@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}
}
@article {52,
	title = {Two novel presenilin 1 gene mutations connected with frontotemporal dementia-like clinical phenotype: genetic and bioinformatic assessment.},
	journal = {Experimental neurology},
	volume = {200},
	year = {2006},
	month = {2006 Jul},
	pages = {82-8},
	abstract = {Mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes are associated with early-onset familial Alzheimer{\textquoteright}s disease (EOAD). There are several reports describing mutations in PSEN1 in cases with frontotemporal dementia (FTD). We identified two novel mutations in the PSEN1 gene: L226F and L424H. The first mutation was detected in a patient with a clinical diagnosis of FTD and a post-mortem diagnosis of AD. The second mutation is connected with a clinical phenotype of variant AD with strong FTD signs. In silico modeling revealed that the mutations, as well as mutations used for comparison (F177L and L424R), change the local structure, stability and/or properties of the transmembrane regions of the presenilin 1 protein (PS1). In contrast, a silent non-synonymous substitution F175S is eclipsed by external residues and has no influence on PS1 interfacial surface. We suggest that in silico analysis of PS1 substitutions can be used to characterize novel PSEN1 mutations, to discriminate between silent polymorphisms and a potential disease-causing mutation. We also propose that PSEN1 mutations should be considered in FTD patients with no MAPT mutations.},
	keywords = {Adult, Computational Biology, Dementia, Diagnosis, Differential, Female, Genetic Testing, Humans, Male, Membrane Proteins, Models, Molecular, Mutation, Phenotype, Presenilin-1},
	issn = {0014-4886},
	doi = {10.1016/j.expneurol.2006.01.022},
	author = {Zekanowski, Cezary and Golan, Maciej P and Krzy{\'s}ko, Krystiana A and Lipczy{\'n}ska-{\L}ojkowska, Wanda and Filipek, S{\l}awomir and Kowalska, Anna and Rossa, Grzegorz and Pep{\l}o{\'n}ska, Beata and Styczy{\'n}ska, Maria and Maruszak, Aleksandra and Religa, Dorota and Wender, Mieczys{\l}aw and Kulczycki, Jerzy and Barcikowska, Maria and Ku{\'z}nicki, Jacek}
}
