A Computational Study of Tetrafluoro-[2.2]Cyclophanes

A computational study of the isomers of tetrafluorinated [2.2]cyclophanes persubstituted in one ring, namely F-4-[2.2]paracyclophane (4), F-4-anti-[2.2]metacyclophane (5a), F-4-syn-[2.2]metacyclophane (5b), and F-4-[2.2]metaparacyclophane (6a and 6b), was carried out. The effects of fluorination on the geometries, relative energies, local and global aromaticity, and strain energies of the bridges and rings were investigated. An analysis of the electron density by B3PW91/6-31+G(d,p), B3LYP/6-31+G(d,p), and MP2/6-31+G(d,p) was carried out using the natural bond orbitals (NBO), natural steric analysis (NSA), and atoms in molecules (AIM) methods. The analysis of frontier molecular orbitals (MOs) was also employed. The results indicated that the molecular structure of [2.2]paracyclophane is the most affected by the fluorination. Isodesmic reactions showed that the fluorinated rings are more strained than the nonfluorinated ones. The NICS, HOMA, and PDI criteria evidenced that the fluorination affects the aromaticity of both the fluorinated and the nonfluorinated rings. The NBO and NSA analyses gave an indication that the fluorination increases not only the number of through-space interactions but also their magnitude. The AIM analysis suggested that the through-space interactions are restricted to the F-4-[2.2]metacyclophanes. In addition, the atomic properties, computed over the atomic basins, shave evidence that not only the substitution, but also the position of the bridges could affect the atomic charges. the first atomic moments, and the atomic volumes.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

The GHEP-EMPOP collaboration on mtDNA population data-A new resource for forensic casework

Mitochondrial DNA (mtDNA) population data for forensic purposes are still scarce for some populations, which may limit the evaluation of forensic evidence especially when the rarity of a haplotype needs to be determined in a database search. In order to improve the collection of mtDNA lineages from the Iberian and South American subcontinents, we here report the results of a collaborative study involving nine laboratories from the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) and EMPOP. The individual laboratories contributed population data that were generated throughout the past 10 years, but in the majority of cases have not been made available to the scientific community. A total of 1019 haplotypes from Iberia (Basque Country, 2 general Spanish populations, 2 North and 1 Central Portugal populations), and Latin America (3 populations from Sao Paulo) were collected, reviewed and harmonized according to defined EMPOP criteria. The majority of data ambiguities that were found during the reviewing process (41 in total) were transcription errors confirming that the documentation process is still the most error-prone stage in reporting mtDNA population data, especially when performed manually. This GHEP-EMPOP collaboration has significantly improved the quality of the individual mtDNA datasets and adds mtDNA population data as valuable resource to the EMPOP database (www.empop.org). (C) 2010 Elsevier Ireland Ltd. All rights reserved.