RESUMO
To improve the availability of three-dimensional (3D) structures of HLA molecules, we created the pHLA3D database. In its first version, we modeled and published 106 3D structures of HLA class I molecules from the HLA-A, HLA-B, and HLA-C loci. This paper presents an update of this database, providing more 127 3D structures of HLA class II molecules (41 DR, 42 DQ, and 44 DP), predicted via homology modeling with MODELLER and SWISS-MODEL. These new 3D structures of HLA class II molecules are now freely available at pHLA3D (www.phla3d.com.br) for immunologists and other researchers working with HLA molecules.
Assuntos
Antígenos HLA-DP/ultraestrutura , Antígenos HLA-DQ/ultraestrutura , Antígenos HLA-DR/ultraestrutura , Biologia Computacional , Bases de Dados de Proteínas , Humanos , Estrutura Terciária de Proteína , Homologia de Sequência de Aminoácidos , SoftwareRESUMO
One of the challenges facing solid organ transplantation programs globally is the identification of low immunological risk donors for sensitized recipients by HLA allele genotype. Because recognition of donor HLA alleles by host antibodies is at the core of organ rejection, the objective of this work was to develop a new version of the EpHLA software, named EpViX, which uses an HLAMatchmaker algorithm and performs automated epitope virtual crossmatching at the initiation of the organ donation process. EpViX is a free, web-based application developed for use over the internet on a tablet, smartphone or computer. This program was developed using the Ruby programming language and the Ruby-on-Rails framework. To improve the user experience, the EpViX software interface was developed based on the best humancomputer interface practices. To simplify epitope analysis and virtual crossmatching, the program was integrated with important available web-based resources, such as OPTN, IMGT/HLA and the International HLA Epitope Registry. We successfully developed a program that allows people to work collaboratively and effectively during the donation process by accurately predicting negative crossmatches, saving time and other resources.