Investigations on the binding specificity of ß-galactoside analogues with human galectin-1 using molecular dynamics simulations.

Galectin-1 (Gal-1) is the first member of galectin family, which has a carbohydrate recognition domain, specifically binds towards ß-galactoside containing oligosaccharides. Owing its association with carbohydrates, Gal-1 is involved in many biological processes such as cell signaling, adhesion and pathological pathways such as metastasis, apoptosis and increased tumour cell survival. The development of ß-galactoside based inhibitors would help to control the Gal-1 expression. In the current study, we carried out molecular dynamics (MD) simulations to examine the structural and dynamic behaviour Gal-1-thiodigalactoside (TDG), Gal-1-lactobionic acid (LBA) and Gal-1-beta-(1→6)-galactobiose (G16G) complexes. The analysis of glycosidic torsional angles revealed that ß-galactoside analogues TDG and LBA have a single binding mode (BM1) whereas G16G has two binding modes (BM1 and BM2) for interacting with Gal-1 protein. We have computed the binding free energies for the complexes Gal-1-TDG, Gal-1-LBA and Gal-1-G16G using MM/PBSA and are -6.45, -6.22 and -3.08 kcal/mol, respectively. This trend agrees well with experiments that the binding of Gal-1 with TDG is stronger than LBA. Further analysis revealed that the interactions due to direct and water-mediated hydrogen bonds play a significant role to the structural stability of the complexes. The result obtained from this study is useful to formulate a set of rules and derive pharmacophore-based features for designing inhibitors against galectin-1.Communicated by Ramaswamy H. Sarma.

Prediction of protein-carbohydrate complex binding affinity using structural features.

Protein-carbohydrate interactions play a major role in several cellular and biological processes. Elucidating the factors influencing the binding affinity of protein-carbohydrate complexes and predicting their free energy of binding provide deep insights for understanding the recognition mechanism. In this work, we have collected the experimental binding affinity data for a set of 389 protein-carbohydrate complexes and derived several structure-based features such as contact potentials, interaction energy, number of binding residues and contacts between different types of atoms. Our analysis on the relationship between binding affinity and structural features revealed that the important factors depend on the type of the complex based on number of carbohydrate and protein chains. Specifically, binding site residues, accessible surface area, interactions between various atoms and energy contributions are important to understand the binding affinity. Further, we have developed multiple regression equations for predicting the binding affinity of protein-carbohydrate complexes belonging to six categories of protein-carbohydrate complexes. Our method showed an average correlation and mean absolute error of 0.731 and 1.149 kcal/mol, respectively, between experimental and predicted binding affinities on a jackknife test. We have developed a web server PCA-Pred, Protein-Carbohydrate Affinity Predictor, for predicting the binding affinity of protein-carbohydrate complexes. The web server is freely accessible at https://web.iitm.ac.in/bioinfo2/pcapred/. The web server is implemented using HTML and Python and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera.

ProCaff: protein-carbohydrate complex binding affinity database.

MOTIVATION: Protein-carbohydrate interactions perform several cellular and biological functions and their structure and function are mainly dictated by their binding affinity. Although plenty of experimental data on binding affinity are available, there is no reliable and comprehensive database in the literature. RESULTS: We have developed a database on binding affinity of protein-carbohydrate complexes, ProCaff, which contains 3122 entries on dissociation constant (Kd), Gibbs free energy change (ΔG), experimental conditions, sequence, structure and literature information. Additional features include the options to search, display, visualization, download and upload the data. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://web.iitm.ac.in/bioinfo2/procaff/. The website is implemented using HTML and PHP and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera. CONTACT: gromiha@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular dynamics of sialic acid analogues complex with cholera toxin and DFT optimization of ethylene glycol-mediated zinc nanocluster conjugation.

Cholera is an infectious disease caused by cholera toxin (CT) protein of bacterium Vibrio cholerae. A sequence of sialic acid (N-acetylneuraminic acid, NeuNAc or Neu5Ac) analogues modified in its C-5 position is modelled using molecular modelling techniques and docked against the CT followed by molecular dynamics simulations. Docking results suggest better binding affinity of NeuNAc analogue towards the binding site of CT. The NeuNAc analogues interact with the active site residues GLU:11, TYR:12, HIS:13, GLY:33, LYS:34, GLU:51, GLN:56, HIE:57, ILE:58, GLN:61, TRP:88, ASN:90 and LYS:91 through intermolecular hydrogen bonding. Analogues N-glycolyl-NeuNAc, N-Pentanoyl-NeuNAc and N-Propanoyl-NeuNAc show the least XPGscore (docking score) of -9.90, -9.16, and -8.91, respectively, and glide energy of -45.99, -42.14 and -41.66 kcal/mol, respectively. Stable nature of CT-N-glycolyl-NeuNAc, CT-N-Pentanoyl-NeuNAc and CT-N-Propanoyl-NeuNAc complexes was verified through molecular dynamics simulations, each for 40 ns using the software Desmond. All the nine NeuNAc analogues show better score for drug-like properties, so could be considered as suitable candidates for drug development for cholera infection. To improve the enhanced binding mode of NeuNAc analogues towards CT, the nine NeuNAc analogues are conjugated with Zn nanoclusters through ethylene glycol (EG) as carriers. The NeuNAc analogues conjugated with EG-Zn nanoclusters show better binding energy towards CT than the unconjugated nine NeuNAc analogues. The electronic structural optimization of EG-Zn nanoclusters was carried out for optimizing their performance as better delivery vehicles for NeuNAc analogues through density functional theory calculations. These sialic acid analogues may be considered as novel leads for the design of drug against cholera and the EG-Zn nanocluster may be a suitable carrier for sialic acid analogues.