Antibody-protein interactions: benchmark datasets and prediction tools evaluation

linked profile(s): juliap
submitted by: apryl
Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction...
Authors: Julia v Ponomarenko, Philip e Bourne

Protein interactions, targeted drug design, and pharmacogenetics - Prof. Timothy Palzkill

submitted by: ralanharris

Identifying protein interactions suitable for therapeutic intervention. Design of short peptides and peptidomimetics. Pharmacogenetics. Part of the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Protein interaction networks - Prof. Yin Liu

submitted by: ralanharris

Inference of gene modules and protein interaction networks using using synthetic lethality method (e.g., Pan X et al, Cell 124 1069-1081, 2006) the yeast-two-hybrid method and homology with model organisms. Part of the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

Structural analysis of the evolution of steroid specificity in the mineralocorticoid and glucocorticoid receptors

submitted by: Michael Baker
Background The glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) evolved from a common ancestor. Still not completely understood is how specificity for glucocorticoids (e.g. cortisol) and mineralocorticoids (e.g. aldosterone) evolved in these receptors. Results Our analysis of several vertebrate GRs and MRs in the context of 3D structures of human GR and MR indicates that with the exception of skate GR, a cartilaginous fish, there is a...
Authors: Michael e Baker, Charlie Chandsawangbhuwana, Noah Ollikainen

Computational Lab - Cristian Coarfa

submitted by: ralanharris

Expression profiles of osteosarcoma that can predict response to chemotherapy. Lab for the Computer-Aided Discovery Methods course taught at Baylor College of Medicine.

HIV Dermatology

submitted by: camdic

Objective: Study of cutaneous displays found during AIDS, spy of early diagnosis of disease.