@article {18428767,
	title = {Comparative protein structure modeling using Modeller},
	journal = {Curr Protoc Bioinformatics},
	volume = {Chapter 5},
	year = {2006},
	note = {Eswar, Narayanan Webb, Ben Marti-Renom, Marc A Madhusudhan, M S Eramian, David Shen, Min-Yi Pieper, Ursula Sali, Andrej P01 A135707/PHS HHS/United States P01 GM71790/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States U54 GM62529/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t United States Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.] Curr Protoc Bioinformatics. 2006 Oct;Chapter 5:Unit 5.6.},
	pages = {Unit 5 6},
	abstract = {Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.},
	keywords = {Algorithms Amino Acid Sequence Computer Simulation Crystallography/*methods *Models, Chemical *Models, Molecular Molecular Sequence Data Protein Conformation Protein Folding Proteins/*chemistry/*ultrastructure Sequence Analysis, Protein/*methods *Software},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18428767},
	author = {Eswar, N. and Webb, B. and M. A. Marti-Renom and Madhusudhan, M. S. and Eramian, D. and Shen, M. Y. and Pieper, U. and Sali, A.}
}
@article {16751606,
	title = {A composite score for predicting errors in protein structure models},
	journal = {Protein Sci},
	volume = {15},
	number = {7},
	year = {2006},
	note = {Eramian, David Shen, Min-yi Devos, Damien Melo, Francisco Sali, Andrej Marti-Renom, Marc A GM 08284/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S. United States Protein science : a publication of the Protein Society Protein Sci. 2006 Jul;15(7):1653-66. Epub 2006 Jun 2.},
	pages = {1653-66},
	abstract = {Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30\% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.},
	keywords = {*Models, Molecular Models, Theoretical Proteins/*chemistry},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16751606},
	author = {Eramian, D. and Shen, M. Y. and Devos, D. and Melo, F. and Sali, A. and M. A. Marti-Renom}
}
