Contribution of Solvation Energy in Protein‐Peptide Recognition Systems

The contribution of solvation energy to guiding molecular recognition for six rigid protein‐peptide systems bad been evaluated by the variation in the number of the identified native‐like configurations and in the driving force of specific interaction resulting from the addition of the explicit solvation term in the force field function. The AMBER force field energy and the total energy including the force field energy and the WZS solvation energy were calculated for sampled configurations. The results obtained by the calculations of both force field and total energies were compared with each other. It suggests that the contribution of solvation energy is important to guiding the specific recognition of the systems in which the ligands possess larger hydrophobic or aromatic residues while the protein receptors provide the active surfaces with hydrophobic property.     

Focused grid‐based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein‐protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near‐native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands. © 2016 Wiley Periodicals, Inc.     

Protein farnesyltransferase: Flexible docking studies on inhibitors using computational modeling

Summary Using MacroModel, peptide, peptidomimetic and non-peptidomimetic inhibitors of the zinc metalloenzyme, farnesyltransferase (FTase), were docked into the enzyme binding site. Inhibitor flexibility, farnesyl pyrophosphate substrate flexibility, and partial protein flexibility were taken into account in these docking studies. In addition to CVFM and CVIM, as well as our own inhibitors FTI-276 and FTI-2148, we have docked other farnesyltransferase inhibitors (FTIs) including Zarnestra, which presently is in advanced clinical trials. The AMBER* force field was employed, augmented with parameters that were derived for zinc. A single binding site model that was derived from the crystal structure of CVFM complexed with farnesyltransferase and farnesylpyrophosphate was used for these studies. The docking results using the lowest energy structure from the simulation, or one of the lowest energy structures, were generally in excellent agreement with the X-ray structures. One of the most important findings of this study is that numerous alternative conformations for the methionine side chain can be accommodated by the enzyme suggesting that the methionine pocket can tolerate groups larger than methionine at the C-terminus of the tetrapeptide and suggesting alternative locations for the placement of side chains that may improve potency.     

A nonredundant structure dataset for benchmarking protein‐RNA computational docking

Protein–RNA interactions play an important role in many biological processes. The ability to predict the molecular structures of protein–RNA complexes from docking would be valuable for understanding the underlying chemical mechanisms. We have developed a novel nonredundant benchmark dataset for protein–RNA docking and scoring. The diverse dataset of 72 targets consists of 52 unbound–unbound test complexes, and 20 unbound–bound test complexes. Here, unbound–unbound complexes refer to cases in which both binding partners of the cocrystallized complex are either in apo form or in a conformation taken from a different protein–RNA complex, whereas unbound–bound complexes are cases in which only one of the two binding partners has another experimentally determined conformation. The dataset is classified into three categories according to the interface root mean square deviation and the percentage of native contacts in the unbound structures: 49 easy, 16 medium, and 7 difficult targets. The bound and unbound cases of the benchmark dataset are expected to benefit the development and improvement of docking and scoring algorithms for the docking community. All the easy‐to‐view structures are freely available to the public at http://zoulab.dalton.missouri.edu/RNAbenchmark/ . © 2012 Wiley Periodicals, Inc.     

CombiDOCK: Structure-based combinatorial docking and library design

We have developed a strategy for efficiently docking a large combinatorial library into a target receptor. For each scaffold orientation, all potential fragments are attached to the scaffold, their interactions with the receptor are individually scored and factorial combinations of fragments are constructed. To test its effectiveness, this approach is compared to two simple control algorithms. Our method is more efficient than the controls at selecting best scoring molecules and at selecting fragments for the construction of an exhaustive combinatorial library. We also carried out a retrospective analysis of the experimental results of a 10×10×10 exhaustive combinatorial library. An enrichment factor of approximately 4 was found for identifying the compounds in the library that are active at 330 nM.     

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure‐based computer‐aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand–protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state‐of‐the‐art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.     

VinaMPI: Facilitating multiple receptor high‐throughput virtual docking on high‐performance computers

The program VinaMPI has been developed to enable massively large virtual drug screens on leadership‐class computing resources, using a large number of cores to decrease the time‐to‐completion of the screen. VinaMPI is a massively parallel Message Passing Interface (MPI) program based on the multithreaded virtual docking program AutodockVina, and is used to distribute tasks while multithreading is used to speed‐up individual docking tasks. VinaMPI uses a distribution scheme in which tasks are evenly distributed to the workers based on the complexity of each task, as defined by the number of rotatable bonds in each chemical compound investigated. VinaMPI efficiently handles multiple proteins in a ligand screen, allowing for high‐throughput inverse docking that presents new opportunities for improving the efficiency of the drug discovery pipeline. VinaMPI successfully ran on 84,672 cores with a continual decrease in job completion time with increasing core count. The ratio of the number of tasks in a screening to the number of workers should be at least around 100 in order to have a good load balance and an optimal job completion time. The code is freely available and downloadable. Instructions for downloading and using the code are provided in the Supporting Information. © 2013 Wiley Periodicals, Inc.     

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.     

Stabilizer‐Guided Inhibition of Protein–Protein Interactions

The discovery of novel protein–protein interaction (PPI) modulators represents one of the great molecular challenges of the modern era. PPIs can be modulated by either inhibitor or stabilizer compounds, which target different though proximal regions of the protein interface. In principle, protein–stabilizer complexes can guide the design of PPI inhibitors (and vice versa). In the present work, we combine X‐ray crystallographic data from both stabilizer and inhibitor co‐crystal complexes of the adapter protein 14‐3‐3 to characterize, down to the atomic scale, inhibitors of the 14‐3‐3/Tau PPI, a potential drug target to treat Alzheimer’s disease. The most potent compound notably inhibited the binding of phosphorylated full‐length Tau to 14‐3‐3 according to NMR spectroscopy studies. Our work sets a precedent for the rational design of PPI inhibitors guided by PPI stabilizer–protein complexes while potentially enabling access to new synthetically tractable stabilizers of 14‐3‐3 and other PPIs.     

GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.     

Applying flexible molecular docking to simulate protein retention behavior in hydrophobic interaction chromatography

Interaction between proteins and stationary phase in hydrophobic interaction chromatography (HIC) is differentiated into two thermodynamic processes involving direct nonbonding/conformation interac- tion and surface hydrophobic effect of proteins, hence quantitatively giving rise to a binary linear rela- tion between HIC retention time (RT) at concentrated salting liquid and ligand-protein binding free en- ergy. Then, possible binding manners for 27 proteins of known crystal structures with hydrophobic ligands are simulated and analyzed via ICM flexible molecular docking and genetic algorithm, with re- sults greatly consistent with experimental values. By investigation, it is confirmed local hydrophobic effects of proteins and nonbinding/conformation interaction between ligand and protein both notably influence HIC chromatogram retention behaviors, mainly focusing on exposed portions on the protein surface.     

Ligand-protein docking using a quantum stochastic tunneling optimization method

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.     

Design of the First‐in‐Class,Highly Potent Irreversible Inhibitor Targeting the Menin‐MLL Protein–Protein Interaction

The structure‐based design of M‐525 as the first‐in‐class, highly potent, irreversible small‐molecule inhibitor of the menin‐MLL interaction is presented. M‐525 targets cellular menin protein at sub‐nanomolar concentrations and achieves low nanomolar potencies in cell growth inhibition and in the suppression of MLL‐regulated gene expression in MLL leukemia cells. M‐525 demonstrates high cellular specificity over non‐MLL leukemia cells and is more than 30 times more potent than its corresponding reversible inhibitors. Mass spectrometric analysis and co‐crystal structure of M‐525 in complex with menin firmly establish its mode of action. A single administration of M‐525 effectively suppresses MLL‐regulated gene expression in tumor tissue. An efficient procedure was developed to synthesize M‐525. This study demonstrates that irreversible inhibition of menin may be a promising therapeutic strategy for MLL leukemia.     

A Usual G‐Protein‐Coupled Receptor in Unusual Membranes

G‐protein‐coupled receptors (GPCRs) are the largest family of membrane‐bound receptors and constitute about 50 % of all known drug targets. They offer great potential for membrane protein nanotechnologies. We report here a charge‐interaction‐directed reconstitution mechanism that induces spontaneous insertion of bovine rhodopsin, the eukaryotic GPCR, into both lipid‐ and polymer‐based artificial membranes. We reveal a new allosteric mode of rhodopsin activation incurred by the non‐biological membranes: the cationic membrane drives a transition from the inactive MI to the activated MII state in the absence of high [H⁺] or negative spontaneous curvature. We attribute this activation to the attractive charge interaction between the membrane surface and the deprotonated Glu134 residue of the rhodopsin‐conserved ERY sequence motif that helps break the cytoplasmic “ionic lock”. This study unveils a novel design concept of non‐biological membranes to reconstitute and harness GPCR functions in synthetic systems.     

Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility

We present a series of molecular‐mechanics‐based protein refinement methods, including two novel ones, applied as part of an induced fit docking procedure. The methods used include minimization; protein and ligand sidechain prediction; a hierarchical ligand placement procedure similar to a‐priori protein loop predictions; and a minimized Monte Carlo approach using normal mode analysis as a move step. The results clearly indicate the importance of a proper opening of the active site backbone, which might not be accomplished when the ligand degrees of freedom are prioritized. The most accurate method consisted of the minimized Monte Carlo procedure designed to open the active site followed by a hierarchical optimization of the sidechain packing around a mobile flexible ligand. The methods have been used on a series of 88 protein‐ligand complexes including both cross‐docking and apo‐docking members resulting in complex conformations determined to within 2.0 Å heavy‐atom RMSD in 75% of cases where the protein backbone rearrangement upon binding is less than 1.0 Å α‐carbon RMSD. We also demonstrate that physics‐based all‐atom potentials can be more accurate than docking‐style potentials when complexes are sufficiently refined. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

A novel scoring function for molecular docking

We present a novel scoring function for docking of small molecules to protein binding sites. The scoring function is based on a combination of two main approaches used in the field, the empirical and knowledge-based approaches. To calibrate the scoring function we used an iterative procedure in which a ligand's position and its score were determined self-consistently at each iteration. The scoring function demonstrated superiority in prediction of ligand positions in docking tests against the commonly used Dock, FlexX and Gold docking programs. It also demonstrated good accuracy of binding affinity prediction for the docked ligands.