@article {3,
	title = {A novel method to compare protein structures using local descriptors.},
	journal = {BMC bioinformatics},
	volume = {12},
	year = {2011},
	month = {08/2011},
	pages = {344},
	abstract = {BACKGROUND: Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships.

RESULTS: We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method{\textquoteright}s capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77\% (the second best method tested achieves 60\% accuracy).

CONCLUSIONS: DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at http://bioexploratorium.pl/EP/DEDAL.},
	keywords = {Algorithms, Animals, Bacterial Proteins, Carrier Proteins, Computational Biology, GTP Phosphohydrolases, Humans, Models, Molecular, Proteins, Saposins, Structural Homology, Protein},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-12-344},
	author = {Daniluk, Pawe{\l} and Lesyng, Bogdan}
}
@article {5,
	title = {Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space.},
	journal = {Journal of chemical information and modeling},
	volume = {48},
	year = {2008},
	month = {2008 Nov},
	pages = {2278-88},
	abstract = {Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein-ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (p K i) ranged from 0.5 to 11.9 (0.7-11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r (2) of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.},
	keywords = {Animals, Artificial Intelligence, Cluster Analysis, Computer Simulation, Databases, Protein, Drug Discovery, Enzymes, Informatics, Kinetics, Ligands, Models, Molecular, Molecular Structure, Oxidoreductases Acting on CH-CH Group Donors, Oxidoreductases Acting on CH-NH Group Donors, Plasmodium falciparum, Protein Conformation, Zea mays},
	issn = {1549-9596},
	doi = {10.1021/ci800200e},
	author = {Str{\"o}mbergsson, Helena and Daniluk, Pawe{\l} and Kryshtafovych, Andriy and Fidelis, Krzysztof and Wikberg, Jarl E S and Kleywegt, Gerard J and Hvidsten, Torgeir R}
}
