. We found that the recently published experimental ligand binding affinity data for this receptor correlated well with the binding scores obtained from our molecular docking calculations.     

分子对接与全局极小化方法

全局极小化方法及其在结构生物学中的应用近年来取得了显著的进展.适当简化的分子对接问题是全局极小化方法的一个很好目标,并且是当前一个相当活跃的研究领域.对接可分为两类：主要用于从头配体设计的细致对接和用于已知化合物数据库筛选以发现药物的粗略对接,它们对全局极小化算法的要求是不同的.简要评述了新出现的适合于对接问题的随机和确定性全局极小化算法,其中势能平滑算法看来很有希望,值得密切关注.     

Applying conformational selection theory to improve crossdocking efficiency in 3‐phosphoinositide dependent protein kinase‐1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced‐fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a “selection” between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein–ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3‐phosphoinositide dependent protein kinase‐1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP. Proteins 2014; 82:436–451. © 2013 Wiley Periodicals, Inc.     

In silico hit optimization toward AKT inhibition: fragment-based approach,molecular docking and molecular dynamics study

Abstract

Protein kinase B also known as AKT is a cardinal node in different signaling pathways that regulates diverse cell processes. AKT has three isoforms that share high homology. Hyperactivation of each isoform is related with different types of cancer. This work describes the computational search for new inhibitors using a hit optimization process of the previously reported AKT pan inhibitor, a 2,4,6-trisubstituted pyridine. A database of new molecules was proposed using a variant of fragment-based docking methodology and previous reported considerations. Molecular docking followed by molecular dynamics studies were performed to select the best compounds and analyze their behavior. Protein–ligand complexes energy was calculated using molecular mechanics Poisson–Boltzmann surface area protocol. Further, proposed molecules were compared with the ChEMBL database of compounds assayed against AKT. Data analysis leads to determine the structural requirements necessary for a favorable interaction of the proposed ligands with the AKT pocket. Molecular dynamics data suggested that the pK_a of the ligands is important for the stability in the AKT pocket. Molecular similarity analysis shows that proposed ligands have not been previously reported. Thus, ligands with high docking scores and favorable behavior on molecular dynamics simulations are proposed as potential AKT inhibitors.     

Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.     

FitDockApp：基于模板的分子对接PyMOL插件图形化计算软件

目的 分子对接在预测分子之间的结合模式和亲和力方面起着至关重要的作用,是计算结构生物学和计算机辅助药物设计研究的重要方法。本研究团队近期开发了一款基于模板的新型对接方法FitDock,当存在近似的蛋白质配体模板时,它在准确性和速度方面都超过了业界常用的分子对接方法。为了增强FitDock方法的可用性,使其在分子模拟领域得到更广泛的应用,很有必要发展图像化的软件工具。方法 基于Python图像化编程,本文开发了FitDockApp,这是分子可视化软件PyMOL的插件软件。结果 FitDockApp能够通过操作窗口界面,实现基于模板的分子对接和配体结构比对,实时显示预测三维结构,并提供将对接文件上传到实验室服务器获取最优模板的便利。此外,FitDockApp还具备批量对接功能。结论 FitDockApp通过用户友好的界面简化了对接过程,并提供丰富的功能,帮助研究人员获得精确的对接结果。FitDockApp是一款免费软件,兼容Windows和Linux系统,可在http://cao.labshare.cn/fitdock/下载。     

Bacterial‐induced expression of RAB18 protein in Orzya sativa salinity stress and insights into molecular interaction with GTP ligand

In the present research, we have studied the inoculation effects of two root‐associated plant growth‐promoting rhizobacteria (PGPR) in rice and provide the pieces of evidence that the inoculation of the PGPR could potentially result in inducing the expression of the salt stress‐related RAB18 plant gene under varying degrees of salinity stress. The sequenced putative gene of RAB18 of Oryza sativa in this study is 740 bp long, has a content of 44.4%, and a molecular weight of 492 102.00 Da. BLAST homology patterns revealed sequence similarity with the previously sequenced RAB in model plant species. We demonstrate the mode of action of this stress‐related protein by performing comparative modeling of Q10RT8 (Os03g0146000 protein, homolog of the sequenced RAB18; O. sativa subsp. japonica) using energy minimization, molecular dynamic simulations, and molecular docking of a guanosine triphosphate (GTP) ligand with the protein. The docking results indicated that Ser21, Ala22, Lys25, Asp68, Ala70, Glu73, and Arg74 are important determinant residues for functional interaction with the GTP ligand. The present research contributes to the understanding of the PGPR inoculation in salinity stress. Additionally, it provides the layout of the understanding of the molecular interactions between RAB and GTP ligand. Copyright © 2014 John Wiley & Sons, Ltd.     

Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an “acceptable” or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.     

MADAMM: a multistaged docking with an automated molecular modeling protocol

Dealing with receptor flexibility in docking methodology is still a problem. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. In this paper, we present an automated procedure, called MADAMM, that allows flexibilization of both the receptor and the ligand during a multistaged docking with an automated molecular modeling protocol. We show that the orientation of particular residues at the interface between the protein and the ligand have a crucial influence on the way they interact during the docking process, and the standard docking methodologies failed to predict their correct mode of binding. We present some examples that demonstrate the capabilities of this approach when compared with traditional docking methodologies.     

Structure-based virtual screening,molecular docking,ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase

Visceral leishmaniasis (VL) is the most fatal form of leishmaniasis and it affects 70 countries worldwide. Increasing drug resistant for antileishmanial drugs such as miltefosine, sodium stibogluconate and pentamidine has been reported in the VL endemic region. Amphotericin B has shown potential antileishmanial activity in different formulations but its cost of treatment and associated nephrotoxicity have limited its use by affected people living in the endemic zone. To control the VL infection in the affected countries, it is necessary to develop new antileishmanial compounds with high efficacy and negligible toxicity. Computer aided programs such as binding free energy estimation; ADMET prediction and molecular dynamics simulation can be used to investigate novel antileishmanial molecules in shorter duration. To develop antileishmanial lead molecule, we performed standard precision (SP) docking for 1160 benzoxaborole analogs along with reference inhibitors against trypanothione reductase of Leishmania parasite. Furthermore, extra precision (XP) docking, ADMET prediction, prime MM-GBSA was conducted over 115 ligands, showing better docking score than reference inhibitors to get potential antileishmanial compounds. Simultaneously, area under the curve (AUC) was estimated using ROC plot to validate the SP and XP docking protocol. Later on, two benzoxaborole analogs with best MM-GBSA ΔG-bind were subjected to molecular simulation and docking confirmation to ensure the ligand interaction with TR. The presented drug discovery based on computational study confirms that BOB27 can be used as a potential drug candidate and warrants further experimental investigation to fight against VL in endemic areas.     

Ligand-based pharmacophore modeling and docking studies on vitamin D receptor inhibitors

In recent years, pharmacophore modeling and molecular docking approaches have been extensively used to characterize the structural requirements and explore the conformational space of a ligand in the binding pocket of the selected target protein. Herein, we report a pharmacophore modeling and molecular docking of 45 compounds comprising of the indole scaffold as vitamin D receptor (VDR) inhibitors. Based on the selected best hypothesis (DRRRR.61), an atom-based three-dimensional quantitative structure-activity relationships model was developed to rationalize the structural requirement of biological activity modulating components. The developed model predicted the binding affinity for the training set and test set with R²_(training) = 0.8869 and R²_(test) = 0.8139, respectively. Furthermore, molecular docking and dynamics simulation were performed to understand the underpinning of binding interaction and stability of selected VDR inhibitors in the binding pocket. In conclusion, the results presented here, in the form of functional and structural data, agreed well with the proposed pharmacophores and provide further insights into the development of novel VDR inhibitors with better activity.     

An ant colony optimization approach to flexible protein–ligand docking

The prediction of the complex structure of a small ligand with a protein, the so-called protein–ligand docking problem, is a central part of the rational drug design process. For this purpose, we introduce the docking algorithm PLANTS (Protein–Ligand ANT System), which is based on ant colony optimization, one of the most successful swarm intelligence techniques. We study the effectiveness of PLANTS for several parameter settings and present a direct comparison of PLANTS’s performance to a state-of-the-art program called GOLD, which is based on a genetic algorithm and frequently used in the pharmaceutical industry for this task. Last but not least, we also show that PLANTS can make effective use of protein flexibility giving example results on cross-docking and virtual screening experiments for protein kinase A. This article is based on a paper that won the best paper award at ANTS 2006, the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence held in Brussels, Belgium, 2006. This article includes new types of experiments and also the possibility of considering flexibility of protein side-chains.