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.

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.

Design and development of a time of flight fast scattering spectrometer : a quantitative surface analysis technique and a new approach towards the experimental investigation of the surface particle interactions

A new surface analysis technique has been developed which has a number of benefits compared to conventional Low Energy Ion Scattering Spectrometry (LEISS). A major potential advantage arising from the absence of charge exchange complications is the possibility of quantification. The instrumentation that has been developed also offers the possibility of unique studies concerning the interaction between low energy ions and atoms and solid surfaces. From these studies it may also be possible, in principle, to generate sensitivity factors to quantify LEISS data. The instrumentation, which is referred to as a Time-of-Flight Fast Atom Scattering Spectrometer has been developed to investigate these conjecture in practice. The development, involved a number of modifications to an existing instrument, and allowed samples to be bombarded with a monoenergetic pulsed beam of either atoms or ions, and provided the capability to analyse the spectra of scattered atoms and ions separately. Further to this a system was designed and constructed to allow incident, exit and azimuthal angles of the particle beam to be varied independently. The key development was that of a pulsed, and mass filtered atom source; which was developed by a cyclic process of design, modelling and experimentation. Although it was possible to demonstrate the unique capabilities of the instrument, problems relating to surface contamination prevented the measurement of the neutralisation probabilities. However, these problems appear to be technical rather than scientific in nature, and could be readily resolved given the appropriate resources. Experimental spectra obtained from a number of samples demonstrate some fundamental differences between the scattered ion and neutral spectra. For practical non-ordered surfaces the ToF spectra are more complex than their LEISS counterparts. This is particularly true for helium scattering where it appears, in the absence of detailed computer simulation, that quantitative analysis is limited to ordered surfaces. Despite this limitation the ToFFASS instrument opens the way for quantitative analysis of the 'true' surface region to a wider range of surface materials.

In-situ measurement and the reconstruction in 3D of femtosecond inscription induced complex permittivity modification in glass

We demonstrate a new approach to in-situ measurement of femtosecond laser pulse induced changes in glass enabling the reconstruction in 3D of the induced complex permittivity modification. The technique can be used to provide single shot and time resolved quantitative measurements with a micron scale spatial resolution.

Innovation in teams : a qualitative and quantitative study of team behaviours

This thesis explores the processes of team innovation. It utilises two studies, an organisationally based pilot and an experimental study, to examine and identify aspects of teams' behaviours that are important for successful innovative outcome. The pilot study, based in two automotive manufacturers, involved the collection of team members' experiences through semi-structured interviews, and identified a number of factors that affected teams' innovative performance. These included: the application of ideative & dissemination processes; the importance of good team relationships, especially those of a more informal nature, in facilitating information and ideative processes; the role of external linkages in enhancing quality and radicality of innovations; and the potential attenuation of innovative ideas by time deadlines. This study revealed a number key team behaviours that may be important in successful innovation outcomes. These included; goal setting, idea generation and development, external contact, task and personal information exchange, leadership, positive feedback and resource deployment. These behaviours formed the basis of a coding system used in the second part of the research. Building on the results from the field based research, an experimental study was undertaken to examine the behavioural differences between three groups of sixteen teams undertaking innovative an task to produce an anti-drugs poster. They were randomly assigned to one of three innovation category conditions suggested by King and Anderson (1990), emergent, imported and imposed. These conditions determined the teams level of access to additional information on previously successful campaigns and the degree of freedom they had with regarding to the design of the poster. In addition, a further experimental condition was imposed on half of the teams per category which involved a formal time deadline for task completion. The teams were video taped for the duration of their innovation and their behaviours analysed and coded in five main aspects including; ideation, external focus, goal setting, interpersonal, directive and resource related activities. A panel of experts, utilising five scales developed from West and Anderson's (1996) innovation outcome measures, assessed the teams' outputs. ANOVAs and repeated measure ANOVAs were deployed to identify whether there were significant differences between the different conditions. The results indicated that there were some behavioural differences between the categories and that over the duration of the task behavioural changes were identified. The results, however, revealed a complex picture and suggested limited support for three distinctive innovation categories. There were many differences in behaviours, but rarely between more than two of the categories. A main finding was the impact that different levels of constraint had in changing teams' focus of attention. For example, emergent teams were found to use both their own team and external resources, whilst those who could import information about other successful campaigns were likely to concentrate outside the team and pay limited attention to the internal resources available within the team. In contrast, those operating under task constraints with aspects of the task imposed onto them were more likely to attend to internal team resources and pay limited attention to the external world. As indicated by the earlier field study, time deadlines did significantly change teams' behaviour, reducing ideative and information exchange behaviours. The model shows an important behavioural progression related to innovate teams. This progression involved the teams' openness initially to external sources, and then to the intra-team environment. Premature closure on the final idea before their mid-point was found to have a detrimental impact on team's innovation. Ideative behaviour per se was not significant for innovation outcome, instead the development of intra-team support and trust emerged as crucial. Analysis of variance revealed some limited differentiation between the behaviours of teams operating under the aforementioned three innovation categories. There were also distinct detrimental differences in the behaviour of those operating under a time deadline. Overall, the study identified the complex interrelationships of team behaviours and outcomes, and between teams and their context.

Quantitative risk management aids refinery construction

Conventional project management techniques are not always sufficient to ensure that schedule, cost and quality goals are met on large-scale construction projects. These jobs require complex planning, designing and implementation processes. The main reasons for a project's nonachievement in India's hydrocarbon processing industry are changes in scope and design, altered government policies and regulations, unforeseen inflation, under and/or improper estimation. Projects that are exposed to such an uncertain environment can be effectively managed by applying risk management throughout the project life cycle.

Psychological traits influence autonomic nervous system recovery following esophageal intubation in health and functional chest pain

Background: Esophageal intubation is a widely utilized technique for a diverse array of physiological studies, activating a complex physiological response mediated, in part, by the autonomic nervous system (ANS). In order to determine the optimal time period after intubation when physiological observations should be recorded, it is important to know the duration of, and factors that influence, this ANS response, in both health and disease. Methods: Fifty healthy subjects (27 males, median age 31.9 years, range 20-53 years) and 20 patients with Rome III defined functional chest pain (nine male, median age of 38.7 years, range 28-59 years) had personality traits and anxiety measured. Subjects had heart rate (HR), blood pressure (BP), sympathetic (cardiac sympathetic index, CSI), and parasympathetic nervous system (cardiac vagal tone, CVT) parameters measured at baseline and in response to per nasum intubation with an esophageal catheter. CSI/CVT recovery was measured following esophageal intubation. Key Results: In all subjects, esophageal intubation caused an elevation in HR, BP, CSI, and skin conductance response (SCR; all p < 0.0001) but concomitant CVT and cardiac sensitivity to the baroreflex (CSB) withdrawal (all p < 0.04). Multiple linear regression analysis demonstrated that longer CVT recovery times were independently associated with higher neuroticism (p < 0.001). Patients had prolonged CSI and CVT recovery times in comparison to healthy subjects (112.5 s vs 46.5 s, p = 0.0001 and 549 s vs 223.5 s, p = 0.0001, respectively). Conclusions & Inferences: Esophageal intubation activates a flight/flight ANS response. Future studies should allow for at least 10 min of recovery time. Consideration should be given to psychological traits and disease status as these can influence recovery. The psychological trait of neuroticism retards autonomic recovery following esophageal intubation in health and functional chest pain. © 2013 John Wiley & Sons Ltd.

Genome-wide screening for DNA variants associated with reading and language traits

Reading and language abilities are heritable traits that are likely to share some genetic influences with each other. To identify pleiotropic genetic variants affecting these traits, we first performed a genome-wide association scan (GWAS) meta-analysis using three richly characterized datasets comprising individuals with histories of reading or language problems, and their siblings. GWAS was performed in a total of 1862 participants using the first principal component computed from several quantitative measures of reading- and language-related abilities, both before and after adjustment for performance IQ. We identified novel suggestive associations at the SNPs rs59197085 and rs5995177 (uncorrected P≈10 for each SNP), located respectively at the CCDC136/FLNC and RBFOX2 genes. Each of these SNPs then showed evidence for effects across multiple reading and language traits in univariate association testing against the individual traits. FLNC encodes a structural protein involved in cytoskeleton remodelling, while RBFOX2 is an important regulator of alternative splicing in neurons. The CCDC136/FLNC locus showed association with a comparable reading/language measure in an independent sample of 6434 participants from the general population, although involving distinct alleles of the associated SNP. Our datasets will form an important part of on-going international efforts to identify genes contributing to reading and language skills. Genome-wide association scan meta-analysis for reading and language ability. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

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.

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).

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

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).

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0:a quantitative molecular docking study

Background: HLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 - 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0. Results: The X-ray structure of the peptide - HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides. Conclusions: The higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His 79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins. © 2012 Patronov et al.; licensee BioMed Central Ltd.