Biomedical subjects

R R Mallios

Publications and source records attributed to R R Mallios.

6 recordsLinked to original sources

PubMed2001

Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: Predicting peptides that bind to both Major Histocompatibility Complex (MHC) molecules and T cell receptors provides crucial information for vaccine development. An agretope is that portion of a peptide that interacts with an MHC molecule. The identification and prediction of agretopes is the first step towards vaccine design. RESULTS: An iterative stepwise discriminant analysis meta-algorithm is utilized to derive a quantitative motif for classifying potential agretopes as high-, moderate- or non-binders for HLA-DR1, a class II MHC molecule. A large molecular online database provides the input for this data-driven algorithm. The model correctly classifies over 85% of the peptides in the database. AVAILABILITY: Stepwise discriminant analysis software is available commercially in SPSS and BMDP statistical software packages. Peptides known to bind MHC molecules can be downloaded from http://wehih.wehi.edu.au/mhcpep/. Peptides known not to bind HLA-DR1 are available from the author upon request. CONTACT: ronna@ucsfresno.edu.

Algorithms↗

PubMed1999

Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm.

MOTIVATION: The identification of T-cell epitopes can be crucial for vaccine development. An epitope is a peptide segment that binds to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. Predicting which peptide segments bind MHC molecules is the first step in epitope prediction. RESULTS: An iterative stepwise discriminant analysis meta-algorithm explores a large molecular database to derive quantitative motifs for peptide binding. The applications presented here demonstrate the algorithm's versatility by producing four closely related models for HLA-DR1. Two models use an expert initial estimate and two do not; two models use amino acid residues as the only predictors and two use amino acid groupings as additional predictors. Each model correctly classifies >90% of the peptides in the database. AVAILABILITY: Software is available commercially; data are free over the Internet.

Algorithms↗

PubMed1998

Iterative stepwise discriminant analysis: a meta-algorithm for detecting quantitative sequence motifs.

An algorithm is presented for detecting a quantitative pattern in peptide fragments that bind class II major histocompatibility complex (MHC) molecules. It is referred to as a meta-algorithm because it requires successive applications of Stepwise Discriminate Analysis (SDA). On every iteration the best subsequence candidates are selected from sequences known to bind class II MHC molecules. When SDA compares probable binding subsequences with subsequences known not to bind class II MHC molecules, a quantitative model emerges that is capable of classifying subsequences as binding or non-binding. In an iterative manner, the resultant model is utilized as a criterion for selecting probable binding subsequence candidates. The procedure is repeated until models converge. In the illustrated examples, the final models correctly classify over 95% of the peptides in a database of peptides whose binding affinity for HLA-DR1 is known. The final model can then be used to predict the binding affinity of peptides that have not yet been laboratory tested.

Algorithms↗

PubMed1997

An iterative algorithm for converting a class II MHC binding motif into a quantitative predictive model.

Biochemists and molecular biologists have suggested motifs for characterizing the binding of peptide fragments and class II major histocompatibility complex (MHC) molecules based on laboratory results and crystal structures. The iterative algorithm presented here converts a suggested motif into a quantitative data-based model. The database accessed consists of peptide fragments known to bind or not bind to class II MHC molecules of particular haplotypes. Stepwise discriminant analysis is utilized to increase or decrease motif coefficients until the resulting motif classifies all binders and non-binders correctly. Stepwise discriminant analysis is a standard multivariate statistical procedure and is available in comprehensive commercial statistical packages. Program 7M of BMDP Statistical Software was used in this study.

Algorithms↗

PubMed1994

Multiple regression analysis suggests motifs for class II MHC binding.

Multiple regression analysis is utilized to predict peptides that bind major histocompatibility complex (MHC) class II molecules of the d haplotype. The predictor variables, obtained from the primary sequence of protein fragments, include pairs of amino acid residues separated by zero to seven intervening residues. The resulting models demonstrate strong predictive validity, and have interpretations in accordance with recent discoveries regarding the binding mechanisms of class I MHC molecules.

Amino Acid Sequence↗

PubMed1993

Predicting the probability of helper T cell immunodominant sites through discriminant analysis.

Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.

Amino Acid Sequence↗