Structural studies of trans-N2S2 copper macrocycles

The X-ray crystal structures of two related trans-N2S2 copper macrocycles are reported. One was isolated with the copper in the divalent form and the other with copper in its univalent form affording a valuable insight into the changes of geometry and metrical parameters that occur during redox processes in macrocyclic copper complexes. A variable temperature NMR study of the copper(I) complex is reported, indicative of a chair-boat conformational change within the alkyl chain backbone of the macrocycle. It was possible to extract the relevant kinetic and thermodynamic parameters (?G‡, 57.8 kJ mol-1; ?H‡, 52.1 kJ mol-1; ?S‡, -19.2 J K-1 mol-1) for this process at 298 K. DFT molecular orbital calculations were used to confirm these observations and to calculate the energy difference (26.2 kJmol-1) between the copper(I) macrocycle in a planar and a distorted tetrahedral disposition.

Design and implementation of graphical user interfaces for complex simulations

The development of increasingly powerful computers, which has enabled the use of windowing software, has also opened the way for the computer study, via simulation, of very complex physical systems. In this study, the main issues related to the implementation of interactive simulations of complex systems are identified and discussed. Most existing simulators are closed in the sense that there is no access to the source code and, even if it were available, adaptation to interaction with other systems would require extensive code re-writing. This work aims to increase the flexibility of such software by developing a set of object-oriented simulation classes, which can be extended, by subclassing, at any level, i.e., at the problem domain, presentation or interaction levels. A strategy, which involves the use of an object-oriented framework, concurrent execution of several simulation modules, use of a networked windowing system and the re-use of existing software written in procedural languages, is proposed. A prototype tool which combines these techniques has been implemented and is presented. It allows the on-line definition of the configuration of the physical system and generates the appropriate graphical user interface. Simulation routines have been developed for the chemical recovery cycle of a paper pulp mill. The application, by creation of new classes, of the prototype to the interactive simulation of this physical system is described. Besides providing visual feedback, the resulting graphical user interface greatly simplifies the interaction with this set of simulation modules. This study shows that considerable benefits can be obtained by application of computer science concepts to the engineering domain, by helping domain experts to tailor interactive tools to suit their needs.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

New horizons in mouse immunoinformatics:reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure–activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide–protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2–Db, H2–Kb and H2–Kk. As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online (http://www.jenner.ac.uk/MHCPred).