1,721,331 research outputs found
Dancing in the air: insects in flight
No full textThe article describes simple techniques to take pictures of insects in flights. The articles contains also information about insect-flight
A neural network based predictor of residue contacts in proteins
No full textWe describe a method based on neural networks for predicting contact maps of proteins using as input chemico-physical and evolutionary information. Neural networks are trained on a data set comprising the contact maps of 200 non-homologous proteins of well resolved three-dimensional structures. The systems learn the association rules between the covalent structure of each protein and its correspondent contact map by means of a standard back propagation algorithm. Validation of the predictor on the training set and on 408 proteins of known structure which are not homologous to those contained in the training set indicate that this method scores higher than statistical approaches previously described and based on correlated mutations and sequence information
HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins.
No full textIn this paper we describe a microcomputer program (HTP) for predicting the location and orientation of α-helical transmembrane segments in integral membrane proteins. HTP is a neural network- based tool which gives as output the protein membrane topology based on the statistical propensity of residues to be located in external and internal loops. This method, which uses single protein sequences as input to the network system, correctly predicts the topology of 71 out of 92 membrane proteins of putative membrane orientation, independently of the protein source
Prediction of disulfide connectivity in proteins
No full textMotivation: A major problem in protein structure prediction is the correct location of disulfide bridges in cysteine-rich proteins. In protein-folding prediction, the location of disulfide bridges can strongly reduce the search in the conformational space. Therefore the correct prediction of the disulfide connectivity starting from the protein residue sequence may also help in predicting its 3D structure. Results: In this paper we equate the problem of predicting the disulfide connectivity in proteins to a problem of finding the graph matching with the maximum weight. The graph vertices are the residues of cysteine-forming disulfide bridges, and the weight edges are contact potentials. In order to solve this problem we develop and test different residue contact potentials. The best performing one, based on the Edmonds-Gabow algorithm and Monte-Carlo simulated annealing reaches an accuracy significantly higher than that obtained with a general mean force contact potential. Significantly, in the case of proteins with four disulfide bonds in the structure, the accuracy is 17 times higher than that of a random predictor. The method presented here can be used to locate putative disulfide bridges in protein-folding
Dewdrop Spiders of the Genus Argyrodes
No full textNatural history account on the squatting spiders of the genus Argyrodes
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants
No full textEvolutionary information is the primary tool for detecting functional conservation in nucleic acid and protein. This information has been extensively used to predict structure, interactions and functions in macromolecules. Pathogenicity prediction models rely on multiple sequence alignment information at different levels. However, most accurate genome-wide variant deleteriousness ranking algorithms consider different features to assess the impact of variants. Here, we analyze three different ways of extracting evolutionary information from sequence alignments in the context of pathogenicity predictions at DNA and protein levels. We showed that protein sequence-based information is slightly more informative in the annotation of Clinvar missense variants than those obtained at the DNA level. Furthermore, to achieve the performance of state-of-the-art methods, such as CADD and REVEL, the conservation of reference and variant, encoded as frequencies of reference/alternate alleles or wild-type/mutant residues, should be included. Our results on a large set of missense variants show that a basic method based on three input features derived from the protein sequence profile performs similarly to the CADD algorithm which uses hundreds of genomic features. As expected, our method results in ~ 3% lower area under the receiver-operating characteristic curve (AUC). When compared with an ensemble-based algorithm (REVEL). Nevertheless, the combination of predictions of multiple methods can help to identify more reliable predictions. These observations indicate that for missense variants, evolutionary information, when properly encoded, plays the primary role in ranking pathogenicity
RCNPRED: prediction of the residue co-ordination numbers in proteins.
No full textThe RCNPRED server implements a neural network-based method to predict the co-ordination numbers of residues starting from the protein sequence. Using evolutionary information as input, RCNPRED predicts the residue states of the proteins in the database with 69% accuracy and scores 12 percentage points higher than a simple statistical method. Moreover the server implements a neural network to predict the relative solvent accessibility of each residue. A protein sequence can be directly submitted to RCNPRED: residue co-ordination numbers and solvent accessibility for each chain are returned via e-mail
Spider Beauty
No full textThe article describes some selected spiders in order to explain why they are nice and interesting
Ragni del genere Argyrodes
No full textDescrizione del comportamento in natura dei ragni chleptoparassiti del genere Argyrodes
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