MaxSub

MaxSub: an automated measure for the assessment of protein structure prediction quality. Results: MaxSub is a new and independently developed method that further builds and extends some of the evaluation methods introduced at CASP3. MaxSub aims at identifying the largest subset of Ca atoms of a model that superimpose ‘well’ over the experimental structure, and produces a single normalized score that represents the quality of the model. Because there exists no evaluation method for assessment measures of predicted models, it is not easy to evaluate how good our new measure is. Even though an exact comparison of MaxSub and the CASP3 assessment is not straightforward, here we use a test-bed extracted from the CASP3 fold-recognition models. A rough qualitative comparison of the performance of MaxSub vis-a-vis the human-expert assessment carried out at CASP3 shows that there is a good agreement for the more accurate models and for the better predicting groups. As expected, some differences were observed among the medium to poor models and groups. Overall, the top six predicting groups ranked using the fully automated MaxSub are also the top six groups ranked at CASP3. We conclude that MaxSub is a suitable method for the automatic evaluation of models. Availability: MaxSub is available at: http://www.cs.bgu.ac.il/ dfischer/MaxSub/MaxSub.html

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References in zbMATH (referenced in 8 articles )

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  1. Fu, Bin; Wang, Lusheng: Constant time approximation scheme for largest well predicted subset (2013)
  2. Di Luccio, Eric; Koehl, Patrice: A quality metric for homology modeling: the H-factor (2011) ioport
  3. Capriotti, Emidio; Martí-Renom, Marc A.: Quantifying the relationship between sequence and three-dimensional structure conservation in RNA (2010) ioport
  4. Jeong, Chan-Seok; Kim, Dongsup: Linear predictive coding representation of correlated mutation for protein sequence alignment (2010) ioport
  5. Margelevicius, Mindaugas; Venclovas, Ceslovas: Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison (2010) ioport
  6. Montuori, Alfonso; Raimondo, Giovanni; Pasero, Eros: An information theoretic approach for improving data driven prediction of protein model quality (2008)
  7. Kaján, László; Rychlewski, Leszek: Evaluation of 3D-jury on CASP7 models (2007) ioport
  8. Mcguffin, Liam J.: Benchmarking consensus model quality assessment for protein fold recognition (2007) ioport