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Titolo:
A fragment library based on gaussian mixtures predicting favorable molecular interactions
Autore:
Rantanen, VV; Denessiouk, KA; Gyllenberg, M; Koski, T; Johnson, MS;
Indirizzi:
Univ Turku, Dept Math, FIN-20014 Turku, Finland Univ Turku Turku FinlandFIN-20014 , Dept Math, FIN-20014 Turku, Finland Abo Akad Univ, Dept Biochem & Pharm, FIN-20521 Turku, Finland Abo Akad Univ Turku Finland FIN-20521 & Pharm, FIN-20521 Turku, Finland Linkoping Univ, Dept Math, S-58183 Linkoping, Sweden Linkoping Univ Linkoping Sweden S-58183 Math, S-58183 Linkoping, Sweden
Titolo Testata:
JOURNAL OF MOLECULAR BIOLOGY
fascicolo: 1, volume: 313, anno: 2001,
pagine: 197 - 214
SICI:
0022-2836(20011012)313:1<197:AFLBOG>2.0.ZU;2-U
Fonte:
ISI
Lingua:
ENG
Soggetto:
PROTEIN-LIGAND INTERACTIONS; HYDROGEN-BONDING REGIONS; DIRECTED DRUG DESIGN; BINDING-SITES; STOCHASTIC COMPLEXITY; SCORING FUNCTION; PROBE GROUPS; LUDI; INFORMATION; POSITIONS;
Keywords:
protein-ligand recognition; prior and conditional probabilities; Bayes' theorem; Gaussian mixture model; expectation-maximization algorithm;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
42
Recensione:
Indirizzi per estratti:
Indirizzo: Rantanen, VV Univ Turku, Dept Math, FIN-20014 Turku, Finland Univ Turku Turku Finland FIN-20014 FIN-20014 Turku, Finland
Citazione:
V.V. Rantanen et al., "A fragment library based on gaussian mixtures predicting favorable molecular interactions", J MOL BIOL, 313(1), 2001, pp. 197-214

Abstract

Here, a protein atom-ligand fragment interaction library is described. Thelibrary is based on experimentally solved structures of protein-ligand andprotein-protein complexes deposited in the Protein Data Bank (PDB) and it is able to characterize binding sites given a ligand structure suitable fora protein. A set of 30 ligand fragment types were defined to include threeor more atoms in order to unambiguously define a frame of referencefor interactions of ligand atoms with their receptor proteins. Interactions between ligand fragments and 24 classes of protein target atoms plus a water oxygen atom were collected and segregated according to type. The spatial distributions of individual fragment - target atom pairs were visually inspected in order to obtain rough-grained constraints on the interaction volumes. Data fulfilling these constraints were given as input to an iterative expectation-maximization algorithm that produces as output maximum likelihood estimates of the parameters of the finite Gaussian mixture models. Concepts of statistical pattern recognition and the resulting mixture model densities are used (i) to predict the detailed interactions between Chlorella virus DNA ligase and the adenine ring of its ligand and (ii) to evaluate the "error" in prediction for both the training and validation sets of protein-ligandinteraction found in the PDB. These analyses demonstrate that this approach can successfully narrow down the possibilities for both the interacting protein atom type and its location relative to a ligand fragment. (C) 2001 Academic Press.

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Documento generato il 09/04/20 alle ore 06:42:49