Structural alphabets derived from attractors in conformational space

Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.Results: A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.Conclusions: The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics. © 2010 Pandini et al; licensee BioMed Central Ltd

Peptide classification using optimal and information theoretic syntactic modeling

We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using distance-based metrics that involve the edit operations required in the peptide comparisons. In this paper, we shall demonstrate that the Optimal and Information Theoretic (OIT) model of Oommen and Kashyap [22] applicable for syntactic pattern recognition can be used to tackle peptide classification problem. We advocate that one can model the differences between compared strings as a mutation model consisting of random substitutions, insertions and deletions obeying the OIT model. Thus, in this paper, we show that the probability measure obtained from the OIT model can be perceived as a sequence similarity metric, using which a support vector machine (SVM)-based peptide classifier can be devised. The classifier, which we have built has been tested for eight different substitution matrices and for two different data sets, namely, the HIV-1 Protease cleavage sites and the T-cell epitopes. The results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, it is less sensitive to the substitution matrix than the other methods compared, and that when combined with a SVM, is among the best peptide classification methods availabl

Protein Fold Recognition Using Neural Networks

To predict accurately the three-dimensional (3D) structures of proteins from their amino acid sequences alone remains a challenging problem. However, using protein fold recognition tools, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis describes development of TUNE (Threading Using Neural Networks), a fold recognition program using artificial neural network (ANN) models. A new method to generate amino acid substitution matrices is described in chapter two. It uses an ANN to generalise amino acid substitutions observed in protein structure alignments. Matrices for alignment scoring from this approach were compared with classic alignment scoring schemes. From these neural network models, a series of encoding schemes were constructed. These schemes describe the amino acid types with a few numbers. They were generated to replace the orthogonal encoding scheme, so that smaller, faster and more accurate neural network models can be applied on bioinformatic problems. The TUNE model was introduced in chapter four to measure protein sequence-structure compatibility. Given the integrated residue structural environment descriptions, the model predicts probabilities of observing amino acid types in such environments. Using this model, a scoring function to measure the fitness of a residue in a protein structure model can be made for protein threading programs. The model in chapter two was extended by including the residue structural environment descriptions for predictions. A simple protein fold recognition program with a dynamic programming algorithm was developed using this model. The program was then tested in the fourth round of the Critical Assessment of protein Structure Prediction methods (CASP4) and produced reasonably good results

A mixture of physicochemical and evolutionary–based feature extraction approaches for protein fold recognition

Griffith Sciences, Griffith School of EngineeringFull Tex

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

BACKGROUND: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. RESULTS: The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 A(ROC )for the MHCBench data sets (up from 0.756), and an average of 0.96 A(ROC )for multiple alleles of the MHCPEP database. CONCLUSION: The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems

Prediction of Factors Determining Changes in Stability in Protein Mutants

Analysing the factors behind protein stability is a key research topic in molecular biology and has direct implications on protein structure prediction and protein-protein docking solutions. Protein stability upon point mutations were analysed using a distance dependant pair potential representing mainly through-space interactions and torsion angle potential representing neighbouring effects as a basic statistical mechanical setup for the analysis. The synergetic effect of accessible surface area and secondary structure preferences was used as a classifier for the potentials. Various principles underlying the protein structure description must also be studied carefully to efficiently understand the relationships between sequence, structure and function. Mean force potentials from atom interactions and main torsion angles were used by different investigators to evaluate the protein structure, stability and protein-protein interactions. In recent experiments, these were also used in the prediction of protein function and enzyme catalysis. Five different atom classification models with interactions in different distance ranges were selected to check their ability to effectively describe the protein environment. Furthermore, torsion angle potentials were also derived in addition to atom potentials so that orientational information of amino acids can be included to the model. The five atom classification models that are used for atom potentials include the following: a basic five (basic5) atom model (C aliphatic, C aromatic, H, O, N), amino acid C[alpha]atoms (C[alpha]20), Li-Nussinov atom model (LN24), SATIS model (SA28) and Melo and Feytmans atom model (MF40). Carbon atoms with aromatic and aliphatic nature exhibit significantly different chemical and functional behaviour and they were considered separately in the basic5 atom model with N, O and S. Li and Nussinov defined 24 different amino acid atom types using the polarity and hydrophobicity of atoms. Similarly, SATIS is also a protocol for the definition and automatic assignment of atom types and the classification of atoms. The free energy values (ddG and ddGH2O values from thermal and chemical denaturation) of unfolding from point mutation experiments were used as an experimental measure of protein stability. In future, this method can also be extended to evaluate other structure descriptors. It has already been reported that the measured free energy changes between wild type and mutant proteins can be predicted using statistical potentials. But, these models lack good prediction efficiency and reliability to predict protein mutant stability in future. A dataset of 4024 non-redundant structures was used to derive the atom interactions and torsion angles phi and psi, after running DSSP for the whole dataset. For torsion potentials, the bins were normalised with a standard procedure using the circular Gaussian function for phi and psi having the bivariate normal distribution. Results were validated based on the correlation observed between the experimental ddG and predicted ddG values. Prediction accuracy of being correctly predicted as stabilising or destabilising was also observed. Results show that the Melo and Feytmens atom model predicts the protein stability to a maximum extent, since it showed a correlation coefficient of 0.85 with 85.31% of 1536 mutations correctly predicted to be either stabilising or destabilising. SA28, LN24, C[alpha]20 and basic5 atom models showed a correlation coefficient of 0.82, 0.78, 0.76 and 0.55 respectively. Later, statistical stepwise regression methods were used to optimise the number of atoms used for the model. Effect of torsion angle potentials with and without the Gaussian apodisation was compared. This shows that the amino acids adapt perturbed torsion angle conformations in partially buried beta sheets than the other structural elements. For the final prediction model, two datasets of point mutations were taken for the comparison of theoretically predicted stabilising energy values with experimental ddG and ddGH2O from thermal and chemical denaturation experiments respectively. These include 1538 and 1581 mutations respectively and contain 101 proteins that share wide range of sequence identity. Results were carefully evaluated with a wide range of statistical tests. Results show a maximum correlation of 0.87 between predicted and experimental ddG values and a prediction accuracy of 85.3% (stabilising or destabilising) for all mutations together. A correlation of 0.77 each for the test dataset of split-sample validation and k-fold cross validation tests was obtained and a correlation of 0.70 was shown by the jack-knife test. A similar model was implemented and the results were analysed for mutations with ddGH2O. A correlation of 0.79 was observed with a prediction efficiency of 85.03%. A web tool has been developed to use this prediction model (www.biotool.uni-koeln.de)

A short survey on protein blocks.

International audienceProtein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications

The rational development of molecularly imprinted polymer-based sensors for protein detection.

The detection of specific proteins as biomarkers of disease, health status, environmental monitoring, food quality, control of fermenters and civil defence purposes means that biosensors for these targets will become increasingly more important. Among the technologies used for building specific recognition properties, molecularly imprinted polymers (MIPs) are attracting much attention. In this critical review we describe many methods used for imprinting recognition for protein targets in polymers and their incorporation with a number of transducer platforms with the aim of identifying the most promising approaches for the preparation of MIP-based protein sensors (277 references)

Maximising the nutritional and sensory quality of Lupin bread made using Western Australian bakers flour

Australian sweet lupin (ASL) addition to bread has potential to increase its nutritional quality. ASL variety significantly affected ASL-wheat bread quality, related to varietal differences in composition and flour particle size. Factorial analysis identified the most significant process parameters affecting ASL-wheat bread quality. Response surface methodology identified optimal levels of process and formulation parameters to deliver ASL-lupin bread with maximum ASL incorporation combined with acceptable physical quality and consumer acceptability

Isolation and characterisation of a channel inhibitor from Bunodosoma capense

Voltage gated ion channels have recently become a subject of investigation as possible pharmaceutical targets. Research has linked the activity of ion channels directly to antiinflammatory pathways, energy homeostasis, cancer proliferation and painful diabetic neuropathy. Sea anemones secrete a diverse array of bioactive compounds including potassium and sodium channel inhibitors. A novel sodium channel inhibitor (molecular mass of 4619.7 ± 0.6 Da) with a predicted sequence: CLCNSDGPSV RGNTLSGILW LAGCPSGWHN CKKHKPTIGW CCK was isolated from Bunodosoma capense using a modified stimulation technique to induce the secretion of the neurotoxin rich mucus confirmed by an Artemia nauplii swimming assay. The peptide purification combined size-exclusion and reverse-phase high performance liquid chromatography. A thallium-based ion flux assay confirmed the presence of a sodium channel inhibitor and purity was determined using a modified tricine SDS-PAGE system. The peptide isolated indicated a tight conformation with the presence of multiple disulfide bonds in a cystine knot conformation. An IC50 value of 26 nM was determined for sodium channel inhibition on MCF-7 cells, indicating increased toxicity in comparison to sodium channel inhibitors previously isolated from Bunodosoma species