Structure-based prediction of MHC-peptide association: algorithm comparison and application to cancer vaccine design.

Peptide vaccination for cancer immunotherapy requires identification of peptide epitopes derived from antigenic proteins associated with the tumor. Such peptides can bind to MHC proteins (MHC molecules) on the tumor-cell surface, with the potential to initiate a host immune response against the tumor. Computer prediction of peptide epitopes can be based on known motifs for peptide sequences that bind to a certain MHC molecule, on algorithms using experimental data as a training set, or on structure-based approaches.

WATGEN: an algorithm for modeling water networks at protein-protein interfaces.

Water molecules at protein-protein interfaces contribute to the close packing of atoms and ensure complementarity between the protein surfaces, as well as mediating polar interactions. Therefore, modeling of interface water is of importance in understanding the structural basis of biomolecular association. We present an algorithm, WATGEN, which predicts locations for water molecules at a protein-protein or protein-peptide interface, given the atomic coordinates of the protein and peptide.

Structural prediction of peptides binding to MHC class I molecules.

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2.

Rapid characterization of amyloid-beta side-chain oxidation by tandem mass spectrometry and the scoring algorithm for spectral analysis.

Purpose: Amyloid-beta (Abeta) is a self-aggregating protein found in senile plaques in Alzheimer's disease (AD) brain and is thought to play a major role in the disease process. Oxidative stress may be a predominant cause of the formation of these Abeta aggregates. This study aims at identifying possible sites of copper-catalyzed oxidation of Abeta1-40 using liquid chromatography tandem mass spectrometry (LC/MS/MS) and scoring algorithm for spectral analysis (SALSA).