Modeling Ligand−Receptor Interaction for Some MHC Class II HLA-DR4 Peptide Mimetic Inhibitors Using Several Molecular Docking and 3D QSAR Techniques

A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series

We introduce the "Prime-ligand" method for ranking ligands in congeneric series. The method employs a single scoring function, the OPLS-AA/GBSA molecular mechanics/implicit solvent model, for all stages of sampling and scoring. We evaluate the method using 12 test sets of congeneric series for which experimental binding data is available in the literature, as well as the structure of one member of the series bound to the protein. Ligands are "docked" by superimposing a common stem fragment among the compounds in the series using a crystal complex from the Protein Data Bank and sampling the conformational space of the variable region. Our results show good correlation between our predicted rankings and the experimental data for cases in which binding affinities differ by at least 1 order of magnitude. For 11 out of 12 cases, >90% of such ligand pairs could be correctly ranked, while for the remaining case, Factor Xa, 76% of such pairs were correctly ranked. A small number of compounds could not be docked using the current protocol because of the large size of functional groups that could not be accommodated by a rigid receptor. CPU requirements for the method, involving CPU minutes per ligand, are modest compared with more rigorous methods that use similar force fields, such as free energy perturbation. We also benchmark the scoring function using series of ligands bound to the same protein within the CSAR data set. We demonstrate that energy minimization of ligands in the crystal structures is critical to obtain any correlation with experimentally determined binding affinities.

Design of glycopeptides used to investigate class II MHC binding and T-cell responses associated with autoimmune arthritis

The glycopeptide fragment CII259–273 from type II collagen (CII) binds to the murine A^q and human DR4 class II Major Histocompatibility Complex (MHC II) proteins, which are associated with development of murine collagen-induced arthritis (CIA) and rheumatoid arthritis (RA), respectively. It has been shown that CII259–273 can be used in therapeutic vaccination of CIA. This glycopeptide also elicits responses from T-cells obtained from RA patients, which indicates that it has an important role in RA as well. We now present a methodology for studies of (glyco)peptide-receptor interactions based on a combination of structure-based virtual screening, ligand-based statistical molecular design and biological evaluations. This methodology included the design of a CII259–273 glycopeptide library in which two anchor positions crucial for binding in pockets of A^q and DR4 were varied. Synthesis and biological evaluation of the designed glycopeptides provided novel structure-activity relationship (SAR) understanding of binding to A^q and DR4. Glycopeptides that retained high affinities for these MHC II proteins and induced strong responses in panels of T-cell hybridomas were also identified. An analysis of all the responses revealed groups of glycopeptides with different response patterns that are of high interest for vaccination studies in CIA. Moreover, the SAR understanding obtained in this study provides a platform for the design of second-generation glycopeptides with tuned MHC affinities and T-cell responses.

pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes

Background

Identification of antigenic peptide epitopes is an essential prerequisite in T cell-based molecular vaccine design. Computational (sequence-based and structure-based) methods are inexpensive and efficient compared to experimental approaches in screening numerous peptides against their cognate MHC alleles. In structure-based protocols, suited to alleles with limited epitope data, the first step is to identify high-binding peptides using docking techniques, which need improvement in speed and efficiency to be useful in large-scale screening studies. We present pDOCK: a new computational technique for rapid and accurate docking of flexible peptides to MHC receptors and primarily apply it on a non-redundant dataset of 186 pMHC (MHC-I and MHC-II) complexes with X-ray crystal structures.

Results

We have compared our docked structures with experimental crystallographic structures for the immunologically relevant nonameric core of the bound peptide for MHC-I and MHC-II complexes. Primary testing for re-docking of peptides into their respective MHC grooves generated 159 out of 186 peptides with Cα RMSD of less than 1.00 Å, with a mean of 0.56 Å. Amongst the 25 peptides used for single and variant template docking, the Cα RMSD values were below 1.00 Å for 23 peptides. Compared to our earlier docking methodology, pDOCK shows upto 2.5 fold improvement in the accuracy and is ~60% faster. Results of validation against previously published studies represent a seven-fold increase in pDOCK accuracy.

Conclusions

The limitations of our previous methodology have been addressed in the new docking protocol making it a rapid and accurate method to evaluate pMHC binding. pDOCK is a generic method and although benchmarks against experimental structures, it can be applied to alleles with no structural data using sequence information. Our outcomes establish the efficacy of our procedure to predict highly accurate peptide structures permitting conformational sampling of the peptide in MHC binding groove. Our results also support the applicability of pDOCK for in silico identification of promiscuous peptide epitopes that are relevant to higher proportions of human population with greater propensity to activate T cells making them key targets for the design of vaccines and immunotherapies.

Class II HLA-peptide binding prediction using structural principles

The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.