Fams-ace: A combined method to select the best model after remodeling all server models

Terashi, Genki; Takeda‐Shitaka, Mayuko; Kanou, Kazuhiko; Iwadate, Mitsuo; Takaya, Daisuke; Hosoi, Akira; Ohta, Kaisaku; Umeyama, Hideaki

doi:10.1002/prot.21785

Cited by 24 publications

(29 citation statements)

References 24 publications

(23 reference statements)

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“…Here, CCL represents the CIRCLE score 6) which was based on a 3D-1D profile score (like Verify3D 24) ), and the SSscore represents the secondary structure similarity score which was calculated by comparison between the secondary structure of the 3D model and the secondary structure predicted from the sequence. The details of this score were mentioned in reference six.…”

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confidence: 99%

“…6) As shown in Table 2, the w value is the weighting factor for the SSscore which was optimized using the training set based on CASP7 targets. The weight values of w were 0.3 and 1 for easy and difficult targets, respectively.…”

Section: )mentioning

confidence: 99%

“…Conversely, the CIRCLE score, which is the major part of the Score str , estimates the stability of a protein structure from a free energy point of view. 6) For difficult targets in which the alignment scores are not reliable, the CIRCLE score estimates a model quality more appropriately than the alignment score. In some cases, however, the CIRCLE score provides a high score to a model which has many stable regions locally in the model even if the global structure was not close in structure to the native fold.…”

Section: Fig 1 Schematic Diagram Of the Alignment Selectionmentioning

confidence: 99%

“…This automated protein modeling approach is called FAMSD. At each of the steps (1)-(4), sequence alignment programs such as SP3 2) and SPARKS2, 3) homology modeling program FAMS, 4,5) model quality estimation program CIR-CLE 6) and the molecular dynamics program APRICOT 7) were primarily used and combined.…”

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confidence: 99%

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FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

Kanou

Iwadate

Hirata

et al. 2009

CHEMICAL & PHARMACEUTICAL BULLETIN

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1335The number of three-dimensional (3D) structures of proteins solved by experimental methods is rapidly increasing. As of June 2009, more than 58000 structures were available in the Protein Data Bank (PDB).1) However, the number of amino acid sequences whose 3D structures have not been determined remains more than 100 times greater. Therefore, some approaches for accurate protein structure prediction are urgently required. One of the most effective approaches for protein structure prediction is a comparative modeling (CM) method. This technique uses 3D template structures that have high sequence identities with the target protein. In this paper, we describe our comparative modeling platform which consists of the following four steps: (1) generating and filtering sequence alignments between the target and template proteins; (2) constructing 3D structure models based on each alignment; (3) selecting the best structural model among the candidates; and (4) were primarily used and combined. We have successively participated in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments [8][9][10][11][12] to assess our modeling methods. The CASP experiment is held once every two years with the aim of assessing technical progress in the prediction of protein structures. As a result, protein modeling techniques have progressed. In recent CASP experiments, more than one hundred protein sequences of unknown structures were released by the CASP organizers. The goal for each participating team is to correctly predict the 3D structures from the amino acid sequences. After the prediction period expires, the CASP assessors assess the quality of all models predicted by each participating team. From April to August 2008, the 8th CASP (CASP8) experiment was held and 128 target protein sequences were released.13) We participated in CASP8 as an automatic predictor using the server for which our FAMSD method was internally included. In this paper, the basic algorithm of the FAMSD and the results of the protein modeling for CASP8 targets are described. We show that the modeling method using FAMSD generates reliable 3D protein structures. The protein structures predicted with the FAMSD method should be extremely valuable for experimental researchers. -BLAST, SP3 2) and SPARKS23) were executed. SPARKS2 and SP3 programs were shown to be excellent in the CASP6 experiment.19) Various alignments were obtained and were filtered with our original alignment score value.First, the alignment score value was calculated using the following Eq. 1 for six BLAST-related alignment methods. These methods were BLAST, PSI-BLAST, PSF-BLAST, RPS-BLAST, IMPALA and Pfam-BLAST.Here, Len represents the number of residues of the region aligned to the template protein, SEQid represents the sequence identity percent, SS is the degree of match between the predicted secondary structure elements (SSE) from the target sequence and the SSE of the template protein. The predicted SSE from the amino acid sequence was obtained with PSI-PR...

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confidence: 99%

Section: )mentioning

confidence: 99%

Section: Fig 1 Schematic Diagram Of the Alignment Selectionmentioning

confidence: 99%

mentioning

confidence: 99%

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FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

Kanou

Iwadate

Hirata

et al. 2009

CHEMICAL & PHARMACEUTICAL BULLETIN

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“…The analysis is a straightforward extension of the 1D-3D method of VERIFY3D [24] and the related methods [25] [40]. At each residue of the model, the local structural environment is classified in terms of structural indices, and the protein structure is represented by a string of structural indices, which we call the structure string.…”

Section: Introductionmentioning

confidence: 99%

The Fragment-based Consistency Score in Model Quality Assessment for De Novo Prediction of Protein Structures

Cetin

Sasaki

Sasai

2011

CBIJ

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Prediction of three-dimensional structures of proteins from amino-acid sequences is an important problem of bioinformatics. We still do not have, however, a reliable computational method for de novo prediction, i.e., prediction of structures that do not have homologues of known structures. One way to improve techniques of de novo prediction is to assess quality of the predicted model structures to select candidate models from them before knowing the experimentally determined structure. In this paper, we develop a new method of model quality assessment for de novo prediction, the fragment-based consistency score (FCS) method. In the FCS method, fragments from library proteins are collected for each fragment of the model, and structural similarities between the model fragment and the library fragments are measured for assessing qualities of the model. Three structural indices are employed; secondary structure, local density and local contact order. The optimal performance was obtained when relatively correlated fragments are collected, and structural similarity is measured in fuzzy comparison and averaged by finite width matching. The FCS method can select partially correct models for hard targets of de novo prediction examined in CASP7 and CASP8. We also evaluated the abilities of the FCS method to select structural models of loops or coil regions in targets, and to distinguish proteins which are similar in sequence but have greatly different conformations from each other. The proposed FCS method helps to improve the de novo prediction scheme and is also useful to solve difficult structures in the template-based modeling.

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Protein Structural Domains: Definition and Prediction

Ezkurdia

Tress

2011

CP Protein Science

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Recognition and prediction of structural domains in proteins is an important part of structure and function prediction. This unit lists the range of tools available for domain prediction, and describes sequence and structural analysis tools that complement domain prediction methods. Also detailed are the basic domain prediction steps, along with suggested strategies for different protein sequences and potential pitfalls in domain boundary prediction. The difficult problem of domain orientation prediction is also discussed. All the resources necessary for domain boundary prediction are accessible via publicly available Web servers and databases and do not require computational expertise.

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Fams-ace: A combined method to select the best model after remodeling all server models

Cited by 24 publications

References 24 publications

FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

The Fragment-based Consistency Score in Model Quality Assessment for De Novo Prediction of Protein Structures

Protein Structural Domains: Definition and Prediction

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