Assessment of predictions in the model quality assessment category

Cozzetto, Domenico; Kryshtafovych, Andriy; Ceriani, Miguel; Tramontano, Anna

doi:10.1002/prot.21669

Cited by 122 publications

(132 citation statements)

References 23 publications

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“…The equations of motion were integrated with the Shake algorithm with a time step of 1 fs. The modeling quality of 3D models was evaluated by MQAP (Fischer, 2006) and the CASP7 experiment methods of QA (Cozzetto et al, 2007). Figures displaying atomistic pictures of molecules were generated using UCSF Chimera (Pettersen et al, 2004).…”

Section: Prediction Of 3d Protein Structure and Interaction By Molecumentioning

confidence: 99%

The bHLH142 Transcription Factor Coordinates with TDR1 to Modulate the Expression of EAT1 and Regulate Pollen Development in Rice

Li²,

et al. 2014

The Plant Cell

139

199

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Male sterility plays an important role in F1 hybrid seed production. We identified a male-sterile rice (Oryza sativa) mutant with impaired pollen development and a single T-DNA insertion in the transcription factor gene bHLH142. Knockout mutants of bHLH142 exhibited retarded meiosis and defects in tapetal programmed cell death. RT-PCR and in situ hybridization analyses showed that bHLH142 is specifically expressed in the anther, in the tapetum, and in meiocytes during early meiosis. Three basic helix-loop-helix transcription factors, UDT1 (bHLH164), TDR1 (bHLH5), and EAT1/DTD1 (bHLH141) are known to function in rice pollen development. bHLH142 acts downstream of UDT1 and GAMYB but upstream of TDR1 and EAT1 in pollen development. In vivo and in vitro assays demonstrated that bHLH142 and TDR1 proteins interact. Transient promoter assays demonstrated that regulation of the EAT1 promoter requires bHLH142 and TDR1. Consistent with these results, 3D protein structure modeling predicted that bHLH142 and TDR1 form a heterodimer to bind to the EAT1 promoter. EAT1 positively regulates the expression of AP37 and AP25, which induce tapetal programmed cell death. Thus, in this study, we identified bHLH142 as having a pivotal role in tapetal programmed cell death and pollen development.

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Section: Prediction Of 3d Protein Structure and Interaction By Molecumentioning

confidence: 99%

The bHLH142 Transcription Factor Coordinates with TDR1 to Modulate the Expression of EAT1 and Regulate Pollen Development in Rice

Li²,

et al. 2014

The Plant Cell

139

199

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“…The SEC Method in the CASP8 Experiment (FAMSD_QA) In the Seventh CASP (CASP7), 12) a new prediction category called Quality Assessment (QA) was implemented. 20) This prediction category was introduced to develop a model quality estimation method without information of the experimental structure of target protein. In this category, predictors estimate the quality of the 3D models which were automatically predicted within 3 d after the receipt of the target sequence by server teams.…”

Section: Methodsmentioning

confidence: 99%

“…After the prediction period expires, the CASP assessors of the QA category computed the correlation between the observed quality in comparison with the 3D coordinates of the target protein and predicted quality of the models achieved by the participating teams. 20) We participated in the latest CASP experiment (CASP8) 13) in 2008 using the SEC method as a QA predictor called 'FAMSD_QA.' 21) The team name was 'FAMSD'; however, the 'FAMSD' team was also a tertiary structure (TS) predictor, and therefore the inclusion of the '_QA' in the name of FAMSD avoids confusion.…”

Section: Methodsmentioning

confidence: 99%

New Protein Structure Model Evaluation Methods That Include a Side-Chain Consensus Score for the Protein Modeling

Kanou

Hirata

Terashi

et al. 2010

CHEMICAL & PHARMACEUTICAL BULLETIN

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A large number of genes from many genomes have been determined. Translation of the genes to amino acid sequences and the amino acid analysis of proteins have provided a wealth of protein sequence information. In order to elucidate the biological function of proteins, the three-dimensional (3D) structures of the proteins are essential. The determination of the 3D structure of proteins has been performed with the experimental methods such as X-ray diffraction and nuclear magnetic resonance (NMR) spectroscopy. However, the number of protein structures determined using the experimental methods lags significantly behind the number of protein sequences. Therefore, computational approaches such as comparative or homology modeling for accurate protein structure prediction are urgently required.Consensus methods have been used in the 3D modeling of protein structures.1-3) For example, the reliability of the predicted 3D model is determined by rankings based on factors such as the similarity between Ca atoms of two protein backbones in the compared models. This representative consensus method is very useful if there are numerous accessible modeling or alignment servers. Here, an amino acid sequence of a target protein is used in the analysis of other excellent servers or websites and the output gives the 3D model or the sequence alignment between the target protein and a template protein 3D structure that has been determined experimentally. As shown in the Results and Discussion, we can submit an amino acid sequence under investigation to several web servers and obtain the 3D models or the sequence alignments. Thereupon, the consensus method is an available method, in spite of the complicated way in which we must collect 3D models and alignments created by each web server. Consequently, improving the consensus method is very important in creating a more accurate model based on many 3D models and alignments. In this paper, we have improved the consensus method in taking notice of the environment similarity of side-chains. We explain the algorithm of the new consensus method mentioned above and, actually, use our modeling method employing several server models and sequence alignments collected. As an example of a modeling target, we chose the human Cabin1 protein, a molecule involved in p53 function, which is a very important protein target involved in apoptosis and is combating with cancer in tissues.4-6) Our 3D model for the N-terminal region consisting of 450 residues of human Cabin1 may be useful in the pharmaceutical, medicinal and biological fields.In the meanwhile, 3D-Jury 1) method which is the representative consensus method was one of the most powerful methods to obtain a model with accurate Ca backbone atoms. In the 3D-Jury 1) method each amino acid is represented only by the Ca atom. As such, this method selects "good backbone" protein models that closely match with the experimental structure of the target protein. However, the quality of the side-chains of the selected models is not ensured by this method, beca...

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“…On the other hand, a number of methods for model quality assessment have been based on statistical evaluation of coarse-grained features that allow for discrimination of models that are outside the global energy minimum (i.e., almost all models produced by bioinformatic approaches such as comparative modeling or de novo folding). A number of model quality assessment programs (MQAPs) have been recently developed and rigorously tested (reviews: [26,29]). Here, we used our own "metaserver" MetaMQAP that obtains scores from a number of third-party MQAPs and uses a regression model to calculate a predicted deviation between the position of each residue in the model and its (unknown) position in the real structure [22].…”

Section: Model Quality Assessmentmentioning

confidence: 99%

Modeling of Escherichia coli Endonuclease V structure in complex with DNA

Majorek

Bujnicki

2008

J Mol Model

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Endonuclease V (EndoV) is a metal-dependent DNA repair enzyme involved in removal of deaminated bases (e.g., deoxyuridine, deoxyinosine, and deoxyxanthosine), with pairing specificities different from the original bases. Homologs of EndoV are present in all major phyla from bacteria to humans and their function is quite well analyzed. EndoV has been combined with DNA ligase to develop an enzymatic method for mutation scanning and has been engineered to obtain variants with different substrate specificities that serve as improved tools in mutation recognition and cancer mutation scanning. However, little is known about the structure and mechanism of substrate DNA binding by EndoV. Here, we present the results of a bioinformatic analysis and a structural model of EndoV from Escherichia coli in complex with DNA. The structure was obtained by a combination of fold-recognition, comparative modeling, de novo modeling and docking methods. The modeled structure provides a convenient tool to study protein sequence-structure-function relationships in EndoV and to engineer its further variants.

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Assessment of predictions in the model quality assessment category

Cited by 122 publications

References 23 publications

The bHLH142 Transcription Factor Coordinates with TDR1 to Modulate the Expression of EAT1 and Regulate Pollen Development in Rice

The bHLH142 Transcription Factor Coordinates with TDR1 to Modulate the Expression of EAT1 and Regulate Pollen Development in Rice

New Protein Structure Model Evaluation Methods That Include a Side-Chain Consensus Score for the Protein Modeling

Modeling of Escherichia coli Endonuclease V structure in complex with DNA

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