Co-evolution and co-adaptation in protein networks

Interacting or functionally related proteins have been repeatedly shown to have similar phylogenetic trees. Two main hypotheses have been proposed to explain this fact. One involves compensatory changes between the two protein families (co-adaptation). The other states that the tree similarity may be an indirect consequence of the involvement of the two proteins in similar cellular process, which in turn would be reflected by similar evolutionary pressure on the corresponding sequences. There are published data supporting both propositions, and currently the available information is compatible with both hypotheses being true, in an scenario in which both sets of forces are shaping the tree similarity at different levels.     

HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence

The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.     

Protein co-evolution, co-adaptation and interactions

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.     

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.     

Production of Dunaliella salina biomass rich in 9-cis-beta-carotene and lutein in a closed tubular photobioreactor

Performance of Dunaliella salina cultures outdoors in a closed tubular photobioreactor has been assessed. Optimization of conditions involved verification of the effect of several determining factors on the yield of both biomass and carotenoids. Maximal biomass productivity (over 2g (dry weight) m(-2) d(-1) or 80 gm(-3) d(-1)) was achieved at 38 cm s(-1), flow rate; 2 x 10(9) cells l(-1), initial population density; 25 degrees C, temperature; semi-continuous regime, keeping a cell density interval between 2 x 10(9) and over 4 x 10(9) cells l(-1). Coverage of the tubular loop with a sunshade screen to avoid light-induced damage of cells was essential to maintain growth performance. The cellular beta-carotene level increased significantly during the light period, as also did that of lutein. The rise in the beta-carotene level could be accounted by the 9-cis-isomer, with all-trans-beta-carotene remaining steady during the light period. By sunset, the ratio between 9-cis- and all-trans-isomers of beta-carotene amounted to 1.5, with over 60% of total beta-carotene corresponding to the 9-cis-isomer. Removal of sunshade enhanced carotenoid accumulation by cells to reach up to 10% of dry biomass. Cultivation of Dunaliella in closed tubular photobioreactor, thus represents a suitable approach for the production of a high-quality microalgal biomass enriched in the valuable 9-cis-isomer of beta-carotene and lutein.     

Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome

The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions.     

Automatic methods for predicting functionally important residues

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily ("Tree-determinant residues"). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.     

Structural studies on the synchronization of catalytic centers in glutamate synthase

The complex iron-sulfur flavoprotein glutamate synthase (GltS) plays a prominent role in ammonia assimilation in bacteria, yeasts, and plants. GltS catalyzes the formation of two molecules of l-glutamate from 2-oxoglutarate and l-glutamine via intramolecular channeling of ammonia. GltS has the impressive ability of synchronizing its distinct catalytic centers to avoid wasteful consumption of l-glutamine. We have determined the crystal structure of the ferredoxin-dependent GltS in several ligation and redox states. The structures reveal the crucial elements in the synchronization between the glutaminase site and the 2-iminoglutarate reduction site. The structural data combined with the catalytic properties of GltS indicate that binding of ferredoxin and 2-oxoglutarate to the FMN-binding domain of GltS induce a conformational change in the loop connecting the two catalytic centers. The rearrangement induces a shift in the catalytic elements of the amidotransferase domain, such that it becomes activated. This machinery, over a distance of more than 30 A, controls the ability of the enzyme to bind and hydrolyze the ammonia-donating substrate l-glutamine.