8,892 research outputs found
Massive non-natural proteins structure prediction using grid technologies
Background
The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of pr oteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is thei r ability to form a well defined three-dimensional structure. T hus, the structural study of NBPs can help to understand if natural protein sequences were selecte d for their peculiar properties or if they are just one of the possible stable and functional ensembles.
Methods
The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 Ă— 10 ^4 sequences) was generated, discarding amino acid sequences with significant simi larity to natural proteins, and the corresponding structures were
predicted using Rosetta. Given th e highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc.
Results
The vast majority of NBPs, according to the Rosetta mode l, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix- β strands relative content and inydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins.
Conclusion
The results obtained suggest that the abil ity to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to
adopt α helical folds indicate that all-α proteins may have emerged ea rly in pre-biotic evolution.
Further, the lower percentage of aromatic residu es observed in natural proteins has important evolutionary implications as far as tolerance to mutati ons is concerned
Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?
The organization and mining of malaria genomic and post-genomic data is
highly motivated by the necessity to predict and characterize new biological
targets and new drugs. Biological targets are sought in a biological space
designed from the genomic data from Plasmodium falciparum, but using also the
millions of genomic data from other species. Drug candidates are sought in a
chemical space containing the millions of small molecules stored in public and
private chemolibraries. Data management should therefore be as reliable and
versatile as possible. In this context, we examined five aspects of the
organization and mining of malaria genomic and post-genomic data: 1) the
comparison of protein sequences including compositionally atypical malaria
sequences, 2) the high throughput reconstruction of molecular phylogenies, 3)
the representation of biological processes particularly metabolic pathways, 4)
the versatile methods to integrate genomic data, biological representations and
functional profiling obtained from X-omic experiments after drug treatments and
5) the determination and prediction of protein structures and their molecular
docking with drug candidate structures. Progresses toward a grid-enabled
chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa
Novel Tools for the Evaluation of the Health Status of Coral Reefs Ecosystems and for the Prediction of Their Biodiversity in the Face of Climatic Changes
International audienceTropical reefs concentrate between one quarter and one third of the total marine biodiversity, though they only cover about 0.1% of the global oceanic surface and are confined to warmer latitudes. Half a billion humans depend partly or totally on the goods and services provided by coral reef ecosystems. However, coral reefs are now recognized as being among the most fragile of all environments in the face of localized anthropic pressures and of their climatic consequences of planetary dimensions. Today less than 20% can still be regarded as unharmed. Research scientists continue to explore natural biodiversity in remote pristine environments (especially biodiversity hotspots, a very useful conservation-promoting concept) and to observe its lossesin degrading habitats. Programs on bioremediation of impacted sites are attracting funds, and classification of natural habitats as protected sites is gaining public support. Yet the scientific community has very little leverage on the decision-making of potentially impacting industrial, commercial and urban development projects, and on the say-so broadcast by their promoters in the media. In this chapter, a holistic concept is proposed that (i) integrates cutting-edge molecular research and standard technologies with field sampling and laboratory simulations of natural habitats (ii) using holobiont-based sentinel systems, (iii) into a single tool that "shows evidence" of ongoing degradation rather than aftermath "score loss". Corrective action can then be taken in specific directions before no-return limits have been reached and total ecosystem collapse is on the way
Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems
A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a
predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the
Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in
Computational Biology.Peer ReviewedPostprint (author's final draft
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community
The EMBnet Conference 2008, focusing on 'Leading Applications and Technologies in Bioinformatics', was organized by the European Molecular Biology network (EMBnet) to celebrate its 20th anniversary. Since its foundation in 1988, EMBnet has been working to promote collaborative development of bioinformatics services and tools to serve the European community of molecular biology laboratories. This conference was the first meeting organized by the network that was open to the international scientific community outside EMBnet. The conference covered a broad range of research topics in bioinformatics with a main focus on new achievements and trends in emerging technologies supporting genomics, transcriptomics and proteomics analyses such as high-throughput sequencing and data managing, text and data-mining, ontologies and Grid technologies. Papers selected for publication, in this supplement to BMC Bioinformatics, cover a broad range of the topics treated, providing also an overview of the main bioinformatics research fields that the EMBnet community is involved in
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