Besnoitia besnoiti protein disulfide isomerase (BbPDI): molecular characterization, expression and in silico modelling

Besnoitia besnoiti is an apicomplexan parasite responsible for bovine besnoitiosis, a disease with a high prevalence in tropical and subtropical regions and re-emerging in Europe. Despite the great economical losses associated with besnoitiosis, this disease has been underestimated and poorly studied, and neither an effective therapy nor an efficacious vaccine is available. Protein disulfide isomerase (PDI) is an essential enzyme for the acquisition of the correct three-dimensional structure of proteins. Current evidence suggests that in Neosporacaninum and Toxoplasmagondii, which are closely related to B. besnoiti, PDI play an important role in host cell invasion, is a relevant target for the host immune response, and represents a promising drug target and/or vaccine candidate. In this work, we present the nucleotide sequence of the B. besnoiti PDI gene. BbPDI belongs to the thioredoxin-like superfamily (cluster 00388) and is included in the PDI_a family (cluster defined cd02961) and the PDI_a_PDI_a'_c subfamily (cd02995). A 3D theoretical model was built by comparative homology using Swiss-Model server, using as a template the crystallographic deduced model of Tapasin-ERp57 (PDB code 3F8U chain C). Analysis of the phylogenetic tree for PDI within the phylum apicomplexa reinforces the close relationship among B. besnoiti, N. caninum and T. gondii. When subjected to a PDI-assay based on the polymerisation of reduced insulin, recombinant BbPDI expressed in E. coli exhibited enzymatic activity, which was inhibited by bacitracin. Antiserum directed against recombinant BbPDI reacted with PDI in Western blots and by immunofluorescence with B. besnoiti tachyzoites and bradyzoites.

Engineering Tocopherol Selectivity in alpha-TTP: A Combined In Vitro/In Silico Study

"We present a combined in vitro/in silico study to determine the molecular origin of the selectivity of a-tocopherol transfer" "protein (a-TTP) towards a-tocopherol. Molecular dynamics simulations combined to free energy perturbation calculations predict a binding free energy for a-tocopherol to a-TTP 8.26+2.13 kcal mol{1 lower than that of c-tocopherol. Our calculations show that c-tocopherol binds to a-TTP in a significantly distorted geometry as compared to that of the natural ligand. Variations in the hydration of the binding pocket and in the protein structure are found as well. We propose a mutation, A156L, which significantly modifies the selectivity properties of a-TTP towards the two tocopherols. In particular, our simulations predict that A156L binds preferentially to c-tocopherol, with striking structural similarities to the wild-type- a-tocopherol complex. The affinity properties are confirmed by differential scanning fluorimetry as well as in vitro competitive binding assays. Our data indicate that residue A156 is at a critical position for determination of the selectivity of a-TTP. The engineering of TTP mutants with modulating binding properties can have potential impact at industrial level for easier purification of single tocopherols from vitamin E mixtures coming from natural oils or synthetic processes. Moreover," "the identification of a c-tocopherol selective TTP offers the possibility to challenge the hypotheses for the evolutionary development of a mechanism for a-tocopherol selection in omnivorous animals."

Identification and in silico analyses of novel TGFBR1 and TGFBR2 mutations in Marfan syndrome-related disorders

Very recently, heterozygous mutations in the genes encoding transforming growth factor beta receptors I (TGFBR1) and II (TGFBR2) have been reported in Loeys-Dietz aortic aneurysm syndrome (LDS). In addition, dominant TGFBR2 mutations have been identified in Marfan syndrome type 2 (MFS2) and familial thoracic aortic aneurysms and dissections (TAAD). In the past, mutations of these genes were associated with atherosclerosis and several human cancers. Here, we report a total of nine novel and one known heterozygous sequence variants in the TGFBR1 and TGFBR2 genes in nine of 70 unrelated individuals with MFS-like phenotypes who previously tested negative for mutations in the gene encoding the extracellular matrix protein fibrillin-1 (FBN1). To assess the pathogenic impact of these sequence variants, in silico analyses were performed by the PolyPhen, SIFT, and Fold-X algorithms and by means of a 3D homology model of the TGFBR2 kinase domain. Our results showed that in all but one of the patients the pathogenic effect of at least one sequence variant is highly probable (c.722C > T, c.799A > C, and c.1460G > A in TGFBR1 and c.773T > G, c.1106G > T, c.1159G > A, c.1181G > A, and c.1561T > C in TGFBR2). These deleterious alleles occurred de novo or segregated with the disease in the families, indicating a causative association between the sequence variants and clinical phenotypes. Since TGFBR2 mutations found in patients with MFS-related disorders cannot be distinguished from heterozygous TGFBR2 mutations reported in tumor samples, we emphasize the importance of segregation analysis in affected families. In order to be able to find the mutation that is indeed responsible for a MFS-related phenotype, we also propose that genetic testing for sequence alterations in TGFBR1 and TGFBR2 should be complemented by mutation screening of the FBN1 gene.

Pseudodominant Inheritance of Goitrous Congenital Hypothyroidism Caused by TPO Mutations: Molecular and in Silico Studies

Context and Objective: Most cases of goitrous congenital hypothyroidism (CH) from thyroid dyshormonogenesis 1) follow a recessive mode of inheritance and 2) are due to mutations in the thyroid peroxidase gene (TPO). We report the genetic mechanism underlying the apparently dominant inheritance of goitrous CH in a nonconsanguineous family of French Canadian origin. Design, Setting, and Participants: Two brothers identified by newborn TSH screening had severe hypothyroidism and a goiter with increased (99m)Tc uptake. The mother was euthyroid, but the father and two paternal uncles had also been diagnosed with goitrous CH. After having excluded PAX8 gene mutations, we hypothesized that the underlying defect could be TPO mutations. Results: Both compound heterozygous siblings had inherited a mutant TPO allele carried by their mother (c.1496delC; p.Pro499Argfs2X), and from their father, one brother had inherited a missense mutation (c.1978C-->G; p.Gln660Glu) and the other an insertion (c.1955insT; p.Phe653Valfs15X). The thyroid gland of one uncle who is a compound heterozygote for TPO mutations (p.Phe653Valfs15X/p.Gln660Glu) was removed because of concurrent multiple endocrine neoplasia type 2A. Immunohistochemistry revealed normal TPO staining, implying that Gln660Glu TPO is expressed properly. Modeling of this mutant in silico suggests that its three-dimensional structure is conserved, whereas the electrostatic binding energy between the Gln660Glu TPO and its heme group becomes repulsive. Conclusion: We report a pedigree presenting with pseudodominant goitrous CH due to segregation of three different TPO mutations. Although goitrous CH generally follows a recessive mode of inheritance, the high frequency of TPO mutations carriers may lead to pseudodominant inheritance.

Proteins in silico-modeling and sampling

Proteins are linear chain molecules made out of amino acids. Only when they fold to their native states, they become functional. This dissertation aims to model the solvent (environment) effect and to develop & implement enhanced sampling methods that enable a reliable study of the protein folding problem in silico. We have developed an enhanced solvation model based on the solution to the Poisson-Boltzmann equation in order to describe the solvent effect. Following the quantum mechanical Polarizable Continuum Model (PCM), we decomposed net solvation free energy into three physical terms– Polarization, Dispersion and Cavitation. All the terms were implemented, analyzed and parametrized individually to obtain a high level of accuracy. In order to describe the thermodynamics of proteins, their conformational space needs to be sampled thoroughly. Simulations of proteins are hampered by slow relaxation due to their rugged free-energy landscape, with the barriers between minima being higher than the thermal energy at physiological temperatures. In order to overcome this problem a number of approaches have been proposed of which replica exchange method (REM) is the most popular. In this dissertation we describe a new variant of canonical replica exchange method in the context of molecular dynamic simulation. The advantage of this new method is the easily tunable high acceptance rate for the replica exchange. We call our method Microcanonical Replica Exchange Molecular Dynamic (MREMD). We have described the theoretical frame work, comment on its actual implementation, and its application to Trp-cage mini-protein in implicit solvent. We have been able to correctly predict the folding thermodynamics of this protein using our approach.

In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens

The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant RUTA GRAVEOLENS L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the IN SILICO prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB (2)). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory IN VITRO activity on AChE (IC (50) 34.7 muM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC (50) values of 11.98 muM and 3.19 muM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB (2) ligand was obtained, i. e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB (2) receptor ( Ki of 7.4 muM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. GRAVEOLENS on three different proteins has shown promise as an IN SILICO tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.

Modeling of human P450 oxidoreductase structure by in silico mutagenesis and MD simulation

P450 oxidoreductase (POR) is the obligate electron donor for microsomal cytochrome P450s and mutations in POR cause several metabolic disorders. We have modeled the structure of human P450 oxidoreductase by in silico amino acid replacements in the rat POR crystal structure. The rat POR has 94% homology with human POR and 38 amino acids were replaced to make its sequence identical to human POR. Several rounds of molecular dynamic simulations refined the model and removed structural clashes from side chain alterations of replaced amino acids. This approach has the advantage of keeping the cofactor contacts and structural features of the core enzyme intact which could not be achieved by homology based approaches. The final model from our approach was of high quality and compared well with experimentally determined structures of other PORs. This model will be used for analyzing the structural implications of mutations and polymorphisms in human POR.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Properties of plant food allergens: In silico studies

International Comparative Medicine Symposium. Allergology. Revisión de métodos computacionales para determinar propiedades de alergenos alimentarios de plantas

In silico identification and characterisation of 17 polymorphic anonymous non-coding sequence markers (ANMs) for red grouse (Lagopus lagopus scotica)

Peer reviewed

In silico detection of control signals: mRNA 3′-end-processing sequences in diverse species

We have investigated mRNA 3′-end-processing signals in each of six eukaryotic species (yeast, rice, arabidopsis, fruitfly, mouse, and human) through the analysis of more than 20,000 3′-expressed sequence tags. The use and conservation of the canonical AAUAAA element vary widely among the six species and are especially weak in plants and yeast. Even in the animal species, the AAUAAA signal does not appear to be as universal as indicated by previous studies. The abundance of single-base variants of AAUAAA correlates with their measured processing efficiencies. As found previously, the plant polyadenylation signals are more similar to those of yeast than to those of animals, with both common content and arrangement of the signal elements. In all species examined, the complete polyadenylation signal appears to consist of an aggregate of multiple elements. In light of these and previous results, we present a broadened concept of 3′-end-processing signals in which no single exact sequence element is universally required for processing. Rather, the total efficiency is a function of all elements and, importantly, an inefficient word in one element can be compensated for by strong words in other elements. These complex patterns indicate that effective tools to identify 3′-end-processing signals will require more than consensus sequence identification.

The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.

Determination of absorption curves, dissolution profiles and establishment of in vitro-in vivo correlation by in silico methods using GastroPlusTM and DDDPlusTM

The use of computer programs to predict drug absorption in humans and to simulate dissolution profiles has become a valuable tool in the pharmaceutical area. The objective of this study was to use in silico methods through software GastroPlusTM and DDDPlusTM to simulate drug absorption curves and dissolution profiles, and to establish in vitro-in vivo correlations (IVIVCs). The work presented herein is divided into five chapters and includes the drugs ketoprofen, pyrimethamine, metronidazole, fluconazole, carvedilol and doxazosin. In Chapter 1, simulated plasma curves for ketoprofen matrix tablets are presented and IVIVC was established. The use of simulated intrinsic dissolution tests for pyrimethamine and metronidazole as a tool for biopharmaceutics classification is detailed in Chapter 2. In Chapter 3, simulation of plasma curves for fluconazole capsules with different dissolution profiles is demonstrated as a tool for biowaiver. IVIVC studies were also conducted for carvedilol immediate-release tablets from dissolution profiles in Chapter 4. Chapter 5 covers the application of simulated dissolution tests for development of doxazosin extended-release formulations. Simulation of plasma curves and IVIVC using the software GastroPlusTM as well as intrinsic dissolution tests and dissolution profiles using the software DDDPlusTM proved to be a tool of wide application in predicting biopharmaceutical characteristics of drugs and formulations, allowing the reduction of time and costs of experimental laboratory work.