Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates.

Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design.

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates.

Characterizing glycans and glycoconjugates in the context of three-dimensional structures is important in understanding their biological roles and developing efficient therapeutic agents. Computational modeling and molecular simulation have become an essential tool complementary to experimental methods. Here, we present a computational tool, Glycan Modeler for in silico N-/O-glycosylation of the target protein and generation of carbohydrate-only systems. In our previous study, we developed Glycan Reader, a web-based tool for detecting carbohydrate molecules from a PDB structure and generation of simulation system and input files. As integrated into Glycan Reader in CHARMM-GUI, Glycan Modeler (Glycan Reader & Modeler) enables to generate the structures of glycans and glycoconjugates for given glycan sequences and glycosylation sites using PDB glycan template structures from Glycan Fragment Database (http://glycanstructure.org/fragment-db). Our benchmark tests demonstrate the universal applicability of Glycan Reader & Modeler to various glycan sequences and target proteins. We also investigated the structural properties of modeled glycan structures by running 2-µs molecular dynamics simulations of HIV envelope protein. The simulations show that the modeled glycan structures built by Glycan Reader & Modeler have the similar structural features compared to the ones solved by X-ray crystallography. We also describe the representative examples of glycoconjugate modeling with video demos to illustrate the practical applications of Glycan Reader & Modeler. Glycan Reader & Modeler is freely available at http://charmm-gui.org/input/glycan.

Molecular Basis of Aqueous-like Activity of Lipase Treated with Glucose-Headed Surfactant in Organic Solvent.

Lipases are useful as catalysts, particularly for the kinetic resolution of racemic alcohols and esters. However, their industrial applications are limited by their poor activities in organic media. We recently found that a lipoprotein lipase from Burkholderia species displays dramatically enhanced activity in organic solvent if the protein is coated with glucose-headed surfactant (GHS). Here we investigate the molecular basis of this enhanced enzymatic activity in organic solvent by performing molecular dynamics simulations on Burkholderia cepacia lipase as a model enzyme. Our simulation results indicate that the enhanced activity of lipase stems from a dual function of GHSs different in water and organic solvent. GHS molecules maintain the open conformation of lipase by providing lipid-like microenvironment surrounding the active site in water and stabilize its native active conformation by providing water-like microenvironment around the surface of the lipase in the organic solvent. Our data also suggest the role of organic cosolvent that can facilitate closed-to-open conformational changes during the freeze-drying process. The computational approach in this study lays its potential for guiding the design of more effective surfactant molecules to improve the activity of lipases in organic solvent.

Augmenting the antinociceptive effects of nicotinic acetylcholine receptor activity through lynx1 modulation.

Neuronal nicotinic acetylcholine receptors (nAChRs) of the cholinergic system have been linked to antinociception, and therefore could be an alternative target for pain alleviation. nAChR activity has been shown to be regulated by the nicotinic modulator, lynx1, which forms stable complexes with nAChRs and has a negative allosteric action on their function. The objective in this study was to investigate the contribution of lynx1 to nicotine-mediated antinociception. Lynx1 contribution was investigated by mRNA expression analysis and electrophysiological responses to nicotine in the dorsal raphe nucleus (DRN), a part of the pain signaling pathway. In vivo antinociception was investigated in a test of nociception, the hot-plate analgesia assay with behavioral pharmacology. Lynx1/α4ß2 nAChR interactions were investigated using molecular dynamics computational modeling. Nicotine evoked responses in serotonergic and GABAergic neurons in the DRN are augmented in slices lacking lynx1 (lynx1KO). The antinociceptive effect of nicotine and epibatidine is enhanced in lynx1KO mice and blocked by mecamylamine and DHßE. Computer simulations predict preferential binding affinity of lynx1 to the α:α interface that exists in the stoichiometry of the low sensitivity (α4)3(ß2)2 nAChRs. Taken together, these data point to a role of lynx1 in mediating pain signaling in the DRN through preferential affinity to the low sensitivity α4ß2 nAChRs. This study suggests that lynx1 is a possible alternative avenue for nociceptive modulation outside of opioid-based strategies.

Effects of N-Glycan Composition on Structure and Dynamics of IgG1 Fc and Their Implications for Antibody Engineering.

Immunoglobulin G1 (IgG1), a subclass of human serum antibodies, is the most widely used scaffold for developing monoclonal antibodies to treat human diseases. The composition of asparagine(N)297-linked glycans can modulate the binding affinity of IgG1 Fc to Fc γ receptors, but it is unclear how the structural modifications of N-glycan termini, which are distal from the binding interface, contribute to the affinity. Through atomistic molecular dynamics simulations of a series of sequentially truncated high-mannose IgG1 Fc glycoforms, we found that the C'E loop and the Cγ2-Cγ3 orientation are highly dynamic, and changes in N-glycan composition alter their conformational ensembles. High-mannose glycoform preferentially samples conformations that are more competent to FcγRIIIa binding, compared to the truncated glycoforms, suggesting a role of IgG1 Fc N-glycan in optimizing the interface with the Fc receptor for efficient binding. The trajectory analyses also reveal that the N-glycan has large amplitude motions and the carbohydrate moiety interconverts between Fc-bound and unbound forms, enabling enzymatic modification of the glycan termini.

Transmembrane motions of PglB induced by LLO are coupled with EL5 loop conformational changes necessary for OST activity.

N-linked glycosylation is an enzymatic reaction in which an oligosaccharide is transferred en bloc onto an asparagine residue of an acceptor polypeptide, catalyzed by oligosaccharyltransferase (OST). Despite the available crystal structures, the role of the external loop EL5, which is critical for the catalytic cycle, is enigmatic as EL5 in the crystal structures is partially absent or blocks a pathway of lipid-linked oligosaccharide to the active site. Here we report the molecular origin of EL5 conformational changes through a series of molecular dynamics simulations of a bacterial OST, Campylobacter lari PglB. The simulations reveal that the isoprenoid moiety of lipid-linked oligosaccharide favorably binds to a hydrophobic groove of the PglB transmembrane domain. This binding triggers the conformational changes of the transmembrane domain and subsequently impairs the structural stability of EL5, leading to disordered EL5 with open conformations that are required for correct placement of the oligosaccharide in the active site.

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank.

MOTIVATION: Glycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader. RESULTS: Glycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB. AVAILABILITY AND IMPLEMENTATION: http://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org. CONTACT: wonpil@lehigh.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules.

Reading ligand structures into any simulation program is often nontrivial and time consuming, especially when the force field parameters and/or structure files of the corresponding molecules are not available. To address this problem, we have developed Ligand Reader & Modeler in CHARMM-GUI. Users can upload ligand structure information in various forms (using PDB ID, ligand ID, SMILES, MOL/MOL2/SDF file, or PDB/mmCIF file), and the uploaded structure is displayed on a sketchpad for verification and further modification. Based on the displayed structure, Ligand Reader & Modeler generates the ligand force field parameters and necessary structure files by searching for the ligand in the CHARMM force field library or using the CHARMM general force field (CGenFF). In addition, users can define chemical substitution sites and draw substituents in each site on the sketchpad to generate a set of combinatorial structure files and corresponding force field parameters for throughput or alchemical free energy simulations. Finally, the output from Ligand Reader & Modeler can be used in other CHARMM-GUI modules to build a protein-ligand simulation system for all supported simulation programs, such as CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Ligand Reader & Modeler is available as a functional module of CHARMM-GUI at http://www.charmm-gui.org/input/ligandrm. © 2017 Wiley Periodicals, Inc.

G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.

Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

CHARMM-GUI 10 years for biomolecular modeling and simulation.

CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM-GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1) PDB Reader & Manipulator, Glycan Reader, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for generation of lipopolysaccharide structures from various Gram-negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM-GUI development project. © 2016 Wiley Periodicals, Inc.

L-Met Activates Arabidopsis GLR Ca2+ Channels Upstream of ROS Production and Regulates Stomatal Movement.

Plant glutamate receptor homologs (GLRs) have long been proposed to function as ligand-gated Ca2+ channels, but no in planta evidence has been provided. Here, we present genetic evidence that Arabidopsis GLR3.1 and GLR3.5 form Ca2+ channels activated by L-methionine (L-Met) at physiological concentrations and regulate stomatal apertures and plant growth. The glr3.1/3.5 mutations resulted in a lower cytosolic Ca2+ level, defective Ca2+-induced stomatal closure, and Ca2+-deficient growth disorder, all of which involved L-Met. Patch-clamp analyses of guard cells showed that GLR3.1/3.5 Ca2+ channels are activated specifically by L-Met, with the activation abolished in glr3.1/3.5. Moreover, GLR3.1/3.5 Ca2+ channels are distinct from previously characterized ROS-activated Ca2+ channels and act upstream of ROS, providing Ca2+ transients necessary for the activation of NADPH oxidases. Our data indicate that GLR3.1/3.5 constitute L-Met-activated Ca2+ channels responsible for maintaining basal [Ca2+]cyt, play a pivotal role in plant growth, and act upstream of ROS, thereby regulating stomatal aperture.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/.

Potential Application of Alchemical Free Energy Simulations to Discriminate GPCR Ligand Efficacy.

G protein-coupled receptors (GPCRs) play fundamental roles in physiological processes by modulating diverse signaling pathways and thus have been one of the most important drug targets. Based on the fact that GPCR-mediated signaling is modulated in a ligand-specific manner such as agonist, inverse agonist, and neutral antagonist (termed ligand efficacy), quantitative characterization of the ligand efficacy is essential for rational design of selective modulators for GPCR targets. As experimental approaches for this purpose are time-, cost-, and labor-intensive, computational tools that can systematically predict GPCR ligand efficacy can have a big impact on GPCR drug design. Here, we have performed free energy perturbation molecular dynamics simulations to calculate absolute binding free energy of an inverse agonist, a neutral antagonist, and an agonist to ß2-adrenergic receptor (ß2-AR) active and inactive states, respectively, in explicit lipid bilayers. Relatively short alchemical free energy calculations reveal that both the time-series of the total binding free energy and decomposed energy contributions can be used as relevant physical properties to discriminate ß2-AR ligand efficacy. This study illustrates a merit of the current approach over simple, fast docking calculations or highly expensive millisecond-time scale simulations.

GS-align for glycan structure alignment and similarity measurement.

MOTIVATION: Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner. RESULTS: A novel method, GS-align, is developed for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition. The optimal alignment is then determined by the maximum structural similarity score, GS-score, which is size-independent. Benchmark tests against the Protein Data Bank (PDB) N-linked glycan library and PDB homologous/non-homologous N-glycoprotein sets indicate that GS-align is a robust computational tool to align glycan structures and quantify their structural similarity. GS-align is also applied to template-based glycan structure prediction and monosaccharide substitution matrix generation to illustrate its utility. AVAILABILITY AND IMPLEMENTATION: http://www.glycanstructure.org/gsalign. CONTACT: wonpil@ku.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of N-glycosylation on protein conformation and dynamics: Protein Data Bank analysis and molecular dynamics simulation study.

N-linked glycosylation is one of the most important, chemically complex, and ubiquitous post-translational modifications in all eukaryotes. The N-glycans that are covalently linked to proteins are involved in numerous biological processes. There is considerable interest in developments of general approaches to predict the structural consequences of site-specific glycosylation and to understand how these effects can be exploited in protein design with advantageous properties. In this study, the impacts of N-glycans on protein structure and dynamics are systematically investigated using an integrated computational approach of the Protein Data Bank structure analysis and atomistic molecular dynamics simulations of glycosylated and deglycosylated proteins. Our study reveals that N-glycosylation does not induce significant changes in protein structure, but decreases protein dynamics, likely leading to an increase in protein stability. Overall, these results suggest not only a common role of glycosylation in proteins, but also a need for certain proteins to be properly glycosylated to gain their intrinsic dynamic properties.

CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues.

CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface to prepare molecular simulation systems and input files to facilitate the usage of common and advanced simulation techniques. Since it is originally developed in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to setup a broad range of simulations including free energy calculation and large-scale coarse-grained representation. Here, we describe functionalities that have recently been integrated into CHARMM-GUI PDB Manipulator, such as ligand force field generation, incorporation of methanethiosulfonate spin labels and chemical modifiers, and substitution of amino acids with unnatural amino acids. These new features are expected to be useful in advanced biomolecular modeling and simulation of proteins.

Lipid-linked oligosaccharides in membranes sample conformations that facilitate binding to oligosaccharyltransferase.

Lipid-linked oligosaccharides (LLOs) are the substrates of oligosaccharyltransferase (OST), the enzyme that catalyzes the en bloc transfer of the oligosaccharide onto the acceptor asparagine of nascent proteins during the process of N-glycosylation. To explore LLOs' preferred location, orientation, structure, and dynamics in membrane bilayers of three different lipid types (dilauroylphosphatidylcholine, dimyristoylphosphatidylcholine, and dioleoylphosphatidylcholine), we have modeled and simulated both eukaryotic (Glc3-Man9-GlcNAc2-PP-Dolichol) and bacterial (Glc1-GalNAc5-Bac1-PP-Undecaprenol) LLOs, which are composed of an isoprenoid moiety and an oligosaccharide, linked by pyrophosphate. The simulations show no strong impact of different bilayer hydrophobic thicknesses on the overall orientation, structure, and dynamics of the isoprenoid moiety and the oligosaccharide. The pyrophosphate group stays in the bilayer head group region. The isoprenoid moiety shows high flexibility inside the bilayer hydrophobic core, suggesting its potential role as a tentacle to search for OST. The oligosaccharide conformation and dynamics are similar to those in solution, but there are preferred interactions between the oligosaccharide and the bilayer interface, which leads to LLO sugar orientations parallel to the bilayer surface. Molecular docking of the bacterial LLO to a bacterial OST suggests that such orientations can enhance binding of LLOs to OST.

Potential pharmacological chaperones targeting cancer-associated MCL-1 and Parkinson disease-associated α-synuclein.

Pharmacological chaperones are small molecules that bind to proteins and stabilize them against thermal denaturation or proteolytic degradation, as well as assist or prevent certain protein-protein assemblies. These activities are being exploited for the development of treatments for diseases caused by protein instability and/or aberrant protein-protein interactions, such as those found in certain forms of cancers and neurodegenerative diseases. However, designing or discovering pharmacological chaperones for specific targets is challenging because of the relatively featureless protein target surfaces, the lack of suitable chemical libraries, and the shortage of efficient high-throughput screening methods. In this study, we attempted to address all these challenges by synthesizing a diverse library of small molecules that mimic protein α-helical secondary structures commonly found in protein-protein interaction surfaces. This was accompanied by establishing a facile "on-bead" high-throughput screening method that allows for rapid and efficient discovery of potential pharmacological chaperones and for identifying novel chaperones/inhibitors against a cancer-associated protein, myeloid cell leukemia 1 (MCL-1), and a Parkinson disease-associated protein, α-synuclein. Our data suggest that the compounds and methods described here will be useful tools for the development of pharmaceuticals for complex-disease targets that are traditionally deemed "undruggable."