7,147 research outputs found
Entropy-scaling search of massive biological data
Many datasets exhibit a well-defined structure that can be exploited to
design faster search tools, but it is not always clear when such acceleration
is possible. Here, we introduce a framework for similarity search based on
characterizing a dataset's entropy and fractal dimension. We prove that
searching scales in time with metric entropy (number of covering hyperspheres),
if the fractal dimension of the dataset is low, and scales in space with the
sum of metric entropy and information-theoretic entropy (randomness of the
data). Using these ideas, we present accelerated versions of standard tools,
with no loss in specificity and little loss in sensitivity, for use in three
domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics
(MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search
(esFragBag, 10x speedup of FragBag). Our framework can be used to achieve
"compressive omics," and the general theory can be readily applied to data
science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo
CLP-based protein fragment assembly
The paper investigates a novel approach, based on Constraint Logic
Programming (CLP), to predict the 3D conformation of a protein via fragments
assembly. The fragments are extracted by a preprocessor-also developed for this
work- from a database of known protein structures that clusters and classifies
the fragments according to similarity and frequency. The problem of assembling
fragments into a complete conformation is mapped to a constraint solving
problem and solved using CLP. The constraint-based model uses a medium
discretization degree Ca-side chain centroid protein model that offers
efficiency and a good approximation for space filling. The approach adapts
existing energy models to the protein representation used and applies a large
neighboring search strategy. The results shows the feasibility and efficiency
of the method. The declarative nature of the solution allows to include future
extensions, e.g., different size fragments for better accuracy.Comment: special issue dedicated to ICLP 201
Order independent structural alignment of circularly permuted proteins
Circular permutation connects the N and C termini of a protein and
concurrently cleaves elsewhere in the chain, providing an important mechanism
for generating novel protein fold and functions. However, their in genomes is
unknown because current detection methods can miss many occurances, mistaking
random repeats as circular permutation. Here we develop a method for detecting
circularly permuted proteins from structural comparison. Sequence order
independent alignment of protein structures can be regarded as a special case
of the maximum-weight independent set problem, which is known to be
computationally hard. We develop an efficient approximation algorithm by
repeatedly solving relaxations of an appropriate intermediate integer
programming formulation, we show that the approximation ratio is much better
then the theoretical worst case ratio of . Circularly permuted
proteins reported in literature can be identified rapidly with our method,
while they escape the detection by publicly available servers for structural
alignment.Comment: 5 pages, 3 figures, Accepted by IEEE-EMBS 2004 Conference Proceeding
ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space
Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The difficulties are often due to the essential role of spatial
and topological structures in the determination of protein functions in living
cells. In this paper, we propose ProtNN, a novel approach for protein function
prediction. Given an unannotated protein structure and a set of annotated
proteins, ProtNN finds the nearest neighbor annotated structures based on
protein-graph pairwise similarities. Given a query protein, ProtNN finds the
nearest neighbor reference proteins based on a graph representation model and a
pairwise similarity between vector embedding of both query and reference
protein-graphs in structural and topological spaces. ProtNN assigns to the
query protein the function with the highest number of votes across the set of k
nearest neighbor reference proteins, where k is a user-defined parameter.
Experimental evaluation demonstrates that ProtNN is able to accurately classify
several datasets in an extremely fast runtime compared to state-of-the-art
approaches. We further show that ProtNN is able to scale up to a whole PDB
dataset in a single-process mode with no parallelization, with a gain of
thousands order of magnitude of runtime compared to state-of-the-art
approaches
Hot-spot analysis for drug discovery targeting protein-protein interactions
Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions.
Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions.
Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft
Structural and Energetic Characterization of the Ankyrin Repeat Protein Family
Ankyrin repeat containing proteins are one of the most abundant solenoid folds. Usually implicated in specific protein-protein interactions, these proteins are readily amenable for design, with promising biotechnological and biomedical applications. Studying repeat protein families presents technical challenges due to the high sequence divergence among the repeating units. We developed and applied a systematic method to consistently identify and annotate the structural repetitions over the members of the complete Ankyrin Repeat Protein Family, with increased sensitivity over previous studies. We statistically characterized the number of repeats, the folding of the repeat-arrays, their structural variations, insertions and deletions. An energetic analysis of the local frustration patterns reveal the basic features underlying fold stability and its relation to the functional binding regions. We found a strong linear correlation between the conservation of the energetic features in the repeat arrays and their sequence variations, and discuss new insights into the organization and function of these ubiquitous proteins.Fil: Parra, Rodrigo Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Espada, Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Verstraete, Nina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Ferreiro, Diego Ulises. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin
Assessing Protein Conformational Sampling Methods Based on Bivariate Lag-Distributions of Backbone Angles
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations
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