459 research outputs found

    Contribution of electrostatic interactions, compactness and quaternary structure to protein thermostability: lessons from structural genomics of Thermotoga maritima.

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    Studies of the structural basis of protein thermostability have produced a confusing picture. Small sets of proteins have been analyzed from a variety of thermophilic species, suggesting different structural features as responsible for protein thermostability. Taking advantage of the recent advances in structural genomics, we have compiled a relatively large protein structure dataset, which was constructed very carefully and selectively; that is, the dataset contains only experimentally determined structures of proteins from one specific organism, the hyperthermophilic bacterium Thermotoga maritima, and those of close homologs from mesophilic bacteria. In contrast to the conclusions of previous studies, our analyses show that oligomerization order, hydrogen bonds, and secondary structure play minor roles in adaptation to hyperthermophily in bacteria. On the other hand, the data exhibit very significant increases in the density of salt-bridges and in compactness for proteins from T.maritima. The latter effect can be measured by contact order or solvent accessibility, and network analysis shows a specific increase in highly connected residues in this thermophile. These features account for changes in 96% of the protein pairs studied. Our results provide a clear picture of protein thermostability in one species, and a framework for future studies of thermal adaptation

    Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

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    One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward

    Scaling of the Hysteresis Loop in Two-dimensional Solidification

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    The first order phase transitions between a two-dimensional (2d) gas and the 2d solid of the first monolayer have been studied for the noble gases Ar, Kr and Xe on a NaCl(100) surface in quasi-equilibrium with the three-dimensional gas phase. Using linear temperature ramps, we show that the widths of the hysteresis loops of these transitions as a function of the heating rate, r, scales with a power law r^alpha with alpha between 0.4 and 0.5 depending on the system. The hysteresis loops for different heating rates are similar. The island area of the condensed layer was found to grow initially with a t^4 time dependence. These results are in agreement with theory, which predicts alpha = 0.5 and hysteresis loop similarity.Comment: 4 pages, 5 figures, Revte

    A New Simulated Annealing Algorithm for the Multiple Sequence Alignment Problem: The approach of Polymers in a Random Media

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    We proposed a probabilistic algorithm to solve the Multiple Sequence Alignment problem. The algorithm is a Simulated Annealing (SA) that exploits the representation of the Multiple Alignment between DD sequences as a directed polymer in DD dimensions. Within this representation we can easily track the evolution in the configuration space of the alignment through local moves of low computational cost. At variance with other probabilistic algorithms proposed to solve this problem, our approach allows for the creation and deletion of gaps without extra computational cost. The algorithm was tested aligning proteins from the kinases family. When D=3 the results are consistent with those obtained using a complete algorithm. For D>3D>3 where the complete algorithm fails, we show that our algorithm still converges to reasonable alignments. Moreover, we study the space of solutions obtained and show that depending on the number of sequences aligned the solutions are organized in different ways, suggesting a possible source of errors for progressive algorithms.Comment: 7 pages and 11 figure

    Divergent evolution of protein conformational dynamics in dihydrofolate reductase.

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    Molecular evolution is driven by mutations, which may affect the fitness of an organism and are then subject to natural selection or genetic drift. Analysis of primary protein sequences and tertiary structures has yielded valuable insights into the evolution of protein function, but little is known about the evolution of functional mechanisms, protein dynamics and conformational plasticity essential for activity. We characterized the atomic-level motions across divergent members of the dihydrofolate reductase (DHFR) family. Despite structural similarity, Escherichia coli and human DHFRs use different dynamic mechanisms to perform the same function, and human DHFR cannot complement DHFR-deficient E. coli cells. Identification of the primary-sequence determinants of flexibility in DHFRs from several species allowed us to propose a likely scenario for the evolution of functionally important DHFR dynamics following a pattern of divergent evolution that is tuned by cellular environment

    Evolution of Voronoi-based Fuzzy Controllers

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    A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. One of the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose a so-called ``approximative'' representation for fuzzy systems, where the antecedent of the rules are determined by a multivariate membership function defined in terms of Voronoi regions. Such representation guarantees the ϵ\epsilon-completeness property and provides a synergistic relation between the rules. An evolutionary algorithm based on this representation can evolve all the components of the fuzzy system, and due to the properties of the representation, the algorithm (1) can benefit from the use of geometric genetic operators, (2) does not need genetic repair algorithms, (3) guarantees the completeness property and (4) can implement previous knowledge in a simple way by using adaptive a priori rules. The proposed representation is evaluated on an obstacle avoidance problem with a simulated mobile robot

    TOPSAN: a collaborative annotation environment for structural genomics

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    <p>Abstract</p> <p>Background</p> <p>Many protein structures determined in high-throughput structural genomics centers, despite their significant novelty and importance, are available only as PDB depositions and are not accompanied by a peer-reviewed manuscript. Because of this they are not accessible by the standard tools of literature searches, remaining underutilized by the broad biological community.</p> <p>Results</p> <p>To address this issue we have developed TOPSAN, The Open Protein Structure Annotation Network, a web-based platform that combines the openness of the wiki model with the quality control of scientific communication. TOPSAN enables research collaborations and scientific dialogue among globally distributed participants, the results of which are reviewed by experts and eventually validated by peer review. The immediate goal of TOPSAN is to harness the combined experience, knowledge, and data from such collaborations in order to enhance the impact of the astonishing number and diversity of structures being determined by structural genomics centers and high-throughput structural biology.</p> <p>Conclusions</p> <p>TOPSAN combines features of automated annotation databases and formal, peer-reviewed scientific research literature, providing an ideal vehicle to bridge a gap between rapidly accumulating data from high-throughput technologies and a much slower pace for its analysis and integration with other, relevant research.</p

    The JCSG high-throughput structural biology pipeline

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    The Joint Center for Structural Genomics high-throughput structural biology pipeline has delivered more than 1000 structures to the community over the past ten years and has made a significant contribution to the overall goal of the NIH Protein Structure Initiative (PSI) of expanding structural coverage of the protein universe

    Simplified amino acid alphabets based on deviation of conditional probability from random background

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    The primitive data for deducing the Miyazawa-Jernigan contact energy or BLOSUM score matrix consists of pair frequency counts. Each amino acid corresponds to a conditional probability distribution. Based on the deviation of such conditional probability from random background, a scheme for reduction of amino acid alphabet is proposed. It is observed that evident discrepancy exists between reduced alphabets obtained from raw data of the Miyazawa-Jernigan's and BLOSUM's residue pair counts. Taking homologous sequence database SCOP40 as a test set, we detect homology with the obtained coarse-grained substitution matrices. It is verified that the reduced alphabets obtained well preserve information contained in the original 20-letter alphabet.Comment: 9 pages,3figure
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