7,008 research outputs found
Automated Protein Structure Classification: A Survey
Classification of proteins based on their structure provides a valuable
resource for studying protein structure, function and evolutionary
relationships. With the rapidly increasing number of known protein structures,
manual and semi-automatic classification is becoming ever more difficult and
prohibitively slow. Therefore, there is a growing need for automated, accurate
and efficient classification methods to generate classification databases or
increase the speed and accuracy of semi-automatic techniques. Recognizing this
need, several automated classification methods have been developed. In this
survey, we overview recent developments in this area. We classify different
methods based on their characteristics and compare their methodology, accuracy
and efficiency. We then present a few open problems and explain future
directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront
Spherical harmonics coeffcients for ligand-based virtual screening of cyclooxygenase inhibitors
Background: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. Methodology/Principal Findings: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. Conclusions/Significance: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort
Visual and computational analysis of structure-activity relationships in high-throughput screening data
Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets
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
Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.
BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used
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