A structural view of microRNA-target recognition

It is well established that the correct identification of the messenger RNA targeted by a given microRNA (miRNA) is a difficult problem, and that available methods all suffer from low specificity. We hypothesize that the correct identification of the pairing should take into account the effect of the Argonaute protein (AGO), an essential catalyst of the recognition process. Therefore, we developed a strategy named MiREN for building and scoring three-dimensional models of the ternary complex formed by AGO, a miRNA and 22 nt of a target mRNA that putatively interacts with it. We show here that MiREN can be used to assess the likelihood that an RNA molecule is the target of a given miRNA and that this approach is more accurate than other existing methods, usually based on sequence or sequence-related features. Our results also suggest that AGO plays a relevant role in the selection of the miRNA targets. Our method can represent an additional step for refining predictions made by faster but less accurate classical methods for the identification of miRNA targets

Prediction of the permeability of neutral drugs inferred from their solvation properties

Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay

Comparative Modelling Techniques: Where are we?

The enormous increase in data availability brought about by genomic projects is paralleled by an equally unprecedented increase in the expectations for new medical, pharmacological, environmental and biotechnological discoveries. Whether or not we will be able to meet (at least partially) these expectations will depend on how well we will be able to interpret the data and translate the mono-dimensional information encrypted in genomes into a detailed understanding of its biological meaning at the phenotypic level. The process is far from being trivial, and the obstacles along the road are formidable: even the problem of identifying coding regions in eukaryotic genomes is not completely solved. Far more complex is identification of the function of the encoded proteins, and this will probably represent the most challenging problem for the next generations of scientists

Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures

In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category

LoopIng: A template-based tool for predicting the structure of protein loops

MOTIVATION: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function. RESULTS: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop)

New encouraging developments in contact prediction: Assessment of the CASP11 results

This article provides a report on the state-of-the-art in the prediction of intra-molecular residue-residue contacts in proteins based on the assessment of the predictions submitted to the CASP11 experiment. The assessment emphasis is placed on the accuracy in predicting long-range contacts. Twenty-nine groups participated in contact prediction in CASP11. At least eight of them used the recently developed evolutionary coupling techniques, with the top group (CONSIP2) reaching precision of 27% on target proteins that could not be modeled by homology. This result indicates a breakthrough in the development of methods based on the correlated mutation approach. Successful prediction of contacts was shown to be practically helpful in modeling three-dimensional structures; in particular target T0806 was modeled exceedingly well with accuracy not yet seen for ab initio targets of this size (>250 residues

Critical assessment of methods of protein structure prediction: Progress and new directions in round XI

Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas

Horizontal and vertical growth of S. cerevisiae metabolic network

<p>Abstract</p> <p>Background</p> <p>The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions.</p> <p>Results</p> <p>We analysed a well-studied unicellular eukaryotic organism, <it>S. cerevisiae</it>, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in <it>S. cerevisiae </it>can be dated to about 350 million years ago.</p> <p>Conclusions</p> <p>Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the <it>S. cerevisiae </it>metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.</p

The prediction of protein function at CASP6

In the CASP6 experiment, the new "Function Prediction" category was tentatively introduced. Predictors were asked to provide functional information on the CASP targets, many of which were of unknown function. This article describes the setup of the experiment and its results, highlighting what was learned from it, and suggesting modifications to its format for the next rounds. The obvious limitation of such an experiment is that the results cannot be assessed in the standard CASP fashion, as all targets remain of unknown function. Furthermore, we had to face the expected difficulties due to the novelty of the experiment and to the problems connected with function definition. Nevertheless, and even with a limited number of participating groups, we believe that the results of the experiment can be useful both for its future and for experimentalists working on the functional assignment of the CASP6 targets. We found that, in a few cases, a consensus functional prediction could be derived for targets of unknown function. However, our analysis suggests that a general description of the method used should be made available together with the predictions so that a higher reliability can be assigned to cases where completely independent methods give the same or similar predictions