Biomedical subjects

Marialuisa Pellegrini-Calace

Publications and source records attributed to Marialuisa Pellegrini-Calace.

5 recordsLinked to original sources

PubMed2006

Revisiting the prediction of protein function at CASP6.

The ability to predict the function of a protein, given its sequence and/or 3D structure, is an essential requirement for exploiting the wealth of data made available by genomics and structural genomics projects and is therefore raising increasing interest in the computational biology community. To foster developments in the area as well as to establish the state of the art of present methods, a function prediction category was tentatively introduced in the 6th edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP) worldwide experiment. The assessment of the performance of the methods was made difficult by at least two factors: (a) the experimentally determined function of the targets was not available at the time of assessment; (b) the experiment is run blindly, preventing verification of whether the convergence of different predictions towards the same functional annotation was due to the similarity of the methods or to a genuine signal detectable by different methodologies. In this work, we collected information about the methods used by the various predictors and revisited the results of the experiment by verifying how often and in which cases a convergent prediction was obtained by methods based on different rationale. We propose a method for classifying the type and redundancy of the methods. We also analyzed the cases in which a function for the target protein has become available. Our results show that predictions derived from a consensus of different methods can reach an accuracy as high as 80%. It follows that some of the predictions submitted to CASP6, once reanalyzed taking into account the type of converging methods, can provide very useful information to researchers interested in the function of the target proteins.

Caspase 6↗

PubMed2006

Towards genome-scale structure prediction for transmembrane proteins.

In this paper we briefly review some of the recent progress made by ourselves and others in developing methods for predicting the structures of transmembrane proteins from amino acid sequence. Transmembrane proteins are an important class of proteins involved in many diverse biological functions, many of which have great impact in terms of disease mechanism and drug discovery. Despite their biological importance, it has proven very difficult to solve the structures of these proteins by experimental techniques, and so there is a great deal of pressure to develop effective methods for predicting their structure. The methods we discuss range from methods for transmembrane topology prediction to new methods for low resolution folding simulations in a knowledge-based force field. This potential is designed to reproduce the properties of the lipid bilayer. Our eventual aim is to apply these methods in tandem so that useful three-dimensional models can be built for a large fraction of the transmembrane protein domains in whole proteomes.

Cell Membrane↗

PubMed2006

Identification of a novel putative mitogen-activated kinase cascade on human chromosome 21 by computational approaches.

UNLABELLED: Down syndrome (DS) is the most frequent form of mental retardation and is caused by chromosome 21 (HSA21) trisomy. Despite the number of known genes involved in DS and its high therapeutic interest, biological mechanisms leading to the DS phenotype are not fully clear. We present a functional hypothesis based on fold recognition and hidden Markov model techniques for four HSA21 genes located in the DS Candidate Region (DSCR). More specifically, we propose that they are members of a novel mitogen-activated protein kinase pathway with DYRK1A, SNF1LK and RIPK4 gene products being elements of the kinase cascade and the DSCR3 acting as structural scaffold for their interaction. This hypothesis finds support in various biochemical studies concerning the biological behavior and features of the involved HSA21 proteins. Our analysis calls for specifically designed experiments to validate our prediction and establish its relevance in terms of therapeutic approaches to the disease. CONTACT: anna.tramontano@uniroma1.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Amino Acid Sequence↗

PubMed2005

Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information.

In this work, we analyse the potential for using structural knowledge to improve the detection of the DNA-binding helix-turn-helix (HTH) motif from sequence. Starting from a set of DNA-binding protein structures that include a functional HTH motif and have no apparent sequence similarity to each other, two different libraries of hidden Markov models (HMMs) were built. One library included sequence models of whole DNA-binding domains, which incorporate the HTH motif, the second library included shorter models of 'partial' domains, representing only the fraction of the domain that corresponds to the functionally relevant HTH motif itself. The libraries were scanned against a dataset of protein sequences, some containing the HTH motifs, others not. HMM predictions were compared with the results obtained from a previously published structure-based method and subsequently combined with it. The combined method proved more effective than either of the single-featured approaches, showing that information carried by motif sequences and motif structures are to some extent complementary and can successfully be used together for the detection of DNA-binding HTHs in proteins of unknown function.

Amino Acid Sequence↗

PubMed2004

Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was mostly engaged by a number of nonlinear QSAFIRs, which relate nAChR affinity with the log P and directional WHIM descriptors. The results reviewed herein show as QSAFIRs may helpfully complement the pharmacophores, thus enhancing the applicability of computer-aided methodologies in the field of nAChR agonists.

Hydrogen Bonding↗