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Abstract: We analyze the quality of the alignment generated by the profile-profile alignment comparison algorithm known as BASIC and compare the results with those obtained with a structural alignment code. By this we compute that a Shannon entropy value > 0.5 gives a sequence to sequence alignment of the target/template couple comparable to that obtained with the structural alignment performed with CE. In our fold recognition/threading code Tangram, the BASIC profile-profile alignment is implemented as follows: 1. The composition profiles PA and PB for the target and template are generated by multiple alignment of the sequences obtained from a three-iteration PSI-BLAST search on the Non-Redundant database (the inclusion threshold is E=10-3). 2. the dot matrix (D) for the profile comparison of two protein sequences D= PTA S PB, (with S=BLOSUM62 substitution matrix) is computed using linear algebra routines. 3. the D matrix is searched for high-scoring alignment by means local Smith-Waterman dynamic programming algorithm. The test set used for the evaluation is composed by 185 template/target couples of PDB structures that share the same SCOP label, but have less than 30% sequence identity When the top-scoring alignments for each target protein in the test set is considered, our BASIC implementation detects the full SCOP label for 125 couples (68%) and generates 114 (62%) alignments with a MaxSub score >=1. Interestingly, it is found that nearly all of the high-quality alignments share a common feature: the average Shannon entropy for the profile sections aligned together is greater than 0.5 for both the template and the target. If only the top scoring alignments for which this condition holds are considered, a subset of 119 alignments is selected, and for 116 of them (97%) the full SCOP label can be assigned to the target, while 108 (91%) gets a nonzero MaxSub score, with an average score of 4.6 MaxSub on the subset On the same 119 couples, the structural alignment program CE computes a nonzero MaxSub score for 116 of them, with an average of 5.7 points. These results indicate that the Shannon entropy value can be used to discriminate a subset of sequence profile-profile alignments of quality comparable to that obtained by means of a structural alignment program.

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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.

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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: A neural network-based predictor is trained to distinguish the bonding states of cysteine in proteins starting from the residue chain. Training is performed using 2452 cysteine-containing segments extracted from 641 non homologous proteins of well resolved 3D structure. After a cross-validation procedure efficiency of the prediction scores as high as 72% when the predictor is trained using protein single sequences. The addition of evolutionary information in the form of multiple sequence alignment and a jury of neural networks increase the prediction efficiency up to 81%. Assessment of the goodness of the prediction with a reliability index indicates that more than 60% of the predictions have an accuracy level greater than 90%. A comparison with a statistical method previously described and tested on the same database shows that the neural network-based predictor is performing with the highest efficiency.

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.

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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.