Decoy Sets Research Articles

An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors.

The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs. activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs. Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

Refining evERdock: Improved selection of good protein-protein complex models achieved by MD optimization and use of multiple conformations.

A method for evaluating binding free energy differences of protein-protein complex structures generated by protein docking was recently developed by some of us. The method, termed evERdock, combined short (2 ns) molecular dynamics (MD) simulations in explicit water and solution theory in the energy representation (ER) and succeeded in selecting the near-native complex structures from a set of decoys. In the current work, we performed longer (up to 100 ns) MD simulations before employing ER analysis in order to further refine the structures of the decoy set with improved binding free energies. Moreover, we estimated the binding free energies for each complex structure based on an average value from five individual MD snapshots. After MD simulations, all decoys exhibit a decrease in binding free energy, suggesting that proper equilibration in explicit solvent resulted in more favourably bound complexes. During the MD simulations, non-native structures tend to become unstable and in some cases dissociate, while near-native structures maintain a stable interface. The energies after the MD simulations show an improved correlation between similarity criteria (such as interface root-mean-square distance) to the native (crystal) structure and the binding free energy. In addition, calculated binding free energies show sensitivity to the number of contacts, which was demonstrated to reflect the relative stability of structures at earlier stages of the MD simulation. We therefore conclude that the additional equilibration step along with the use of multiple conformations can make the evERdock scheme more versatile under low computational cost.

Design of antimalarial transmission blocking agents: Pharmacophore mapping of ligands active against stage-V mature gametocytes of Plasmodium falciparum

The discovery of transmission-blocking (T-B) agents is crucial for preventing and complete removal of malaria infection. However, most of the existing antimalarials are only active against the asexual stages of Plasmodium parasite, but ineffective against the sexual stage (gametocytes). In this background, we have developed pharmacophore models against the stage-V mature gametocytes of P. falciparum parasites. The pharmacophore model (Hypo-1) showed five pharmacophoric features namely, one hydrogen bond donor (HBD), one hydrophobic aliphatic (HYAl), one ring aromatic (RA), and two hydrophobic aromatic (HYAr) essential for the anti-gametocytic activity. The amino, methyl, fused phenyl ring of the quinazoline heterocycle, two phenyl rings of biphenyl moiety (HBD, HYAl, HYAr1, HYAr2 and RA) are the crucial features responsible for the non-specific anti-gametocytic activity (PfG). Subsequently, the model (Hypo-2) developed against the stage-V female gametocytes (PffG) showed the contribution of three pharmacophoric features namely, two hydrogen bond acceptor (HYA) and one RA required for the anti-gametocytic activity. The sulfhydryl, imine and pyridyl groups are observed to be essential for anti-gametocytic activity against female gametocytes. Both the models (PfG and PfGG) showed the classification accuracies of 78.26 and 71.64% for training set compounds and 60.80 and 60.18% for the test set compounds, respectively, for classification of compounds into higher and lower active classes. Also, both the models were found to retain the higher active compounds (IC50 <100 nM) in top 1% of total compounds (actives and decoys) as observed after screening the decoy set compounds.Communicated by Ramaswamy H Sarma

A Membrane Protein Complex Docking Benchmark

We report the first membrane protein–protein docking benchmark consisting of 37 targets of diverse functions and folds. The structures were chosen based on a set of parameters such as the availability of unbound structures, the modeling difficulty and their uniqueness. They have been cleaned and consistently numbered to facilitate their use in docking. Using this benchmark, we establish the baseline performance of HADDOCK, without any specific optimization for membrane proteins, for two scenarios: true interface-driven docking and ab initio docking. Despite the fact that HADDOCK has been developed for soluble complexes, it shows promising docking performance for membrane systems, but there is clearly room for further optimization. The resulting set of docking decoys, together with analysis scripts, is made freely available. These can serve as a basis for the optimization of membrane complex-specific scoring functions.

Discover potential inhibitors for PFKFB3 using 3D-QSAR, virtual screening, molecular docking and molecular dynamics simulation

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40 ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.

Identification of novel acetylcholinesterase inhibitors designed by pharmacophore-based virtual screening, molecular docking and bioassay

In this study, pharmacophore based 3D QSAR models for human acetylcholinesterase (AChE) inhibitors were generated, with good significance, statistical values (r2training = 0.73) and predictability (q2training = 0.67). It was further validated by three methods (Fischer’s test, decoy set and Güner-Henry scoring method) to show that the models can be used to predict the biological activities of compounds without costly and time-consuming synthesis. The criteria for virtual screening were also validated by testing the selective AChE inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed a novel and selective AChE inhibitor. Thus, the findings reported herein may provide a new strategy for the discovery of selective AChE inhibitors. The IC50 value of compounds 5c and 6a presented selective inhibition of AChE without inhibiting butyrylcholinesterase (BChE) at uM level. Molecular docking studies were performed to explain the potent AChE inhibition of the target compounds studies to explain high affinity.

Development of hydroxamic acid derivatives as anticancer agent with the application of 3D-QSAR, docking and molecular dynamics simulations studies

The inhibition of histone deacetylase (HDAC) has become a well-recognized target for cancer therapy. Until now only five HDAC inhibitors i.e., SAHA, romidepsin (FK-228), belinostat, chidamide, and panobinostat have been approved by FDA. The first four of them are being employed for the treatment of cutaneous T-cell lymphoma and last one is being used for the treatment of multiple myeloma. In the present study, structure and ligand-based computational approaches were selected to design novel histone deacetylase inhibitors. A ligand-based pharmacophore model was developed employing phase module that exhibited five similar features and the generated pharmacophore models were validated by enrichment studies using the decoy set. Atom-based 3D-QSAR model was developed and validated using internal, partial least square (PLS) and external validation methods. The best 3D-QSAR model exhibited high value of regression coefficient for training set (R2) = 0.926 and test (R2) = 0.699, cross-validated coefficient (rcv2) = 0.967 and R2 pred = 0.6578 with low root mean standard deviation (RMSE) = 0.4963. Additionally, the selected pharmacophore model (AAADR.12514) was employed as a 3D query for virtual screening against the ZINC database. The hit compounds were subsequently subjected to ligand–receptor interaction studies. Further, HDAC receptor-ligand complex were subjected to MM-GBSA and molecular dynamic simulation to evaluate the binding energy, strain energy, stability, and electrostatics of complex. Moreover, ADMET studies were also performed on resulted molecules. The outcome of these studies could be utilized for identification of novel lead for the development of HDAC inhibitors.

Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.

In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.

Exploration for novel inhibitors showing back-to-front approach against VEGFR-2 kinase domain (4AG8) employing molecular docking mechanism and molecular dynamics simulations

BackgroundAngiogenesis is a process of formation of new blood vessels and is an important criteria demonstrated by cancer cells. Over a period of time, these cancer cells infect the other parts of the healthy body by a process called progression. The objective of the present article is to identify a drug molecule that inhibits angiogenesis and progression.MethodsIn this pursuit, ligand based pharmacophore virtual screening was employed, generating a pharmacophore model, Hypo1 consisting of four features. Furthermore, this Hypo1 was validated recruiting, Fischer’s randomization, test set method and decoy set method. Later, Hypo1 was allowed to screen databases such as Maybridge, Chembridge, Asinex and NCI and were further filtered by ADMET filters and Lipinski’s Rule of Five. A total of 699 molecules that passed the above criteria, were challenged against 4AG8, an angiogenic drug target employing GOLD v5.2.2.ResultsThe results rendered by molecular docking, DFT and the MD simulations showed only one molecule (Hit) obeyed the back-to-front approach. This molecule displayed a dock score of 89.77, involving the amino acids, Glu885 and Cys919, Asp1046, respectively and additionally formed several important hydrophobic interactions. Furthermore, the identified lead molecule showed interactions with key residues when challenged with CDK2 protein, 1URW.ConclusionThe lead candidate showed several interactions with the crucial residues of both the targets. Furthermore, we speculate that the residues Cys919 and Leu83 are important in the development of dual inhibitor. Therefore, the identified lead molecule can act as a potential inhibitor for angiogenesis and progression.

New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures.

Accurate prediction of a protein's structure requires a reliable free energy function that consists of both enthalpic and entropic contributions. Although considerable progresses have been made in the calculation of potential energies in protein structure prediction, the computation for entropies of protein has lagged far behind, due to the challenge that estimation of entropies often requires expensive conformational sampling. In this study, we have used a knowledge-based approach to estimate the backbone conformational entropies from experimentally determined structures. Instead of conducting computationally expensive MD/MC simulations, we obtained the entropies of protein structures based on the normalized probability distributions of back dihedral angles observed in the native structures. Our new knowledge-based scoring function with inclusion of the backbone entropies, which is referred to as ITScoreDA or ITDA, was extensively evaluated on 16 commonly used decoy sets and compared with 50 other published scoring functions. It was shown that ITDA is significantly superior to the other tested scoring functions in selecting native structures from decoys. The present study suggests the role of backbone conformational entropies in protein structures and provides a way for fast estimation of the entropic effect.

Hydrophobic residues can identify native protein structures.

Evaluation of protein structures needs a trustworthy potential function. Although several knowledge-based potential functions exist, the impact of different types of amino acids in the scoring functions has not been studied yet. Previously, we have reported the importance of nonlocal interactions in scoring function (based on Delaunay tessellation) in discrimination of native structures. Then, we have questioned the structural impact of hydrophobic amino acids in protein fold recognition. Therefore, a Hydrophobic Reduced Model (HRM) was designed to reduce protein structure of FS (Full Structure) into RS (Reduced Structure). RS is considered as a reduced structure of only seven hydrophobic amino acids (L, V, F, I, A, W, Y) and all their interactions. The presented model was evaluated via four different performance metrics including the number of correctly identified natives, the Z-score of the native energy, the RMSD of the minimum score, and the Pearson correlation coefficient between the energy and the model quality. Results indicated that only nonlocal interactions between hydrophobic amino acids could be sufficient and accurate enough for protein fold recognition. Interestingly, the results of HRM is significantly close to the model that considers all amino acids (20-amino acid model) to discriminate the native structure of the proteins on eleven decoy sets. This indicates that the power of knowledge-based potential functions in protein fold recognition is mostly due to hydrophobic interactions. Hence, we suggest combining a different well-designed scoring function for non-hydrophobic interactions with HRM to achieve better performance in fold recognition.

Molecular Docking and PLIF Studies of Novel Tacrine-Naphtoquinone Hybrids Based on Multi-Target-Directed Ligand Approach for Alzheimer’s Disease

Background: Alzheimer’s disease (AD), the most typical type of dementia and memory loss, is a complicated and progressive neurodegenerative disorder. Due to the multi-factorial etiology of AD, the multi-target-directed ligand (MTDL) approach can be a potential method in seeking new drug candidates for this disease. Methods: In this study, over 200 tacrine-naphtoquinone hybrids have been designed and their drug-likeness, molecular docking, and descriptor analysis were conducted to find out a drug candidate with less toxicity and better binding affinity than tacrine. The Docking analysis was conducted using human acetylcholineesterase (1ACJ), human butyrylcholineesterase (4BDS), and β-secretase (BACE1) (1w51) enzymes using Autodock 4.2 and Vina. Results: Promising results were obtained on the types of interactions. Based on molecular docking on 3 targets as well as protein ligand interaction fingerprint (PLIF) studies, the compounds with better results were introduced as good candidates for synthesis. The validity of docking protocols was confirmed using a set of familiar active ligands and decoys on these targets by means of 2 known statistical metrics such as the receiver-operating characteristic (ROC) and Enrichment Factor (EF). Conclusion: Structure activity relationship (SAR) studies, in these class of compounds, show that the hydroxyethylamine, as a linker, is an essential group to improving binding site to AChE and BACE-1 targets.

Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction

BackgroundAs one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated.ResultsBased on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential’s performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials.ConclusionsThe optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential’s performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.

OPUS-CSF: A C-atom-based scoring function for ranking protein structural models.

We report a C‐atom‐based scoring function, named OPUS‐CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testing OPUS‐CSF on decoy recognition, it maximally recognized 257 native structures out of 278 targets in 11 commonly used decoy sets, significantly outperforming other popular all‐atom empirical potentials. The average correlation coefficient with TM‐score was also comparable with those of other potentials. OPUS‐CSF is a highly coarse‐grained scoring function, which only requires input of partial mainchain information, and very fast. Thus, it is suitable for applications at early stage of structural building.

Dockground: A comprehensive data resource for modeling of protein complexes.

Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website.

Strategies to select the best pharmacophore model: a case study in pyrazoloquinazoline class of PLK-1 inhibitors

Polo like kinase-1 (PLK-1) is essential during mitosis and in the maintenance of genomic stability. Thus, PLK-1 can be a preferential target for inhibition of mitotic processing and hence can be chosen as a drug target for the treatment of cancer. The aim of the study was to develop and validate a pharmacophore model for PLK-1 inhibitors, which is able to reproduce the receptor bound conformation of the ligand as closely as possible. Out of 14 models which could reproduce the receptor-bound conformation of the ligand, two models performed well in the decoy set statistics. Final selection of the pharmacophore model was based upon the ability of the validated model to identify actives belonging to diverse chemical classes. The selected five-point pharmacophore model for a pyrazoloquinazoline class of PLK-1 inhibitors includes one hydrogen-bond donor (D), two hydrophobic features (H), one positively-charged feature (P), and one aromatic ring feature (R).

Field trials of an acoustic decoy to attract sperm whales away from commercial longline fishing vessels in western Gulf of Alaska

Abstract In the Gulf of Alaska, sperm whales (Physeter macrocephalus) are known to remove sablefish (Anoplopoma fimbria) from commercial longline fishing gear. This removal, called depredation, is economically costly to fishermen, presents risk of injury or mortality to whales, and could lead to unknown removals during the federal sablefish longline survey that contributes to estimation of the annual fishing quota. In 2013 the Southeast Alaska Sperm Whale Avoidance Project (SEASWAP) evaluated the efficacy of an acoustic decoy in reducing encounters between sperm whales and longline fishing gear. The aim of the acoustic decoy was to use fishing vessel sounds to attract whales to an area away from the true fishing haul in order to reduce interactions between commercial fishing vessels and whales. A custom playback device that could be remotely activated via a radio modem was incorporated into an anchored buoy system that could be deployed by the vessel during a two-month trip between June and July 2013. Once activated, the decoy broadcasted vessel-hauling noises known to attract whales, while the vessel performed several true hauls at various ranges from the device. Passive acoustic recorders at both the decoy and true set locations were also deployed to evaluate whale presence. Twenty-six hauls were conducted while a decoy was deployed, yielding fourteen sets with whales present while the decoy was functional. A significant relationship was found between the number of whales present at the true fishing haul and the distance of the haul from the decoy (1–14 km range), with the decoy being most effective at ranges greater than 9 km (t = −2.06, df = 12, p = 0.04). The results suggest that acoustic decoys may be a cost-effective means for reducing longlining depredation from sperm and possibly killer whales under certain circumstances.

Assessment of model accuracy estimations in CASP12.

The record high 42 model accuracy estimation methods were tested in CASP12. The paper presents results of the assessment of these methods in the whole-model and per-residue accuracy modes. Scores from four different model evaluation packages were used as the "ground truth" for assessing accuracy of methods' estimates. They include a rigid-body score-GDT_TS, and three local-structure based scores-LDDT, CAD and SphereGrinder. The ability of methods to identify best models from among several available, predict model's absolute accuracy score, distinguish between good and bad models, predict accuracy of the coordinate error self-estimates, and discriminate between reliable and unreliable regions in the models was assessed. Single-model methods advanced to the point where they are better than clustering methods in picking the best models from decoy sets. On the other hand, consensus methods, taking advantage of the availability of large number of models for the same target protein, are still better in distinguishing between good and bad models and predicting local accuracy of models. The best accuracy estimation methods were shown to perform better with respect to the frozen in time reference clustering method and the results of the best method in the corresponding class of methods from the previous CASP. Top performing single-model methods were shown to do better than all but three CASP12 tertiary structure predictors when evaluated as model selectors.

OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing

We report a new distance- and orientation-dependent, all-atom statistical potential derived from side-chain packing, named OPUS-DOSP, for protein structure modeling. The framework of OPUS-DOSP is based on OPUS-PSP, previously developed by us [JMB (2008), 376, 288–301], with refinement and new features. In particular, distance or orientation contribution is considered depending on the range of contact distance. A new auxiliary function in energy function is also introduced, in addition to the traditional Boltzmann term, in order to adjust the contributions of extreme cases. OPUS-DOSP was tested on 11 decoy sets commonly used for statistical potential benchmarking. Among 278 native structures, 239 and 249 native structures were recognized by OPUS-DOSP without and with the auxiliary function, respectively. The results show that OPUS-DOSP has an increased decoy recognition capability comparing with those of other relevant potentials to date.

Identification of hit compounds for squalene synthase: Three‐dimensional quantitative structure‐activity relationship pharmacophore modeling, virtual screening, molecular docking, binding free energy calculation, and molecular dynamic simulation

Squalene synthase (SQS) is the key precursor in the synthesis of cholesterol. Located downstream in relation to hydroxy methylglutaryl coenzyme A reductase and having no influence on the formation of biologically necessary isoprenoids make it an interesting target for the development of cholesterol lowering drugs with fewer side effects. To discover novel SQS inhibitors, three‐dimensional quantitative structure‐activity relationship pharmacophore models were built and further validated by cost function analysis, test set validation, and decoy set validation to obtain a reliable model for virtual screening against a database that contains 5.5 million compounds. The interactions between SQS and the ligands were predicted by an integrated protocol that contains molecular docking, molecular mechanics/generalized born surface area, and molecular dynamic simulation. After that, five compounds with best binding affinities and binding modes were obtained as potential hits for further study and three of them showed inhibitory effects against SQS.