PDBsum entry 1zbj

Transferase

PDB id: 1zbj

Contents

Protein chain

59 a.a. *

* Residue conservation analysis

PDB id:

1zbj

Name:

Transferase

Title:

Inferential structure determination of the fyn sh3 domain using noesy data from a 15n,h2 enriched protein

Structure:

Proto-oncogene tyrosine-protein kinase fyn. Chain: a. Fragment: sh3 domain, residues 83-141. Synonym: p59-fyn, syn, slk. Engineered: yes

Source:

Homo sapiens. Human. Organism_taxid: 9606. Gene: fyn. Expressed in: escherichia coli. Expression_system_taxid: 562

NMR struc:

20 models

Authors:

W.Rieping,M.Habeck,M.Nilges

Key ref:

W.Rieping et al. (2005). Inferential structure determination. Science, 309, 303-306. PubMed id: 16002620 DOI: 10.1126/science.1110428

Date:

08-Apr-05 Release date: 03-May-05

Protein chain

P06241  (FYN_HUMAN) -  Tyrosine-protein kinase Fyn from Homo sapiens

Seq: 537 a.a.

Struc: 59 a.a.

Enzyme reactions

• Enzyme class: E.C.2.7.10.2 - non-specific protein-tyrosine kinase.
• Reaction: L-tyrosyl-[protein] + ATP = O-phospho-L-tyrosyl-[protein] + ADP + H⁺

Molecule diagrams generated from .mol files obtained from the KEGG ftp site

Added reference

DOI no: 10.1126/science.1110428

Science 309:303-306 (2005)

PubMed id: 16002620

Inferential structure determination.

W.Rieping, M.Habeck, M.Nilges.

ABSTRACT

Macromolecular structures calculated from nuclear magnetic resonance data are not fully determined by experimental data but depend on subjective choices in data treatment and parameter settings. This makes it difficult to objectively judge the precision of the structures. We used Bayesian inference to derive a probability distribution that represents the unknown structure and its precision. This probability distribution also determines additional unknowns, such as theory parameters, that previously had to be chosen empirically. We implemented this approach by using Markov chain Monte Carlo techniques. Our method provides an objective figure of merit and improves structural quality.

Selected figure(s)

Figure 1.
Fig. 1. Replica-exchange Monte Carlo algorithm. (A) We generate a stochastic sample (X(k), (k), (k)) from the joint posterior distribution in an iterative fashion by using Gibbs sampling (20). The nuisance parameters and are consecutively drawn from their conditional posterior distributions, with the values of the other parameters being fixed to their previously generated values. Coordinates are updated by using the hybrid Monte Carlo method (21). (B) To overcome energy barriers, we embed this scheme in a replica-exchange strategy, which simulates a sequence of heated copies of the system. Samples of the target distribution are generated in the low-temperature copy (T[low]) and propagate via stochastic exchanges between intermediate copies (T[low] < T[i] < T[high]) to the high-temperature system (T[high]). The temperature T[high] is chosen such that the polypeptide chain can move freely in order to escape local modes of the probability density.

Figure 3.
Fig. 3. Estimation of nuisance parameters. Posterior histograms compiled from MCMC samples for the scaling factor in the ISPA and for the width of the log normal error distribution. (A) Posterior histogram p( - |D,I) for the inverse sixth power of . This factor corrects interproton distances to match the experimental distances best. (B) Posterior histogram p( |D,I) for the error . In conventional approaches, this analog to the weight (w[data] -2) can only be estimated via cross-validation or must be set empirically.

The above figures are reprinted by permission from the AAAs: Science (2005, 309, 303-306) copyright 2005.

Figures were selected by an automated process.