@article {18397517,
	title = {Evolutionary potentials: structure specific knowledge-based potentials exploiting the evolutionary record of sequence homologs},
	journal = {Genome Biol},
	volume = {9},
	number = {4},
	year = {2008},
	note = {Journal article Genome biology Genome Biol. 2008 Apr 8;9(4):R68.},
	pages = {R68},
	abstract = {ABSTRACT: We introduce a new type of knowledge-based potentials for protein structure prediction, called {\textquoteright}evolutionary potentials{\textquoteright}, which are derived using a single experimental protein structure and all three-dimensional models of its homologous sequences. The new potentials have been benchmarked against other knowledge-based potentials, resulting in a significant increase in accuracy for model assessment. In contrast to standard knowledge-based potentials, we propose that evolutionary potentials capture key determinants of thermodynamic stability and specific sequence constraints required for fast folding.},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18397517},
	author = {Panjkovich, A. and Melo, F. and M. A. Marti-Renom}
}
@article {16506243,
	title = {Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets},
	journal = {Proteins},
	volume = {63},
	number = {4},
	year = {2006},
	note = {Melo, Francisco Marti-Renom, Marc A Research Support, Non-U.S. Gov{\textquoteright}t United States Proteins Proteins. 2006 Jun 1;63(4):986-95.},
	pages = {986-95},
	abstract = {Reduced or simplified amino acid alphabets group the 20 naturally occurring amino acids into a smaller number of representative protein residues. To date, several reduced amino acid alphabets have been proposed, which have been derived and optimized by a variety of methods. The resulting reduced amino acid alphabets have been applied to pattern recognition, generation of consensus sequences from multiple alignments, protein folding, and protein structure prediction. In this work, amino acid substitution matrices and statistical potentials were derived based on several reduced amino acid alphabets and their performance assessed in a large benchmark for the tasks of sequence alignment and fold assessment of protein structure models, using as a reference frame the standard alphabet of 20 amino acids. The results showed that a large reduction in the total number of residue types does not necessarily translate into a significant loss of discriminative power for sequence alignment and fold assessment. Therefore, some definitions of a few residue types are able to encode most of the relevant sequence/structure information that is present in the 20 standard amino acids. Based on these results, we suggest that the use of reduced amino acid alphabets may allow to increasing the accuracy of current substitution matrices and statistical potentials for the prediction of protein structure of remote homologs.},
	keywords = {Amino Acid Sequence Amino Acids/*chemistry/classification/*metabolism Consensus Sequence Molecular Sequence Data Oxidation-Reduction *Protein Folding Proteins/*chemistry/*metabolism Sequence Alignment/*methods Structural Homology, Protein},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16506243},
	author = {Melo, F. and M. A. Marti-Renom}
}
@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}
}
@article {16381869,
	title = {MODBASE: a database of annotated comparative protein structure models and associated resources},
	journal = {Nucleic Acids Res},
	volume = {34},
	number = {Database issue},
	year = {2006},
	note = {Pieper, Ursula Eswar, Narayanan Davis, Fred P Braberg, Hannes Madhusudhan, M S Rossi, Andrea Marti-Renom, Marc Karchin, Rachel Webb, Ben M Eramian, David Shen, Min-Yi Kelly, Libusha Melo, Francisco Sali, Andrej GM 08284/GM/NIGMS NIH HHS/United States P50 GM62529/GM/NIGMS NIH HHS/United States R01 GM 54762/GM/NIGMS NIH HHS/United States R33 CA84699/CA/NCI NIH HHS/United States U54 GM074945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t England Nucleic acids research Nucleic Acids Res. 2006 Jan 1;34(Database issue):D291-5.},
	pages = {D291-5},
	abstract = {MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP).},
	keywords = {Binding Sites *Databases, Molecular Polymorphism, Protein Humans Internet Ligands *Models, Protein Systems Integration User-Computer Interface, Single Nucleotide Protein Structure, Tertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=16381869},
	author = {Pieper, U. and Eswar, N. and Davis, F. P. and Braberg, H. and Madhusudhan, M. S. and Rossi, A. and M. A. Marti-Renom and Karchin, R. and Webb, B. M. and Eramian, D. and Shen, M. Y. and Kelly, L. and Melo, F. and Sali, A.}
}
