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332
TM-align: A protein structure alignment algorithm based on TM-score
- Nucleic Acids Research
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MODBASE, a database of annotated comparative protein structure models
- Nucleic Acids Res
, 2000
"... and associated resources ..."
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3D-Jury: A simple approach to improve protein structure predictions
- Bioinformatics
"... Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable ..."
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Cited by 137 (18 self)
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Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. Results: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. Availability: The 3D-Jury system is available via the Structure Prediction Meta Server
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
, 2002
"... Motivation: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure pre ..."
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Cited by 75 (16 self)
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Motivation: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.
Alignment of protein sequences by their profiles
, 2004
"... The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thir ..."
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Cited by 69 (14 self)
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The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based align-ments with sequence identities below 40 % is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43 % for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.
Assessing the limits of genomic data integration for predicting protein networks
, 2005
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The principled design of large-scale recursive neural network architectures–dag-rnns and the protein structure prediction problem
, 2003
"... We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameteriza ..."
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Cited by 62 (20 self)
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We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connections to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dynamics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. The protein prediction servers are available over the
TASSER: an automated method for the prediction of protein tertiary structures
- in CASP6. Proteins 2005;61(Suppl 7):91–98
"... (Threading/ASSembly/Refinement) method is ap-plied to predict the tertiary structures of all CASP6 targets. TASSER is a hierarchical approach that consists of template identification by the threading program PROSPECTOR_3, followed by tertiary structure assembly via rearranging continuous tem-plate f ..."
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Cited by 60 (15 self)
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(Threading/ASSembly/Refinement) method is ap-plied to predict the tertiary structures of all CASP6 targets. TASSER is a hierarchical approach that consists of template identification by the threading program PROSPECTOR_3, followed by tertiary structure assembly via rearranging continuous tem-plate fragments. Assembly occurs using parallel hyperbolicMonte Carlo sampling under the guide of an optimized, reduced force field that includes knowledge-based statistical potentials and spatial restraints extracted from threading alignments. Models are automatically selected from the Monte Carlo trajectories in the low-temperature replicas using the clustering program SPICKER. For all 90 CASP targets/domains, PROSPECTOR_3 generates initial alignmentswith an average root-mean-square deviation (RMSD) to native of 8.4 Å with 79 % cover-age. After TASSER reassembly, the average RMSD decreases to 5.4 Å over the same aligned residues; the overall cumulativeTM-score increases from39.44 to 52.53. Despite significant improvements over the PROSPECTOR_3 template alignment observed in all target categories, the overall quality of the final models is essentially dictated by the quality of threading templates: The average TM-scores of TASSER models in the three categories are, respec-
Comparative protein structure modeling by iterative alignment, model building and model assessment
- Nucleic Acids Res
, 2003
"... Comparative or homology protein structure model-ing is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model impli ..."
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Cited by 57 (7 self)
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Comparative or homology protein structure model-ing is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model implied by it. This task is achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through re-alignment, model building and model assessment to optimize a model assessment score. During this iterative pro-cess: (i) new alignments are constructed by applica-tion of a number of operators, such as alignment mutations and cross-overs; (ii) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in our program MODELLER; (iii) the models are assessed by a variety of criteria, partly depending on an atomic statistical potential. When testing the procedure on a very dif®cult set of 19 modeling targets sharing only 4±27 % sequence identity with their template structures, the average ®nal align-ment accuracy increased from 37 to 45 % relative to the initial alignment (the alignment accuracy was measured as the percentage of positions in the tested alignment that were identical to the reference structure-based alignment). Correspondingly, the average model accuracy increased from 43 to 54% (the model accuracy was measured as the percent-age of the Ca atoms of the model that were within 5 AÊ of the corresponding Ca atoms in the super-posed native structure). The present method also compares favorably with two of the most successful previously described methods, PSI-BLAST and SAM. The accuracy of the ®nal models would be increased further if a better method for ranking of the models were available.
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res
, 2014
"... Protein structure homology modelling has become a routine technique to generate 3D models for pro-teins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable mod-els without the need for complex software pack-a ..."
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Cited by 54 (1 self)
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Protein structure homology modelling has become a routine technique to generate 3D models for pro-teins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable mod-els without the need for complex software pack-ages or downloading large databases. Here, we de-scribe the latest version of the SWISS-MODEL ex-pert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The im-proved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the ex-pected accuracy of the resulting models. The accu-racy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and se-lect the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided tem-plate selection step allows building models in differ-ent functional states. SWISS-MODEL is available at
