Catalogo Articoli (Spogli Riviste)
OPAC HELP
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, FIN20014 Turku, Finland Univ Turku Turku FinlandFIN20014 , Dept Math, FIN20014 Turku, Finland Abo Akad Univ, Dept Biochem & Pharm, FIN20521 Turku, Finland Abo Akad Univ Turku Finland FIN20521 & Pharm, FIN20521 Turku, Finland Linkoping Univ, Dept Math, S58183 Linkoping, Sweden Linkoping Univ Linkoping Sweden S58183 Math, S58183 Linkoping, Sweden
 Titolo Testata:
 JOURNAL OF MOLECULAR BIOLOGY
fascicolo: 1,
volume: 313,
anno: 2001,
pagine: 197  214
 SICI:
 00222836(20011012)313:1<197:AFLBOG>2.0.ZU;2U
 Fonte:
 ISI
 Lingua:
 ENG
 Soggetto:
 PROTEINLIGAND INTERACTIONS; HYDROGENBONDING REGIONS; DIRECTED DRUG DESIGN; BINDINGSITES; STOCHASTIC COMPLEXITY; SCORING FUNCTION; PROBE GROUPS; LUDI; INFORMATION; POSITIONS;
 Keywords:
 proteinligand recognition; prior and conditional probabilities; Bayes' theorem; Gaussian mixture model; expectationmaximization algorithm;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 Life Sciences
 Citazioni:
 42
 Recensione:
 Indirizzi per estratti:
 Indirizzo: Rantanen, VV Univ Turku, Dept Math, FIN20014 Turku, Finland Univ Turku Turku Finland FIN20014 FIN20014 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. 197214
Abstract
Here, a protein atomligand fragment interaction library is described. Thelibrary is based on experimentally solved structures of proteinligand andproteinprotein 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 roughgrained constraints on the interaction volumes. Data fulfilling these constraints were given as input to an iterative expectationmaximization 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 proteinligandinteraction 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.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 09/04/20 alle ore 06:42:49