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Although protein folding is often described by motion on a funnel-shaped overall topology of the energy landscape, the many local interactions that can occur result in considerable landscape roughness which slows folding by increasing internal friction. Recent experimental results have brought to light that this roughness also causes unusual diffusional behaviour of the backbone of an unfolded protein, i.e. the relative motion of protein sections cannot be described by the normal diffusion equation, but shows strongly subdiffusional behaviour with a nonlinear time dependence of the mean square displacement, 〈r(2)(t)〉∝t(α) (α≪ 1). This results in significantly slower configurational equilibration than had been assumed hitherto. Analysis of the results also allows quantification of the energy landscape roughness, i.e. the root-mean-squared depth of local minima, yielding a value of 4-5kBT for a typical small protein.

The dynamics of protein conformational changes, from protein folding to smaller changes, such as those involved in ligand binding, are governed by the properties of the conformational energy landscape. Different techniques have been used to follow the motion of a protein over this landscape and thus quantify its properties. However, these techniques often are limited to short timescales and low-energy conformations. Here, we describe a general approach that overcomes these limitations. Starting from a nonnative conformation held by an aromatic disulfide bond, we use time-resolved spectroscopy to observe nonequilibrium backbone dynamics over nine orders of magnitude in time, from picoseconds to milliseconds, after photolysis of the disulfide bond. We find that the reencounter probability of residues that initially are in close contact decreases with time following an unusual power law that persists over the full time range and is independent of the primary sequence. Model simulations show that this power law arises from subdiffusional motion, indicating a wide distribution of trapping times in local minima of the energy landscape, and enable us to quantify the roughness of the energy landscape (4-5 k(B)T). Surprisingly, even under denaturing conditions, the energy landscape remains highly rugged with deep traps (>20 k(B)T) that result from multiple nonnative interactions and are sufficient for trapping on the millisecond timescale. Finally, we suggest that the subdiffusional motion of the protein backbone found here may promote rapid folding of proteins with low contact order by enhancing contact formation between nearby residues.

Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can be important to infer information about their possible functions and interactions. In this work, we present a surface-matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features. Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of macromolecular functional analysis and to screen possible protein interactions to predict pairing capability.

Background: The design of mutants in protein functional regions, such as the ligand binding sites, is a powerful approach to recognize the determinants of specific protein activities in cellular pathways. For an exhaustive analysis of selected positions of protein structure large scale mutagenesis techniques are often employed, with laborious and time consuming experimental set-up. 'In silico' mutagenesis and screening simulation represents a valid alternative to laboratory methods to drive the 'in vivo' testing toward more focused objectives., Results: We present here a high performance computational procedure for large-scale mutant modelling and subsequent evaluation of the effect on ligand binding affinity. The mutagenesis was performed with a 'saturation' approach, where all 20 natural amino acids were tested in positions involved in ligand binding sites. Each modelled mutant was subjected to molecular docking simulation and stability evaluation. The simulated protein-ligand complexes were screened for their impairment of binding ability based on change of calculated Ki compared to the wild-type.An example of application to the Endothelial Protein C Receptor residues involved in lipid binding is reported., Conclusion: The computational pipeline presented in this work is a useful tool for the design of structurally stable mutants with altered affinity for ligand binding, considerably reducing the number of mutants to be experimentally tested. The saturation mutagenesis procedure does not require previous knowledge of functional role of the residues involved and allows extensive exploration of all possible substitutions and their pairwise combinations. Mutants are screened by docking simulation and stability evaluation followed by a rationally driven selection of those presenting the required characteristics. The method can be employed in molecular recognition studies and as a preliminary approach to select models for experimental testing.

High-dilution equilibrium macrocyclization is developed as a general approach to trapping proteins in a non-native state with a synthetic cross-linking agent. The approach is illustrated using the N-terminal domain of phosphoglycerate kinase and a synthetic reagent containing two maleimide groups, for selective attachment to cysteines introduced onto the protein surface through mutagenesis, and an aromatic disulfide that can be chemically or photochemically cleaved. Following functionalization of the cysteine residues, thiol-disulfide exchange chemistry under strongly unfolding conditions was used to achieve intramolecular cyclization and a high yield of the cross-linked protein. (1)H NMR, CD, and fluorescence spectroscopies indicate that the conformation of the cross-linked protein is non-native. Chemical cleavage of the aromatic disulfide cross-link by a reducing agent results in the acquisition of a nativelike conformation for the reduced protein. Thus, the cross-link acts as a reversible switch of protein folding.

Many tasks in bioinformatics can be faced only using a combination of computational tools. In particular, functional annotation of gene products can be a very expensive task that may require the application of many analysis together with a manual intervention of biologists. In this area, the phylogenomics inference is one of the most accurate analysis methodologies for functional annotation that is not yet widely used due to the computational cost of some steps in its protocol. This paper discusses the implementation and deployment of such analysis protocol in a distributed grid environment using an agent architecture in order to simplify the interaction between users and the grid.

Alzheimer's disease (AD) is considered to be a conformational disease arising from the accumulation of misfolded and unfolded proteins in the endoplasmic reticulum (ER). SEL1L is a component of the ER stress degradation system, which serves to remove unfolded proteins by retrograde degradation using the ubiquitin-proteosome system. In order to identify genetic variations possibly involved in the disease, we analysed the entire SEL1L gene sequence in Italian sporadic AD patients. Here we report on the identification of a new polymorphism within the SEL1L intron 3 (IVS3-88 A>G), which contains potential binding sites for transcription factors involved in ER-induced stress. Our statistical analysis shows a possible role of the novel polymorphism as independent susceptibility factor of Alzheimer's dementia.

Starting from the genomic and proteomic sequence data, a complex computational infrastructure as been established with the objective to develop a GRID based system to to automate the analysis, prediction and annotation processes of genomic DNA. To support of this type of analysis, several algorithms as been used to recognize biological signals involved in the identification of genes and proteins. The system implemented can be use to analyse the content of the large number of genomic sequences. For this reason, the system realized is capable of using a computational architecture specifically designed for intensive computing based on GRID technologies developed throughout the BIOINFOGRID European project. We developed a GRID based workflow to correlate different kind of Bioinformatics data, going from the Genomics Nucleotide to the Protein Sequence. The first step in the workflow consists of submitting a nucleotide sequence that is elaborated by a specific software for gene prediction. In particular this tool performs a search in the nucleotide sequence to find out the key components of gene. The predicted gene is then translated in the corresponding protein sequence. Based on protein sequence is then possible to identify the domains that characterize the protein functionality using specific tools of domain prediction. Protein domains classification are very important in the analysis of the macromolecular functionality. To analyze a whole protein family from large genome of various organism means to elaborate a large amount of data that requires huge computational resources. To analyze all this data we suggest the use of a high performance platform based on grid technology. We have implemented our applications on a wide area grid platform for scientific applications [http://www.grid.it and http://grid-it.cnaf.infn.it] composed of about 1000 CPU's. The grid infrastructure consists in a collection of computing elements and storage elements that jointly concur to define a platform for high performance elaboration. In this study a grid based application is presented to compute the protein domain analysis in a distributed way. This approach has high performance because the protein domains are checked with different software in parallel in different grid sites.

Background: This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian Grid.it project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites., Methods: Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs., Results: Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge., Conclusion: A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.

Background: Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure., Results: In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors., Conclusion: The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction.

Surface characterization of peptides may provide useful information about functionality and potential interactions with other molecules. A description of a protein site through a surface that models the shape conferred by the exposed residues is an effective tool for the analysis and the modeling of proteins that may highlight similarities and relationships not detectable through comparisons at level of primary, secondary, and tertiary structure. This study concerns the development of a tool that extracts the residues that concur to the shape modeling of the surface of a protein or a portion of it. This task is accomplished without taking into account the order of the amino acids in the primary structure, but only according to the selection of a portion of the protein indicated through geometric parameters or an explicit list of amino acids belonging to the site of interest. Both in the case of an entire protein and in the case of a portion of it, the method provides the mesh that models the surface described by the exposed residues that constitute the external envelope. The developed tool which allows the extraction of the exposed residues, and thus of the potential function determinants, is applied to identify the amino acids that concur to the structural interaction in several protein complexes.

It is well known that protein secondary-structure information can help the process of performing multiple alignment, in particular when the amount of similarity among the involved sequences moves toward the "twilight zone" (less than 30% of pairwise similarity). In this paper, a multiple alignment algorithm is presented, explicitly designed for exploiting any available secondary-structure information. A layered architecture with two interacting levels has been defined for dealing with both primary- and secondary-structure information of target sequences. Secondary structure (either available or predicted by resorting to a technique based on multiple experts) is used to calculate an initial alignment at the secondary level, to be arranged by locally scoped operators devised to refine the alignment at the primary level. Aimed at evaluating the impact of secondary information on the quality of alignments, in particular alignments with a low degree of similarity, the technique has been implemented and assessed on relevant test cases.

A new series of photocleavable protein cross-linking reagents based on bis(maleimide) derivatives of diaryl disulfides have been synthesised. They have been functionalised with cysteine and transient absorption spectra for the photolysis reaction have been recorded by using the pump-probe technique with a time resolution of 100 femtoseconds. Photolysis of the disulfide bond yields the corresponding thiyl radicals in less than a picosecond. There is a significant amount of geminate recombination, but some of the radicals escape the solvent cage and the quantum yield for photocleavage is 30 % in water.

The gene identification procedure in a completely new gene with no good homology with protein sequences can be a very complex task. In order to identify the protein-coding region, a new method, 'SYNCOD', based on the analysis of conservative evolutionary properties of coding regions, has been realized. This program is able to identify and use the coding region homologies of the non-annotated (unknown) protein-coding sequences already present in the nucleotide sequence databases by using the alignment produced by BLASTN. The ratio of number mismatches resulting in synonymous codons to the number of mismatches resulting in non-synonymous codons is estimated for each open reading frame. Monte Carlo simulations are then used to estimate the significance of the ratio deviation from random behavior. The SYNCOD program has been tested on generated random sequences and on different control sets. The high accuracy of predicting protein-coding regions (the correlation coefficient, CC, varies from 0.67 to 0.79) and the high specificity (the portion of wrong exons, WE, varies from 0.06 to 0.07) have proved to be important features of the suggested approach. The SYNCOD program is resident on the ITBA-CNR Web Server and can be used via the Internet (URL: www.itba.mi.cnr.it/webgene).

In this paper a new approach for the prediction of protein coding gene structures is described. The principal scheme of prediction is as follows: first, the exons with the best potential are predicted in a sequence with unknown functions and a list of potential amino acid fragments coded by these exons is formed. Second, testing the homology between each amino acid fragment from the list and proteins from the SWISS-PROT database of amino acid sequences. One protein with the best homology is chosen out of all the homologous sequences. Third, reconstruction of the exon-intron structure, basing it on its homology with the chosen protein sequences. The method was tested on an independent control set (20 genes). The results were as follows: 21% of real exons were lost and 3% of non-real exons were found. This system can be used to refine the results of gene prediction systems, especially if highly homologous proteins are found in the amino acid sequence database.

The completion of a number of large genome sequencing projects emphasizes the importance of protein-coding gene predictions. Most of the problems associated with gene prediction are caused by the complex exon-intron structures commonly found in eukaryotic genomes. However, information from homologous sequences can significantly improve the accuracy of the prediction. In particular, expressed sequence tags (ESTs) are very useful for this purpose, since currently existing EST collections are very large. We developed an ESTMAP system, which utilizes homology searches against a database of repetitive elements using the RepeatView program and the EST Division of GenBank using the BLASTN program. ESTMAP extracts "exact" matches with EST sequences (>95% of homology) from BLASTN output file and predicts introns in DNA comparing ESTs and a query sequence. ESTMAP is implemented as a part of the WebGene system (http://www.cnr.it/webgene). [ABSTRACT FROM PUBLISHER]