Static energy analysis of MHC class I and class II peptide-binding affinity

Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.

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.

An unusual ruthenium(II) complex of 2-(2-pyridyl)benzothiazole

The ligand 2-(2-pyridyl)benzothiazole (L) can act both as an N-N and an N-S chelating donor. The latter coordination mode is expected to be preferred when it is involved in coordination to Ru(II) which is a soft acceptor centre However, in the title compound, chlorobis(acetonitrile)triphenylphosphino-2-(2-pyridyl)benzothiazole-N,N-ruthenium(II) chlride, [Ru(L)(PPh3(CH3CN)2Cl]Cl, the ligand acts in N,N-bidentate manner and the Ru(II) ion is found to be present in an N4PCl coordination environment. PPh3 and Cl are trans to each other and the two CH3CN ligands occupy cis positions facing the NN donor atoms of ligand L.

Spin transition of 1D, 2D and 3D iron(II) complex polymers - the tug-of-war between elastic interaction and a shock-absorber effect

The structures of linear chain Fe(II) spin-crossover compounds of α,β- and α,ω-bis (tetrazol-1-yl)alkane type ligands are described in relation to their magnetic properties. The first threefold interlocked 3-D catenane Fe(II) spin-transition system, [μ-tris(1,4-bis(tetrazol-1-yl)butane-N1,N1′) iron(II)] bis(perchlorate), will be discussed. An analysis is made among the structures and the cooperativity of the spin-crossover behaviour of polynuclear Fe(II) spin-transition materials.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. 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 freely available online (http://www.jenner.ac.uk/MHCPred).

Efficient 1,4-addition of enones and boronic acids catalyzed by a Ni-Zn hydroxyl double salt-intercalated anionic rhodium(III) complex

Intercalation of an in situ prepared [Rh(OH)6]3- complex into an anion exchangeable Ni-Zn layered hydroxy double salt (Rh/NiZn) was demonstrated. The resulting Rh/NiZn effectively catalyzed the 1,4-addition of diverse enones and phenylboronic acids to their corresponding β-substituted carbonyl compounds. In the case of 2-cyclohexen-1-one and phenylboronic acid, a turnover frequency (TOF) of 920 h-1 based on Rh was achieved. The [Rh(OH)6]3- complex maintained its original monomeric trivalent state within the NiZn interlayer following catalysis, attributable to a strong electrostatic interaction between the NiZn host and anionic Rh(III) complex.