Characterisation of immunodominant protein encoded by the F1L gene of orf virus strains isolated in Italy.

We analysed the molecular properties of the immunodominant protein of different orf virus strains isolated in Italy. The F1L encoding genes and the deduced amino acid sequences of all strains were determined and compared, and they showed several mutations. Structural analysis was carried out in order to assess the influence of amino acid variations on protein structure demonstrating a conservation of the secondary structure. Western blot analysis and immunogold electron microscopy showed that all orf virus strains were antigenically identical. The results of our study confirmed the immunogenicity of the F1L protein; furthermore, our data suggest a possible involvement of the protein in the virus cycle.

Protein structure prediction and biomolecular recognition: from protein sequence to peptidomimetic design with the human beta3 integrin.

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable alpha-helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.

Prediction of the transmembrane regions of beta-barrel membrane proteins with a neural network-based predictor.

A method based on neural networks is trained and tested on a nonredundant set of beta-barrel membrane proteins known at atomic resolution with a jackknife procedure. The method predicts the topography of transmembrane beta strands with residue accuracy as high as 78% when evolutionary information is used as input to the network. Of the transmembrane beta-strands included in the training set, 93% are correctly assigned. The predictor includes an algorithm of model optimization, based on dynamic programming, that correctly models eight out of the 11 proteins present in the training/testing set. In addition, protein topology is assigned on the basis of the location of the longest loops in the models. We propose this as a general method to fill the gap of the prediction of beta-barrel membrane proteins.

Predictions of protein segments with the same aminoacid sequence and different secondary structure: a benchmark for predictive methods.

The most stringent test for predictive methods of protein secondary structure is whether identical short sequences that are known to be present with different conformations in different proteins known at atomic resolution can be correctly discriminated. In this study, we show that the prediction efficiency of this type of segments in unrelated proteins reaches an average accuracy per residue ranging from about 72 to 75% (depending on the alignment method used to generate the input sequence profile) only when methods of the third generation are used. A comparison of different methods based on segment statistics (2nd generation methods) and/or including also evolutionary information (3rd generation methods) indicate that the discrimination of the different conformations of identical segments is dependent on the method used for the prediction. Accuracy is similar when methods similarly performing on the secondary structure prediction are tested. When evolutionary information is taken into account as compared to single sequence input, the number of correctly discriminated pairs is increased twofold. The results also highlight the predictive capability of neural networks for identical segments whose conformation differs in different proteins.

Neural networks predict protein folding and structure: artificial intelligence faces biomolecular complexity.

In the genomic era DNA sequencing is increasing our knowledge of the molecular structure of genetic codes from bacteria to man at a hyperbolic rate. Billions of nucleotides and millions of aminoacids are already filling the electronic files of the data bases presently available, which contain a tremendous amount of information on the most biologically relevant macromolecules, such as DNA, RNA and proteins. The most urgent problem originates from the need to single out the relevant information amidst a wealth of general features. Intelligent tools are therefore needed to optimise the search. Data mining for sequence analysis in biotechnology has been substantially aided by the development of new powerful methods borrowed from the machine learning approach. In this paper we discuss the application of artificial feedforward neural networks to deal with some fundamental problems tied with the folding process and the structure-function relationship in proteins.

Hyaluronic acid by atomic force microscopy.

Hyaluronic acid (HA) of different molecular weights has been examined by atomic force microscopy (AFM) in air. This technique allows 3-D surface images of soft samples without any pretreatment, such as shadowing or staining. In the present study we examined the supermolecular organization of HA chains when deposited on mica and graphite, to better understand the interchain and intrachain interactions of HA molecules in solution. The concentration of the solution deposited varied from 0.001 to 1 mg/ml. On both substrates, and independent of the concentration, high-molecular-mass HA formed networks in which molecules ran parallel for hundreds of nanometers, giving rise to flat sheets and tubular structures that separate and rejoin into similar neighboring aggregates. Accurate measurements of the thickness of the thinnest sheets were consistent with a monolayer of HA molecules, 0.3 nm thick, strongly indicating lateral aggregation forces between chains as well as rather strong hydrophilic interactions between mica and HA. The results agree with an existing model of HA tertiary structure in solution in which the network is stabilized by both hydrophilic and hydrophobic interactions. Our images support this model and indicate that hydrophobic interactions between chains may exert a pivotal role in aqueous solution.