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Abstract: Supervised Neural Networks have been proved to be some of the most efficient tools to predict secondary structure of proteins from their aminoacid sequences. We developed a method that is able to evaluate the reliability of the predictions and the stability of helical structural motifs. A neural network with a 13 residue-long input window and a 2 neuron output is trained to recognize 2 classes: residues that have or not have a native a-helical structure in the protein data base. The two activations of the output neurons are interpreted as the probabilities for the central residue of the input fragment to be or not to be in helical structure and the Shannon entropy of the output is used as a measure of the prediction reliability . A data base of minimally frustrated alpha helical segments is then defined by filtering a set comprising 822 non redundant proteins, which contain 4783 alpha helical structures. The data base definition is performed using the neural network-based alpha-helix predictor, whose outputs are rated according to an entropy criterion. A comparison with the presently available experimental results indicates that a subset of the data base contains the initiation sites of protein folding experimentally detected and also protein fragments which fold into stable isolated alpha-helices. This suggests the usage of the data base (and/or of the predictor) to highlight patterns which govern the stability of alpha helices in proteins and the helical behavior of isolated protein fragments.

Abstract: Within the Biosapiens network of excellence (EC Framework VI), the Biocomputing Group of the Bologna University installed a DAS server in a pipeline connected to the EBI. Our task in collaboration with Gunnar von Hejne (Stockholm Bioinformatics Center, SCFAB, Stockholm University, Sweden), Gert Vriend (CMBI University of Nijmegen, the Netherlands) and David Jones (Bioinformatics Unit, University College London, United Kingdom) is the large scale screening of the human genome in order to annotate membrane proteins based on topology prediction of chains.

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Abstract: Hidden Markov models (HMM) are powerful machine learning tools successfully applied to problems of computational Molecular Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the class labeling, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the automaton grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi.

Abstract: Neural networks have been proved to be the most efficient methods for the protein secondary structure prediction. However one of the problems in using neural networks for sequence analysis is the independence of every prediction from the others. This is introducing noise in the results. For example the most evident effect is the presence of helices and strands one-residue long in the prediction of protein secondary structure. Since the shortest helical stretch is 3-residue long (if the 310 helices are included in this class) and the shortest strand is 2-residue long, these predictions can be regarded as 'syntax errors'. As a consequence, the length distributions of the segments of secondary structure predicted by Neural Networks is quite different from those of helices, strands and coil extracted from the atomic-resolved structures of the PDB database. In this work we propose a new kind of filter, based on Hidden Markov Models. We take advantage of their capabilities in capturing the duration of phenomena. For instance it is possible to include in HMMs a 'minimum length' constraint, in order to avoid the single residue predictions of helices and beta-strands. Our tool consists of a 6-states HMM: 3 states are labeled as helical states, 2 as strand and 1 as coil, following the minimal length observed in the database for these structural types. The transition probabilities among the states are then computed from the databases of known structures. Every residue along the sequence is emitted by each state with probability values equal to the outputs of the Neural Network predictor. The prediction of the HMM is given by the Viterbi-decoding, namely by the computation of the most probable path through the states of the model, given the Neural Network outputs. We prove that the filter doesn't affect the prediction efficiency of the Neural Network (72 % of overall accuracy when three structural states are discriminated). However its application improves considerably the length distributions of the predicted structures as compared to a Neural Network based filter previously adopted.

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Abstract: What is thermostability? This question is still unanswered in spite of several studies aiming at the determination of typical features of thermostable proteins (for a recent review see [1]). We tackled the problem considering a large set of proteins from thermophilic and hyperthermophilic organisms available in the PDB with atomic resolution. A PDB derived data base was generated containing proteins from thermophiles and their counterparts from mesophiles, with the specific constraint of sequence identity >30% and difference in sequence length <20%. By this, 128 proteins from thermophiles were compared to 109 structures from mesophiles with a root mean square deviation <0.29 nm. Residue composition, secondary structure, length of secondary structure motifs, hydrogen bonds, salt bridges, composition of solvent accessible surface were evaluated with specifically developed programs in both sets in order to perform a statistical analysis. The results of our investigation are as follows: proteins from thermophiles are endowed with more charged residues, particularly in the exposed surfaces, with more salt bridges, that are more accessible on average as compared to those in proteins from mesophiles. However neither the content of secondary structure neither the length of secondary structure motifs was significantly different. These data, all together suggest that thermostable proteins as compared to their mesophilic counterpart are endowed with more electrostatic interactions, particularly on the protein surface to stabilize more water dipoles and compensate for thermal motion at high temperatures.