A Gibbs free energy correlation for automated docking of carbohydrates

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Preparation, Characterization, and Bioavailability of Host-Guest Inclusion Complex of Ginsenoside Re with Gamma-Cyclodextrin

This work aimed at improving the water solubility of Ginsenoside (G)-Re by forming an inclusion complex. The solubility parameters of G-Re in alpha (α), beta (β), and gamma (γ) cyclodextrin (CD) were investigated. The phase solubility profiles were all classified as AL-type that indicated the 1:1 stoichiometric relationship with the stability constants Ks which were 22 M⁻¹ (α-CD), 612 M⁻¹ (β-CD), and 14,410 M⁻¹ (γ-CD), respectively. Molecular docking studies confirmed the results of phase solubility with the binding energy of −4.7 (α-CD), −5.10 (β-CD), and −6.70 (γ-CD) kcal/mol, respectively. The inclusion complex (IC) of G-Re was prepared with γ-CD via the water-stirring method followed by freeze-drying. The successful preparation of IC was confirmed by powder X-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). In-vivo absorption studies were carried out by LC-MS/MS. Dissolution rate of G-Re was increased 9.27 times after inclusion, and the peak blood concentration was 2.7-fold higher than that of pure G-Re powder. The relative bioavailability calculated from the ratio of Area under the curve AUC₀–_∞ of the inclusion to pure G-Re powder was 171%. This study offers the first report that describes G-Re’s inclusion into γ-CD, and explored the inclusion complex’s mechanism at the molecular level. The results indicated that the solubility could be significantly improved as well as the bioavailability, implying γ-CD was a very suitable inclusion host for complex preparation of G-Re.

Comparison of Carbohydrate Force Fields in Molecular Dynamics Simulations of Protein-Carbohydrate Complexes

In this paper, we present the results of molecular dynamics simulations aimed at critical comparison of classical, biomolecular force fields (FFs) in the context of their capabilities to describe the structural and thermodynamic features of carbohydrate-protein interactions. We have considered the three main families of FFs (CHARMM, GROMOS, and GLYCAM/AMBER) by applying them to investigate the seven different carbohydrate-protein complexes. The results indicate that although the qualitative pattern of several structural descriptors (intermolecular hydrogen bonding, ligand dynamic location, etc.) is conserved among the compared FFs, there also exists a number of significant divergences (mainly the patterns of contacts between particular amino acid residues and bound carbohydrate). The carbohydrate-protein unbinding free energies also vary from one FF to another, displaying diversified trends in deviations from the experimental data. The magnitude of those deviations is not negligible and indicates the need for refinement in the currently existing combinations of carbohydrate- and protein-dedicated biomolecular force fields. In spite of the lack of explicit functional terms responsible for the corresponding intermolecular forces, all tested FFs are capable of adequately reproducing the CH-π interactions, crucial for carbohydrate-protein binding.

Three-Dimensional Structures of Carbohydrates and Where to Find Them

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

Carbohydrate-protein interactions: molecular modeling insights

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.

Scoring functions for AutoDock

Automated docking allows rapid screening of protein-ligand interactions. A scoring function composed of a force field and linear weights can be used to compute a binding energy from a docked atom configuration. For different force fields or types of molecules, it may be necessary to train a custom scoring function. This chapter describes the data and methods one must consider in developing a custom scoring function for use with AutoDock.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions

P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp–drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r²=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Ki_exp or pKi_exp; experimental Ki or negative decimal logarithm of Ki_exp) converted from experimental IC₅₀ (half maximal inhibitory concentration) values with POLSCORE-predicted constants (Ki_POLSCORE or pKi_POLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand-specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide-protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X-ray structure refinement programs.

Identification of substrate-binding and selectivity-related residues of maltooligosyltrehalose synthase from the thermophilic archaeon Sulfolobus solfataricus ATCC 35092

Maltooligosyltrehalose synthase (MTSase) is a key enzyme in the synthesis of trehalose. Computer simulations using AutoDock and NAMD were employed to assess the substrate-binding and selectivity-related residues of MTSase. We introduced mutations at residues D411, D610, and R614 to determine the substrate-binding residues of Sulfolobus solfataricus ATCC 35092 MTSase, and introduced mutations at residues P402, A406, and V426 to investigate the enzyme's selectivity-related residues. Kinetic studies of D411A, D610A, and R614A MTSases reveal significant reductions in catalytic efficiency and cause increase in the transition-state energy of mutant MTSases, indicating that residues D411, D610, and R614 form hydrogen bonds to the substrate. Compared with wild-type MTSase, the hydrolysis: transglycosylation selectivity ratio was significantly decreased for P402Q and significantly increased for A406S MTSases, while the ratio for V426T MTSase showed little change. The results suggest that P402 and A406 residues are selectivity-related.

Open Babel: An open chemical toolbox

Background

A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats.

Results

We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion.

Conclusions

Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.

Open Babel: An open chemical toolbox

BACKGROUND

A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats.

RESULTS

We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion.

CONCLUSIONS

Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein-carbohydrate interaction are reviewed.

Molecular simulations of carbohydrates and protein-carbohydrate interactions: motivation, issues and prospects

The characterization of the 3D structure of oligosaccharides, their conjugates and analogs is particularly challenging for traditional experimental methods. Molecular simulation methods provide a basis for interpreting sparse experimental data and for independently predicting conformational and dynamic properties of glycans. Here, we summarize and analyze the issues associated with modeling carbohydrates, with a detailed discussion of four of the most recently developed carbohydrate force fields, reviewed in terms of applicability to natural glycans, carbohydrate-protein complexes and the emerging area of glycomimetic drugs. In addition, we discuss prospectives and new applications of carbohydrate modeling in drug discovery.

Molecular docking of carbohydrate ligands to antibodies: structural validation against crystal structures

Cell surface glycoproteins play vital roles in cellular homeostasis and disease. Antibody recognition of glycosylation on different cells and pathogens is critically important for immune surveillance. Conversely, adverse immune reactions resulting from antibody-carbohydrate interactions have been implicated in the development of autoimmune diseases and impact areas such as xenotransplantation and cancer treatment. Understanding the nature of antibody-carbohydrate interactions and the method by which saccharides fit into antibody binding sites is important in understanding the recognition process. In silico techniques offer attractive alternatives to experimental methods (X-ray crystallography and NMR) for the study of antibody-carbohydrate complexes. In particular, molecular docking provides information about protein-ligand interactions in systems that are difficult to study with experimental techniques. Before molecular docking can be used to investigate antibody-carbohydrate complexes, validation of an appropriate docking method is required. In this study, four popular docking programs, Glide, AutoDock, GOLD, and FlexX, were assessed for their ability to accurately dock carbohydrates to antibodies. Comparison of top ranking poses with crystal structures highlighted the strengths and weaknesses of these programs. Rigid docking, in which the protein conformation remains static, and flexible docking, where both the protein and ligand are treated as flexible, were compared. This study has revealed that generally molecular docking of carbohydrates to antibodies has been performed best by Glide.

Barley xyloglucan xyloglucosyl transferases bind xyloglucan-derived oligosaccharides in their acceptor-binding regions in multiple conformational states

Three barley xyloglucan endotransglycosylases (HvXETs), known as xyloglucan xyloglucosyl transferases (EC 2.4.1.207), were subjected to kinetic and computational docking studies. The k(cat) x K(m)(-1) values with the reduced [3H]-labelled XXXG, XXLG/XLXG and XLLG acceptor substrates were 0.02 x 10(-2), 0.1 x 10(-2) and 3.2 x 10(-2) s(-1) microM(-1), while the K(m) constants were 10.6, 8.6 and 5.3 mM, obtained for HvXET3, HvXET4 and HvXET6, respectively. Docking of XLLG in acceptor-binding regions revealed that at least two conformational states were likely to participate in all isoforms. The assessments of kinetic and computational data indicated that the disposition of aromatic residues at the entrance to the active sites and the flexibility of proximal COOH-terminal loops could orient acceptors more or less favourably during binding, thus leading to tighter or weaker K(m) constants. The data suggested that binding of acceptors in HvXETs is guided by contributions from the conserved residues in the active sites and by the of neighbouring loops.