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.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

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).

Identifiying human MHC supertypes using bioinformatic methods

Classification of MHC molecules into supertypes in terms of peptide-binding specificities is an important issue, with direct implications for the development of epitope-based vaccines with wide population coverage. In view of extremely high MHC polymorphism (948 class I and 633 class II HLA alleles) the experimental solution of this task is presently impossible. In this study, we describe a bioinformatics strategy for classifying MHC molecules into supertypes using information drawn solely from three-dimensional protein structure. Two chemometric techniques–hierarchical clustering and principal component analysis–were used independently on a set of 783 HLA class I molecules to identify supertypes based on structural similarities and molecular interaction fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77. action fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77.

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

MHCPred : a server for quantitative prediction of peptide-MHC binding

Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-base multivariate statistical approach to the quantitative prediction of peptide binding to major histocom-patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive,cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203,HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401and HLA-DRB* 0701).

Histidine hydrogen bonding in MHC at pH 5 and pH 7 modeled by molecular docking and molecular dynamics simulations

Hydrogen bonds play important roles in maintaining the structure of proteins and in the formation of most biomolecular protein-ligand complexes. All amino acids can act as hydrogen bond donors and acceptors. Among amino acids, Histidine is unique, as it can exist in neutral or positively charged forms within the physiological pH range of 5.0 to 7.0. Histidine can thus interact with other aromatic residues as well as forming hydrogen bonds with polar and charged residues. The ability of His to exchange a proton lies at the heart of many important functional biomolecular interactions, including immunological ones. By using molecular docking and molecular dynamics simulation, we examine the influence of His protonation/deprotonation on peptide binding affinity to MHC class II proteins from locus HLA-DP. Peptide-MHC interaction underlies the adaptive cellular immune response, upon which the next generation of commercially-important vaccines will depend. Consistent with experiment, we find that peptides containing protonated His residues bind better to HLA-DP proteins than those with unprotonated His. Enhanced binding at pH 5.0 is due, in part, to additional hydrogen bonds formed between peptide His+ and DP proteins. In acidic endosomes, protein His79β is predominantly protonated. As a result, the peptide binding cleft narrows in the vicinity of His79β, which stabilizes the peptide - HLA-DP protein complex. © 2014 Bentham Science Publishers.

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Transcriptional Response of Two Core Photosystem Genes in Symbiodinium spp. Exposed to Thermal Stress

Mutualistic symbioses between scleractinian corals and endosymbiotic dinoflagellates (Symbiodinium spp.) are the foundation of coral reef ecosystems. For many coral-algal symbioses, prolonged episodes of thermal stress damage the symbiont's photosynthetic capability, resulting in its expulsion from the host. Despite the link between photosynthetic competency and symbiont expulsion, little is known about the effect of thermal stress on the expression of photosystem genes in Symbiodinium. This study used real-time PCR to monitor the transcript abundance of two important photosynthetic reaction center genes, psbA(encoding the D1 protein of photosystem II) and psaA (encoding the P700 protein of photosystem I), in four cultured isolates (representing ITS2-types A13, A20, B1, and F2) and two in hospite Symbiodinium spp. within the coral Pocillopora spp. (ITS2-types C1b-c and D1). Both cultured and in hospite Symbiodinium samples were exposed to elevated temperatures (32°C) over a 7-day period and examined for changes in photochemistry and transcript abundance. Symbiodinium A13 and C1b-c (both thermally sensitive) demonstrated significant declines in both psbA and psaA during the thermal stress treatment, whereas the transcript levels of the other Symbiodinium types remained stable. The downregulation of both core photosystem genes could be the result of several different physiological mechanisms, but may ultimately limit repair rates of photosynthetic proteins, rendering some Symbiodinium spp. especially susceptible to thermal stress.

Síntese e caracterização de complexo tetranuclear de cobre com o ligante resorcinareno

The main objective of the present work is to contribute to the development of the coordination chemistry of macromolecules such as resorcinarene with the synthesis and characterization of new copper complexes with chloride, vanillin and resorcinarene binders, all coordinated to phenanthroline, a biologically active molecule with important properties in biological systems. The complex [(Cu(phen))4(resvan)], was synthesized from the direct reaction of the metals with resorcinarene and generates several possibilities for coordination, which hinders its characterization. Therefore, in order to limit the coordination sites of the ligand, the complex [(Cu(phen))4(resvan)]Cl4 was formed from a new synthetic methodology. The complex cis-[Cu(phen)Cl2], cis-[Cu(phen)(van)]Cl, [(Cu(phen))4(resvan)] and [(Cu(phen))4(resvan)]Cl4 were characterized by spectroscopic techniques such as IR, UV-vis and EPR. By using infrared it has been possible to demonstrate the presence of the phenanthroline ligand in the synthesized complexes, and vanillin in the complex cis- [Cu(phen)(van)]Cl and resvan ligand in the complex [(Cu(phen))4(resvan)], besides this indicating the formation of resorcinarene in the complex [(Cu(phen))4(resvan)]Cl4. The electronic spectra of these coordination compounds indicated the presence of the phenanthroline ligand, by its intense bands in the ultraviolet region. For the complex cis- [Cu(phen)(van)]Cl it still indicated the presence of the ligand vanillin based on intraligand bands of vanillin and charge transfer, LMCT. Furthermore, the spectra showed d-d bands, confirming the formation of metal compounds. The amount of copper atoms present in the complex [(Cu(phen))4(resvan)]Cl4 was estimated from a comparative analysis of the absorbances of solutions of the same concentration of [(Cu(phen))4(resvan)]Cl4 and cis- [Cu(phen)(van)]Cl, which indicates that these compounds have copper atoms in the ratio 4:1. The EPR spectra of the complex cis-[Cu(phen)Cl2], cis-[Cu(phen)(van)]Cl and [(Cu(phen))4(resvan)]Cl4 showed axial profiles, while the complex [(Cu(phen))4(resvan)] showed of axial and rhombic profiles, indicating a change in the symmetry of the Cu (II) to this complex environment. The binders vanillin and resvan underwent biological assays with satisfactory results, both exhibited antioxidant activity and low toxicity, as well vanillin present antitoxoplásmico character.

Biostratigraphic Evidence Relating to the Age-Old Question of Hannibal's Invasion of Italy, II: Chemical Biomarkers and Microbial Signatures

As discussed in Part I, a large accumulation of mammalian faeces at the mire site in the upper Guil Valley near Mt. Viso, dated to 2168cal ¹⁴C yr., provides the first evidence of the passage of substantial but indeterminate numbers of mammals within the time frame of the Punic invasion of Italia. Specialized organic biomarkers bound up in a highly convoluted and bioturbated bed constitute an unusual anomaly in a histosol comprised of fibric and hemist horizons that are usually expected to display horizontal bedding. The presence of deoxycholic acid and ethylcoprostanol derived from faecal matter, coupled with high relative numbers of Clostridia 16S rRNA genes, suggests a substantial accumulation of mammalian faeces at the site over 2000years ago. The results reported here constitute the first chemical and biological evidence of the passage of large numbers of mammals, possibly indicating the route of the Hannibalic army at this time. Combined with the geological analysis reported in Part I, these data provide a background supporting the need for further historical archaeological exploration in this area.

A Phase II Biomarker-Embedded Study of Lapatinib plus Capecitabine as First-line Therapy in Patients with Advanced or Metastatic Gastric Cancer

An exploratory phase II biomarker-embedded trial (LPT109747; NCT00526669) designed to determine the association of lapatinib-induced fluoropyrimidine gene changes with efficacy of lapatinib plus capecitabine as first-line treatment for advanced gastric cancer or gastroesophageal junction adenocarcinoma independent of tumor HER2 status. Tumor biopsies obtained before and after 7-day lapatinib (1,250 mg) to analyze changes in gene expression, followed by a 14-day course of capecitabine (1,000 mg/m(2) twice daily, 14/21 days) plus lapatinib 1,250 mg daily. Blood samples were acquired for pharmacokinetic analysis. Primary clinical objectives were response rate (RR) and 5-month progression-free survival (PFS). Secondary objectives were overall survival (OS), PFS, time to response, duration of response, toxicity, and identification of associations between lapatinib pharmacokinetics and biomarker endpoints. Primary biomarker objectives were modulation of 5-FU-pathway genes by lapatinib, effects of germline SNPs on treatment outcome, and trough steady-state plasma lapatinib concentrations. Sixty-eight patients were enrolled; (75% gastric cancer, 25% gastroesophageal junction). Twelve patients (17.9%) had confirmed partial response, 31 (46.3%) had stable disease, and 16 (23.9%) had progressive disease. Median PFS and OS were 3.3 and 6.3 months, respectively. Frequent adverse events included diarrhea (45%), decreased appetite (39%), nausea (36%), and fatigue (36%). Lapatinib induced no changes in gene expression from baseline and no significant associations were found for SNPs analyzed. Elevated baseline HER3 mRNA expression was associated with a higher RR (33% vs. 0%; P = 0.008). Lapatinib plus capecitabine was well tolerated, demonstrating modest antitumor activity in patients with advanced gastric cancer. The association of elevated HER3 and RR warrants further investigation as an important player for HER-targeted regimens in combination with capecitabine

Caracterização genotípica de doentes portugueses com patologias associadas ao complexo multienzimático lisossomal: sialidose e galactosialidose

Dissertação de mestrado em Biologi apresentada à Faculdade de Ciências da Universidade do Porto, 2008

Detection of virulence-associated genes in pathogenic and commensal avian Escherichia coli isolates

Poultry colibacillosis due to Avian Pathogenic Escherichia coli (APEC) is responsible for several extra-intestinal pathological conditions, leading to serious economic damage in poultry production. The most commonly associated pathologies are airsacculitis, colisepticemia, and cellulitis in broiler chickens, and salpingitis and peritonitis in broiler breeders. In this work a total of 66 strains isolated from dead broiler breeders affected with colibacillosis and 61 strains from healthy broilers were studied. Strains from broiler breeders were typified with serogroups O2, O18, and O78, which are mainly associated with disease. The serogroup O78 was the most prevalent (58%). All the strains were checked for the presence of 11 virulence genes: 1) arginine succinyltransferase A (astA); ii) E. coli hemeutilization protein A (chuA); iii) colicin V A/B (cvaA/B); iv) fimbriae mannose-binding type 1 (fimC); v) ferric yersiniabactin uptake A (fyuA); vi) iron-repressible high-molecular-weight proteins 2 (irp2); vii) increased serum survival (iss); viii) iron-uptake systems of E. coli D (iucD); ix) pielonefritis associated to pili C (papC); x) temperature sensitive haemaglutinin (tsh), and xi) vacuolating autotransporter toxin (vat), by Multiplex-PCR. The results showed that all genes are present in both commensal and pathogenic E. coli strains. The iron uptake-related genes and the serum survival gene were more prevalent among APEC. The adhesin genes, except tsh, and the toxin genes, except astA, were also more prevalent among APEC isolates. Except for astA and tsh, APEC strains harbored the majority of the virulence-associated genes studied and fimC was the most prevalent gene, detected in 96.97 and 88.52% of APEC and AFEC strains, respectively. Possession of more than one iron transport system seems to play an important role on APEC survival.

Inibição de linhagens do complexo Fusarium Graminearum por compostos naturais e sintéticos

Quando produtos alimentícios e especiarias são contaminados por micotoxinas é quase impossível detoxificar utilizando processos usuais da indústria de alimentos ou durante o preparo doméstico. Por isso, controlar o crescimento do fungo e a produção de toxinas é uma demanda para garantir a segurança alimentar. Os agrotóxicos são rotineiramente utilizados como estratégia para proteger as plantas de doenças provocadas pela contaminação fúngica. No entanto, eles estão associados a efeitos adversos ao sistema nervoso central e periférico, têm ação imunodepressora e são cancerígenos. Em virtude disso, o objetivo deste trabalho foi estudar a inibição do desenvolvimento, do potencial toxigênico e da expressão gênica de linhagens do Complexo Fusarium graminearum por compostos naturais comparativamente aos fungicidas azoxistrobina e trifloxistrobina. Do farelo de arroz, foram extraídos o γ-orizanol e os ácidos fenólicos (EFF). Das sementes de nim foram extraídos os ácidos fenólicos (EFN), totalizando três extratos naturais. A capacidade antioxidante dos extratos foi verificada pelo consumo do radical livre DPPH• , capacidade de captura do radical ABTS●+, redução do ferro e inibição da oxidação enzimática. Os mecanismos de inibição de três linhagens de F. graminearum foram avaliados através da determinação de compostos estruturais (glicosamina e ergosterol) e da atividade de enzimas do metabolismo primário (α- amilase e proteases). Foram determinadas as micotoxinas de Fusarium: deoxinivalenol (DON), 15 acetildeoxinivalenol (15AcDON), 3 acetildeoxinivalenol (3AcDON), nivalenol (NIV) e zearalenona (ZEA). A expressão dos genes Tri1 e Tri5 foi determinada a fim de verificar se ocorria modificação da expressão gênica nas linhagens do Complexo F. graminearum ocasionada pela presença dos antifúngicos. O EFF apresenta atividade antioxidante destacada em relação aos demais extratos naturais para inibir a iniciação do processo, a propagação do radical livre e a catálise enzimática. A presença dos compostos naturais mostrou efeito promissor como antifúngico para as linhagens, sendo que a concentração necessária para inibir 50% do crescimento radial das colônias (MIC50) foi 0,9 g/kg para γ-orizanol; 0,032 g/kg para EFF e 0,037 g/kg para EFN, portanto, os extratos fenólicos são mais eficazes para inibição de F. graminearum do que o γ-orizanol. Os extratos naturais afetaram as atividades das enzimas α-amilase e proteases. Também ocorreu a redução da formação de componentes estruturais (glicosamina e ergosterol). Os extratos naturais se destacaram pela capacidade de inibição de micotoxinas produzidas pela biomassa fúngica, com destaque para o EFN sobre a produção de DON, 15AcDON, 3AcDON e ZEA. Sendo assim, é possível dizer que há uma relação direta entre a atividade antioxidante na inibição do fungo e na manifestação do seu potencial toxigênico. Além disso, esse estudo contribuiu com a elucidação do mecanismo de ação dos antifúngicos naturais estudados. Ocorre modificação na expressão gênica quando a linhagem é submetida ao tratamento com antifúngico, havendo uma relação direta entre a expressão do gene Tri5 e a produção de DON.