Conformational stability of recombinant manganese superoxide dismutase from the filamentous fungus Trichoderma reesei

Superoxide dismutases (SODS; EC 1.15.1.1) are part of the antioxidant system of aerobic organisms and are used as a defense against oxidative injury caused by reactive oxygen species (ROS). The cloning and sequencing of the 788-bp genomic DNA from Trichoderma reesei strain QM9414 (anamorph of Hypocrea jecorina) revealed an open reading frame encoding a protein of 212 amino acid residues, with 65-90% similarity to manganese superoxide dismutase from other filamentous fungi. The TrMnSOD was purified and shown to be stable from 20 to 90 degrees C for 1 h at pH from 8 to 11.5, while maintaining its biological activity. (C) 2011 Elsevier B.V. All rights reserved.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

A statistical mechanical model for β-hairpin kinetics

Understanding the mechanism of protein secondary structure formation is an essential part of the protein-folding puzzle. Here, we describe a simple statistical mechanical model for the formation of a β-hairpin, the minimal structural element of the antiparallel β-pleated sheet. The model accurately describes the thermodynamic and kinetic behavior of a 16-residue, β-hairpin-forming peptide, successfully explaining its two-state behavior and apparent negative activation energy for folding. The model classifies structures according to their backbone conformation, defined by 15 pairs of dihedral angles, and is further simplified by considering only the 120 structures with contiguous stretches of native pairs of backbone dihedral angles. This single sequence approximation is tested by comparison with a more complete model that includes the 215 possible conformations and 15 × 215 possible kinetic transitions. Finally, we use the model to predict the equilibrium unfolding curves and kinetics for several variants of the β-hairpin peptide.

Metal Clips Induce Folding of a Short Unstructured Peptide into an a-Helix via Turn Conformations in Water. Kinetic versus Thermodynamic Products

Short peptides corresponding to two to four a-helical turns of proteins are not thermodynamically stable helices in water. Unstructured octapeptide Ac-His1*-Ala2-Ala3-His4*-His5*-Glu6-Leu7-His8*-NH2 (1) reacts with two [Pd ((NH2)-N-15(CH2)(2) (NH2)-N-15)(NO3)(2)] in water to form a kinetically stable intermediate, [{Pden}(2)-{(1,4)(5,8)-peptide}](2), in which two 19-membered metallocyclic rings stabilize two peptide turns. Slow subsequent folding to a thermodynamically more stable two-turn a-helix drives the equilibrium to [{Pden}(2)-{(1,5)(4,8)-peptide}] (3), featuring two 22-membered rings. This transformation from unstructured peptide via turns to an a-helix suggests that metal clips might be useful probes for investigating peptide folding.

Single turn peptide alpha helices with exceptional stability in water

Cyclic pentapepticles are not known to exist in a-helical conformations. CD and NMR spectra show that specific 20-membered cyclic pentapepticles, Ac-(cyclo-1,5) [KxxxD]-NH2 and Ac-(cyclo-2,6)R[KxxxD]-NH2, are highly a-helical structures in water and independent of concentration, TFE, denaturants, and proteases. These are the smallest a-helical peptides in water.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

NMR of conotoxins: structural features and an analysis of chemical shifts of post-translationally modified amino acids

Conotoxins are small conformationally constrained peptides found in the venom of marine snails of the genus Conus. They are usually cysteine rich and frequently contain a high degree of post-translational modifications such as C-terminal amidation, hydroxylation, carboxylation, bromination, epimerisation and glycosylation. Here we review the role of NMR in determining the three-dimensional structures of conotoxins and also provide a compilation and analysis of H-1 and C-13 chemical shifts of post-translationally modified amino acids and compare them with data from common amino acids. This analysis provides a reference source for chemical shifts of post-translationally modified amino acids. Copyright (C) 2006 John Wiley & Sons, Ltd.

Molecular dynamics simulations of the hydrophobin SC3 at a hydrophobic/hydrophilic interface

Hydrophobins are small (similar to 100 aa) proteins that have an important role in the growth and development of mycelial fungi. They are surface active and, after secretion by the fungi, self-assemble into amphipathic membranes at hydrophobic/hydrophilic interfaces, reversing the hydrophobicity of the surface. In this study, molecular dynamics simulation techniques have been used to model the process by which a specific class I hydrophobin, SC3, binds to a range of hydrophobic/ hydrophilic interfaces. The structure of SC3 used in this investigation was modeled based on the crystal structure of the class II hydrophobin HFBII using the assumption that the disulfide pairings of the eight conserved cysteine residues are maintained. The proposed model for SC3 in aqueous solution is compact and globular containing primarily P-strand and coil structures. The behavior of this model of SC3 was investigated at an air/water, an oil/water, and a hydrophobic solid/water interface. It was found that SC3 preferentially binds to the interfaces via the loop region between the third and fourth cysteine residues and that binding is associated with an increase in a-helix formation in qualitative agreement with experiment. Based on a combination of the available experiment data and the current simulation studies, we propose a possible model for SC3 self-assembly on a hydrophobic solid/water interface.

Avaliação da interação entre aflatoxina M1 e B1 com a fração proteica do leite

Dissertação composta por 02 artigos.

Synthesis and self-assembly behaviour of poly(Nα-Boc-l-tryptophan)-block-poly(ethylene glycol)-block-poly(Nα-Boc-l-tryptophan)

We report for the first time the use of Nα-Boc-l-tryptophan for the synthesis of amphiphilic BAB triblock copolymers for potential drug delivery applications. A library of poly(Nα-Boc-l-tryptophan)-block-poly(ethylene glycol)-block-poly(Nα-Boc-l-tryptophan) (PBoclTrp-b-PEG-b-PBoclTrp) amphiphilic copolymers were synthesized through the ring opening polymerization of Nα-Boc-l-tryptophan Nα-carboxy anhydride as initiated by diamino-terminated PEG of fixed molecular weight (Mn 3350). The influence of the hydrophobic block length over self-assembly was investigated for 4 of the BAB copolymers of molecular weights varying between Mn 5000 and Mn 17000. It was found that an increase in hydrophobic block length led to an increase in hydrodynamic size of aggregates in solution, as well as a decrease in critical micelle concentration. TEM analysis showed the formation of spherical micelles with the largest of the copolymers forming interconnected networks of spherical micelles. The influence of hydrophobic block length over the formation of secondary structure was analyzed using circular dichroism and infrared spectroscopy. Collectively we found that the presence of t-Boc protected l-tryptophan leads to the preferential formation of α-helix secondary structure through hydrogen bonding, which, in a drug delivery vehicle context, could help in controlling drug release. Also, it is believed that the use of novel Nα-Boc-l-tryptophan could improve drug stabilization in the hydrophobic core via π-π interactions between indole rings.

The Structure and Functions of Hantavirus Nucleocapsid Protein

Hantaviruses, members of the genus Hantavirus in the Bunyaviridae family, are enveloped single-stranded RNA viruses with tri-segmented genome of negative polarity. In humans, hantaviruses cause two diseases, hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS), which vary in severity depending on the causative agent. Each hantavirus is carried by a specific rodent host and is transmitted to humans through excreta of infected rodents. The genome of hantaviruses encodes four structural proteins: the nucleocapsid protein (N), the glycoproteins (Gn and Gc), and the polymerase (L) and also the nonstructural protein (NSs). This thesis deals with the functional characterization of hantavirus N protein with regard to its structure. Structural studies of the N protein have progressed slowly and the crystal structure of the whole protein is still not available, therefore biochemical assays coupled with bioinformatical modeling proved essential for studying N protein structure and functions. Presumably, during RNA encapsidation, the N protein first forms intermediate trimers and then oligomers. First, we investigated the role of N-terminal domain in the N protein oligomerization. The results suggested that the N-terminal region of the N protein forms a coiled-coil, in which two antiparallel alpha helices interact via their hydrophobic seams. Hydrophobic residues L4, I11, L18, L25 and V32 in the first helix and L44, V51, L58 and L65 in the second helix were crucial for stabilizing the structure. The results were consistent with the head-to-head, tail-to-tail model for hantavirus N protein trimerization. We demonstrated that an intact coiled-coil structure of the N terminus is crucial for the oligomerization capacity of the N protein. We also added new details to the head-to-head, tail-to-tail model of trimerization by suggesting that the initial step is based on interaction(s) between intact intra-molecular coiled-coils of the monomers. We further analyzed the importance of charged aa residues located within the coiled-coil for the N protein oligomerization. To predict the interacting surfaces of the monomers we used an upgraded in silico model of the coiled-coil domain that was docked into a trimer. Next the predicted target residues were mutated. The results obtained using the mammalian two-hybrid assay suggested that conserved charged aa residues within the coiled-coil make a substantial contribution to the N protein oligomerization. This contribution probably involves the formation of interacting surfaces of the N monomers and also stabilization of the coiled-coil via intramolecular ionic bridging. We proposed that the tips of the coiled-coils are the first to come into direct contact and thus initiate tight packing of the three monomers into a compact structure. This was in agreement with the previous results showing that an increase in ionic strength abolished the interaction between N protein molecules. We also showed that residues having the strongest effect on the N protein oligomerization are not scattered randomly throughout the coiled-coil 3D model structure, but form clusters. Next we found evidence for the hantaviral N protein interaction with the cytoplasmic tail of the glycoprotein Gn. In order to study this interaction we used the GST pull-down assay in combination with mutagenesis technique. The results demonstrated that intact, properly folded zinc fingers of the Gn protein cytoplasmic tail as well as the middle domain of the N protein (that includes aa residues 80 248 and supposedly carries the RNA-binding domain) are essential for the interaction. Since hantaviruses do not have a matrix protein that mediates the packaging of the viral RNA in other negatve stranded viruses (NSRV), hantaviral RNPs should be involved in a direct interaction with the intraviral domains of the envelope-embedded glycoproteins. By showing the N-Gn interaction we provided the evidence for one of the crucial steps in the virus replication at which RNPs are directed to the site of the virus assembly. Finally we started analysis of the N protein RNA-binding region, which is supposedly located in the middle domain of the N protein molecule. We developed a model for the initial step of RNA-binding by the hantaviral N protein. We hypothesized that the hantaviral N protein possesses two secondary structure elements that initiate the RNA encapsidation. The results suggest that amino acid residues (172-176) presumably act as a hook to catch vRNA and that the positively charged interaction surface (aa residues 144-160) enhances the initial N-RNA interacation. In conclusion, we elucidated new functions of hantavirus N protein. Using in silico modeling we predicted the domain structure of the protein and using experimental techniques showed that each domain is responsible for executing certain function(s). We showed that intact N terminal coiled-coil domain is crucial for oligomerization and charged residues located on its surface form a interaction surface for the N monomers. The middle domain is essential for interaction with the cytoplasmic tail of the Gn protein and RNA binding.

Protein structure prediction servers at University College London

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction.

Assembling novel protein folds from super-secondary structural fragments

The results of applying a fragment-based protein tertiary structure prediction method to the prediction of 14 CASP5 target domains are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. A number of good predictions for proteins with novel folds were produced, although not always as the first model. For two fold recognition targets, FRAGFOLD produced the most accurate model in both cases, despite the fact that the predictions were not based on a template structure. Although clear progress has been made in improving FRAGFOLD since CASP4, the ranking of final models still seems to be the main problem that needs to be addressed before the next CASP experiment