Biomedical subjects

Piero Fariselli

Publications and source records attributed to Piero Fariselli.

22 records · Page 2Linked to original sources

PubMed2002

Prediction of protein--protein interaction sites in heterocomplexes with neural networks.

In this paper we address the problem of extracting features relevant for predicting protein--protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.

Animals↗

PubMed2002

A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins.

MOTIVATION: Membrane proteins are an abundant and functionally relevant subset of proteins that putatively include from about 15 up to 30% of the proteome of organisms fully sequenced. These estimates are mainly computed on the basis of sequence comparison and membrane protein prediction. It is therefore urgent to develop methods capable of selecting membrane proteins especially in the case of outer membrane proteins, barely taken into consideration when proteome wide analysis is performed. This will also help protein annotation when no homologous sequence is found in the database. Outer membrane proteins solved so far at atomic resolution interact with the external membrane of bacteria with a characteristic beta barrel structure comprising different even numbers of beta strands (beta barrel membrane proteins). In this they differ from the membrane proteins of the cytoplasmic membrane endowed with alpha helix bundles (all alpha membrane proteins) and need specialised predictors. RESULTS: We develop a HMM model, which can predict the topology of beta barrel membrane proteins using, as input, evolutionary information. The model is cyclic with 6 types of states: two for the beta strand transmembrane core, one for the beta strand cap on either side of the membrane, one for the inner loop, one for the outer loop and one for the globular domain state in the middle of each loop. The development of a specific input for HMM based on multiple sequence alignment is novel. The accuracy per residue of the model is 83% when a jack knife procedure is adopted. With a model optimisation method using a dynamic programming algorithm seven topological models out of the twelve proteins included in the testing set are also correctly predicted. When used as a discriminator, the model is rather selective. At a fixed probability value, it retains 84% of a non-redundant set comprising 145 sequences of well-annotated outer membrane proteins. Concomitantly, it correctly rejects 90% of a set of globular proteins including about 1200 chains with low sequence identity (<30%) and 90% of a set of all alpha membrane proteins, including 188 chains.

Algorithms↗

PubMed2002

Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks.

A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 80% using neural networks based on evolutionary information. When the whole protein is taken into account by means of an HMM, a hybrid system is generated, whose emission probabilities are computed using the NN output (hidden neural networks). In this case, the predictor accuracy increases up to 88%. Further, when tested on a protein basis, the hybrid system can correctly predict 84% of the chains in the data set, with a gain of at least 27% over the NN predictor.

Cysteine↗

PubMed2002

Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy.

The task of predicting the cysteine-bonding state in proteins starting from the residue chain is addressed by implementing a new hybrid system that combines a neural network and a hidden Markov model (hidden neural network). Training is performed using 4136 cysteine-containing segments extracted from 969 nonhomologous proteins of well-resolved three-dimensional structure. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 88% and 84%, when measured on cysteine and protein basis, respectively. These results outperform previously described methods for the same task.

Cysteine↗