Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results: A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations

Quantum mechanics studies of the tautomers of diacetylformoin, an important maillard product and odorant

Diacetylformoin (3,4-dihydroxy-3-hexene-2,5-dione) has 16 tautomers, many with several possible conformations and all have been geometry optimised using quantum mechanics at the HF/6-31+G* level. Eleven structures have been identified with energies within 10 kcal mol(-1) of the minimum energy structure. Of these eight are acyclic and three cyclic. Calculations of NMR spectra have clarified the identity of the acyclic and cyclic structures found experimentally. The mechanism for cyclisation has been investigated and transition states obtained. The lowest energy reaction path requires the loss and gain of a proton during cyclisation. (c) 2006 Elsevier B.V. All rights reserved

On the hydrophobicity of peptides: comparing empirical predictions of peptide log P values

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis’s LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r2 values, with ALogP being the most effective (r2 = 0.822) and MLogP the least (r2 = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides – ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides – PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides – QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides – LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted

Quantitative Structure-Activity Relationships of Sweet Isovanillyl Derivatives

Isovanillyl derivatives constitute a large class of sweet compounds in which there is a high degree of structural similarity and a wide range of biological activity, the relative sweetness RS spanning from 50 to 10000 times with respect to sucrose. This paper describes the results obtained by applying statistical models to develop QSARs for these derivatives. For a set of 14 compounds (set 1) appropriate physicochemical parameters for regression equations were selected using the genetic algorithm method. The best equation indicates a very close correlation (N = 14, ND = 5, r(2) = 0.982, r(cv)(2), = 0.942, LOF = 0.074, PRESS = 0.271, S(PRESS) = 0.184, S(DEP) = 0.139). Good results have also been obtained by Molecular Field Analysis (MFA) applied to the same set of compounds (N = 14, ND = 4, r(2) = 0.957, r(cv)(2) = 0.925, LOF = 0.044, PRESS = 0.348, S(PRESS) = 0.196, S(DEP) = 0.158) QSARS have also been derived for a larger set of 41 compounds (set 2, including set 1, plus other 27 compounds) with a much larger variety of structural types. These compounds have been divided into a training set of 35 compounds and a test set of 6 compounds. The most significant QSAR obtained using physicochemical parameters (N = 35, ND = 6, r(2) = 0.673, r(cv)(2) = 0.522, LOF 0.337, PRESS = 7.432, S(PRESS) = 0.515, S(DEP) = 0.461) proved less successful than one using MFA parameters (N = 35, ND = 6, r(2) = 0.746, r(cv)(2) = 0.607, LOF 0.261, PRESS = 6.110, S(PRESS) = 0.467, S(DEP) = 0.418). PRESS values for the test set were 4.079 and 1.962 respectively showing that the MFA data had more predictive power. Equations with different numbers of descriptors were compared and it was concluded that the LOF which is dependent upon the number of parameters used as well as the sum of squares is a suitable measure of equation quality. These equations were also validated by scrambling the experimental data which gave significantly worse agreement than the real data except when an excessive number of descriptors was use

Synthesis of heterosulfamates. Search for structure-taste relationships

Thirty one new sodium heterosulfamates, RNHSO3Na, where the R portion contains mainly thiazole, benzothiazole, thiadiazole and pyridine ring structures, have been synthesized and their taste portfolios have been assessed. A database of 132 heterosulfamates ( both open-chain and cyclic) has been formed by combining these new compounds with an existing set of 101 heterosulfamates which were previously synthesized and for which taste data are available. Simple descriptors have been obtained using (i) measurements with Corey-Pauling-Koltun (CPK) space- filling models giving x, y and z dimensions and a volume VCPK, (ii) calculated first order molecular connectivities ((1)chi(v)) and (iii) the calculated Spartan program parameters to obtain HOMO, LUMO energies, the solvation energy E-solv and V-SPART AN. The techniques of linear (LDA) and quadratic (QDA) discriminant analysis and Tree analysis have then been employed to develop structure-taste relationships (SARs) that classify the sweet (S) and non-sweet (N) compounds into separate categories. In the LDA analysis 70% of the compounds were correctly classified ( this compares with 65% when the smaller data set of 101 compounds was used) and in the QDA analysis 68% were correctly classified ( compared to 80% previously). TheTree analysis correctly classified 81% ( compared to 86% previously). An alternative Tree analysis derived using the Cerius2 program and a set of physicochemical descriptors correctly classified only 54% of the compounds