PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes.

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein-carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.

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 simulation studies on influenza A virus H5N1 complexed with sialic acid and fluorinated sialic acid.

Abbreviations HA Hemagglutinin MD Molecular Dynamics MM-PBSA Molecular Mechanics Poisson-Boltzmann Surface Area NA Neuraminidase NAMD Nanoscale Molecular Dynamic Simulation PMEMD Particle Mesh Ewald Molecular Dynamics RMSD Root-Mean-Square Deviation RMSF Root-Mean-Square Fluctuation SIA sialic acid VMD Visual Molecular Dynamics Communicated by Ramaswamy H. Sarma.