Epitope mapping of metuximab on CD147 using phage display and molecular docking.

Metuximab is the generic name of Licartin, a new drug for radioimmunotherapy of hepatocellular carcinoma. Although it is known to be a mouse monoclonal antibody against CD147, the complete epitope mediating the binding of metuximab to CD147 remains unknown. We panned the Ph.D.-12 phage display peptide library against metuximab and got six mimotopes. The following bioinformatics analysis based on mimotopes suggested that metuximab recognizes a conformational epitope composed of more than 20 residues. The residues of its epitope may include T28, V30, K36, L38, K57, F74, D77, S78, D79, D80, Q81, G83, S86, N98, Q100, L101, H102, G103, P104, V131, P132, and K191. The homology modeling of metuximab and the docking of CD147 to metuximab were also performed. Based on the top one docking model, the epitope was predicted to contain 28 residues: AGTVFTTV (23-30), I37, D45, E84, V88, EPMGTANIQLH (92-102), VPP (131-133), Q164, and K191. Almost half of the residues predicted on the basis of mimotope analysis also appear in the docking result, indicating that both results are reliable. As the predicted epitopes of metuximab largely overlap with interfaces of CD147-CD147 interactions, a structural mechanism of metuximab is proposed as blocking the formation of CD147 dimer.

Group testing for case identification with correlated responses.

This article examines group testing procedures where units within a group (or pool) may be correlated. The expected number of tests per unit (i.e., efficiency) of hierarchical- and matrix-based procedures is derived based on a class of models of exchangeable binary random variables. The effect on efficiency of the arrangement of correlated units within pools is then examined. In general, when correlated units are arranged in the same pool, the expected number of tests per unit decreases, sometimes substantially, relative to arrangements that ignore information about correlation.

HLArestrictor--a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides.

Traditionally, T cell epitope discovery requires considerable amounts of tedious, slow, and costly experimental work. During the last decade, prediction tools have emerged as essential tools allowing researchers to select a manageable list of epitope candidates to test from a larger peptide, protein, or even proteome. However, no current tools address the complexity caused by the highly polymorphic nature of the restricting HLA molecules, which effectively individualizes T cell responses. To fill this gap, we here present an easy-to-use prediction tool named HLArestrictor ( http://www.cbs.dtu.dk/services/HLArestrictor ), which is based on the highly versatile and accurate NetMHCpan predictor, which here has been optimized for the identification of both the MHC restriction element and the corresponding minimal epitope of a T cell response in a given individual. As input, it requires high-resolution (i.e., 4-digit) HLA typing of the individual. HLArestrictor then predicts all 8-11mer peptide binders within one or more larger peptides and provides an overview of the predicted HLA restrictions and minimal epitopes. The method was tested on a large dataset of HIV IFNγ ELIspot peptide responses and was shown to identify HLA restrictions and minimal epitopes for about 90% of the positive peptide/patient pairs while rejecting more than 95% of the negative peptide-HLA pairs. Furthermore, for 18 peptide/HLA tetramer validated responses, HLArestrictor in all cases predicted both the HLA restriction element and minimal epitope. Thus, HLArestrictor should be a valuable tool in any T cell epitope discovery process aimed at identifying new epitopes from infectious diseases and other disease models.

SYFPEITHI: database for searching and T-cell epitope prediction.

Reverse immunology has been used for about 12 years in order to identify T-cell epitopes from pathogens or tumor-associated antigens. In this chapter, we discuss the advantages and pitfalls of T-cell epitope prediction compared to classical experimental procedures such as epitope mapping and cloning experiments. We introduce our three established programs, SYFPEITHI, PAProc, and SNEP, which are freely accessible at no cost in the World Wide Web for the prediction of either HLA-peptide binding or proteasomal processing of antigens. We demonstrate the performance of our epitope prediction programs with several examples and in comparison to other epitope prediction programs available. We also reflect the actual possibilities and limitations of such computer-aided work.

Unique peptide identification of RNase A superfamily sequences based on reinforced merging algorithms.

Human ribonuclease A (RNaseA) superfamily consists of eight RNases with high similarity in which RNase2 and RNase3 share 76.7% identity. The evolutionary variation of RNases results in differential structures and functions of the enzymes. To distinguish the characteristics of each RNase, we developed reinforced merging algorithms (RMA) to rapidly identify the unique peptide motifs for each member of the highly conserved human RNaseA superfamily. Many motifs in RNase3 identified by RMA correlated well with the antigenic regions predicted by DNAStar. Two unique peptide motifs were experimentally confirmed to contain epitopes for monoclonal antibodies (mAbs) specifically against RNase3. Further analysis of homologous RNases in different species revealed that the unique peptide motifs were located at the correspondent positions, and one of these motifs indeed matched the epitope for a specific anti-bovine pancreatic RNaseA (bpRNaseA) antibody. Our method provides a useful tool for identification of unique peptide motifs for further experimental design. The RMA system is available and free for academic use at http://bioinfo.life.nthu.edu.tw/rma/ and http://spider.cs.ntou.edu.tw/bioinformatics/RMA.html.