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Protein Secondary Structure Prediction . . .
- JO MOL B/O/. (1999) 292, 195-202 .GG
, 1999
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The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling
- BIOINFORMATICS
, 2005
"... Motivation: Homology models of proteins are of great interest for planning and analyzing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all re ..."
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Cited by 575 (5 self)
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Motivation: Homology models of proteins are of great interest for planning and analyzing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. Results: SWISS-MODEL Workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building, and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease
CATH -- a hierarchic classification of protein domain structures
- STRUCTURE
, 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
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Cited by 470 (33 self)
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Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. Results: We present a semi-automatic procedure for deriving a novel hierarchical classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast, architecture summarises the shape revealed by the orientations of the secondary structure units, such as barrels and sandwiches. At the topology level, sequential connectivity is considered, such that members of the same architecture might have quite different topologies. When structures belonging to the same T-level have suitably high similarities combined with similar functions, the proteins are assumed to be evolutionarily related and put into the same homologous superfamily. Conclusions: Analysis of the structural families generated by CATH reveals the prominent features of protein structure space. We find that nearly a third of the homologous superfamilies (H-levels) belong to ten major T-levels, which we call superfolds, and furthermore that nearly two-thirds of these H-levels cluster into nine simple architectures. A database of well-characterised protein structure families, such as CATH, will facilitate the assignment of structure–function/ evolution relationships to both known and newly determined protein structures.
Protein homology detection by HMM-HMM comparison
- BIOINFORMATICS
, 2005
"... Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction, and evolution. Results: We have generalized the alignment of protein se-quences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile H ..."
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Cited by 401 (8 self)
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Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction, and evolution. Results: We have generalized the alignment of protein se-quences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER, and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all compari-son of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%. Sensitivity: When predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approxi-mately half of the improvement over the profile–profile com-parison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an in-creased alignment quality. HHsearch produced 1.2, 1.7, and 3.3 times more good alignments (“balanced ” score> 0.3) than the next best method (COMPASS), and 1.6, 2.9, and 9.4 times more than PSI-BLAST, at the family, super-family, and fold level. Speed: HHsearch scans a query of 200 residues against 3691 domains in 33s on an AMD64 3GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than
Knowledge-based protein secondary structure assignment
- Proteins
, 1995
"... ABSTRACT We have developed an auto-matic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone tor-sional angle information. Parameters of the pattern recognition procedure were optimiz ..."
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Cited by 324 (2 self)
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ABSTRACT We have developed an auto-matic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone tor-sional angle information. Parameters of the pattern recognition procedure were optimized using designations provided by the crystallog-raphers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 222577-2637, 1983) shows that STRIDE and DSSP as-sign secondary structural states in 58 and 31% of 226 protein chains in our data sample, re-spectively, in greater agreement with the spe-cific residue-by-residue definitions provided by the discoverers of the structures while in 11 % of the chains, the assignments are the same. STRIDE delineates every 11 th helix and every 32nd strand more in accord with published assignments. Q 1995 Wiley-Liss, Inc. Key words: protein structure analysis, hydro-gen bond, torsional angle, a-helix, p-sheet
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
- Machine Learning
, 1993
"... In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of t ..."
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Cited by 309 (3 self)
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In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior ...
TM-align: A protein structure alignment algorithm based on TM-score
- Nucleic Acids Research
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Protein secondary structure from circular dichroism spectroscopy. Combining variable selection principle and cluster analysis with neural network, ridge regression and self-consistent
, 1994
"... We have expanded our reference set of proteins used in the estimation of protein secondary structure by CD spectroscopy from 29 to 37 proteins by including 3 additional globular proteins with known X-ray structure and 5 denatured proteins. We have also modified the self-consistent method for analyzi ..."
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Cited by 234 (1 self)
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We have expanded our reference set of proteins used in the estimation of protein secondary structure by CD spectroscopy from 29 to 37 proteins by including 3 additional globular proteins with known X-ray structure and 5 denatured proteins. We have also modified the self-consistent method for analyzing protein CD spectra, SELCON3, by including a new selection criterion
