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S Fortier

Publications and source records attributed to S Fortier.

At least 37 records · Page 2Linked to original sources
PubMed1996

Effect of thyroid hormones on G proteins in synaptosomes of chick embryo.

We have described a thyroid hormone receptor in synaptosomes of the chick embryo brain. To understand how the hormones exert their actions at this level, we performed a series of studies to demonstrate that this receptor could be linked to G proteins. Guanosine 5'-[gamma-thio]triphosphate (GTP gamma S)(100 muM) lowered the binding capacity of the receptor high affinity site from 8.9 +/- 1.3 to 3.4 +/- 1.3 ng T3/mg protein, a finding consistent with the coupling of receptor to G proteins. Furthermore, ADP ribosylation with pertussis toxin showed that thyroid hormones induced a dose-dependent increase in the inactive alpha 0-subunit of the G0 protein. This effect was detected at 10 pM, with a maximal increase (mean +/- SEM, 50 +/- 3.6%) at 100 nM, and T4 was as effective as T3. Both hormones also decreased the intrinsic guanine triphosphatase activity of G proteins by lowering the binding of GTP to the alpha-subunit and their rate of hydrolysis. This inhibition was greater with T4 (25 +/- 5%) than with T3 (14 +/- 2%), suggesting that the former could be the more active hormone at the synaptosomal level. The effect on guanine triphosphatase activity confirms that the synaptosomal thyroid hormone receptor is coupled to a G(zero) protein. These results demonstrate that thyroid hormones increase or favor the ADP ribosylation of G alpha(zero) by pertussis toxin. Thus, they enhance the alpha(zero)-GDP form of the G(zero) protein, namely its inactive conformation. By decreasing the activity of this protein, these hormones may modulate the formation of second messengers in synaptosomes and intervene in the regulation of neuronal proliferation and differentiation induced by several factors. Therefore, thyroid hormones may exert their action on brain maturation at least in part by modulating G alpha(zero) through their synaptosomal receptor.

Adenosine Diphosphate Ribose↗
PubMed1996

From electron density and sequence to structure: integrating protein image analysis and threading for structure determination.

This paper presents a computational methodology for integrating techniques from protein image interpretation and protein sequence threading, applied to the problem of structure determination from experimental X-ray crystallographic electron density maps. In the proposed architecture, image interpretation of an electron density map produces candidate structural segments; threading is applied to evaluate these hypothesized segments and thus to constrain the set of possible image interpretations. We present the results of experiments designed to test ability of the threading module to discriminate between correct and incorrect alignments of protein sequences onto structural models derived from protein image interpretation. The long-term goal of this research is to improve our ability to determine protein structures from crystallographic data, and to further our understanding of the underlying relationship between sequence and structure.

Algorithms↗
PubMed1994

Molecular scene analysis: application of a topological approach to the automated interpretation of protein electron-density maps.

Methods to assist in the spatial and visual analysis of electron-density maps have been investigated as part of a project in molecular scene analysis [Fortier, Castleden, Glasgow, Conklin, Walmsley, Leherte & Allen (1993). Acta Cryst. D49, 168-178]. In particular, the usefulness of the topological approach for the segmentation of medium-resolution (3 A) maps of proteins and their interpretation in terms of structural motifs has been assessed. The approach followed is that proposed by Johnson [Johnson (1977). ORCRIT. The Oak Ridge Critical Point Network Program. Chemistry Division, Oak Ridge National Laboratory, USA] which provides a global representation of the electron-density distribution through the location, identification and linkage of its critical points. In the first part of the study, the topological approach was applied to calculated maps of three proteins of small to medium size so as to develop a methodology that could then be used for analyzing maps of medium resolution. The methodology was then applied to both calculated and experimental maps of penicillopepsin at 3 A resolution. The study shows that the networks of critical points can provide a useful segmentation of the maps, tracing the protein main chains and capturing their conformation. In addition, these networks can be parsed in terms of secondary-structure motifs, through a geometrical analysis of the critical points. The procedure adopted for secondary-structure recognition, which was phrased in terms of geometry-based rules, provides a basis for a further automated implementation of a more complete set of recognition operations through the use of artificial-intelligence techniques.

Journal Article↗
PubMed1994

Segmentation and interpretation of 3D protein images.

The segmentation and interpretation of three-dimensional images of proteins is considered. A topological approach is used to represent a protein structure as a spanning tree of critical points, where each critical point corresponds to a residue or the connectivity between residues. The critical points are subsequently analyzed to recognize secondary structure motifs within the protein. Results of applying the approach to ideal and experimental images of proteins at medium resolution are presented.

Computer Simulation↗
PubMed1994

Knowledge discovery of multilevel protein motifs.

A new category of protein motif is introduced. This type of motif captures, in addition to global structure, the nested structure of its component parts. A dataset of four proteins is represented using this scheme. A structured machine discovery procedure is used to discover recurrent amino acid motifs and this knowledge is utilized for the expression of subsequent protein motif discoveries. Examples of discovered multilevel motifs are presented.

Animals↗
PubMed1993

Molecular scene analysis: the integration of direct-methods and artificial-intelligence strategies for solving protein crystal structure.

A knowledge-based approach to crystal structure determination is presented. The approach integrates direct-methods and artificial-intelligence strategies to rephrase the structure determination process as an exercise in scene analysis. A general joint probability distribution framework, which allows the incorporation of isomorphous replacement, anomalous scattering and a priori structural information, forms the basis of the direct-methods strategies. The accumulated knowledge on crystal and molecular structures is exploited through the use of artificial-intelligence strategies, which include techniques of knowledge representation, search and machine learning.

Journal Article↗
PubMed1993

Representation for discovery of protein motifs.

There are several dimensions and levels of complexity in which information on protein motifs may be available. For example, one-dimensional sequence motifs may be associated with secondary structure identifiers. Alternatively, three-dimensional information on polypeptide segments may be used to induce prototypical three-dimensional structure templates. This paper surveys various representations encountered in the protein motif discovery literature. Many of the representations are based on incompatible semantics, making difficult the comparison and combination of previous results. To make better use of machine learning techniques and to provide for an integrated knowledge representation framework, a general representation language--in which all types of motifs can be encoded and given a uniform semantics--is required. In this paper we propose such a model, called a spatial description logic, and present a machine learning approach based on the model.

Amino Acids↗
PubMed1989

On the probabilistic theory of isomorphous data sets: general joint distributions for the SIR, SAS and partial/complete structure cases.

By characterizing isomorphism in reciprocal space [i.e. diffraction data sets are isomorphous if they have the same geometry (the same reciprocal-lattice unit cell) and the same symmetry] it is shown that the diffraction data of a native protein and of its heavy-atom derivatives, the calculated data of a partial structure and the observed data of its associated complete structure, and the Friedel-pair data of an anomalously scattering crystal structure all belong to the more general class of isomorphous data sets. Their joint probability distributions for two- and three-phase structure invariants are shown to be isomorphous: they have the same functional form and differ only in individual atomic scattering factors. General joint probability distributions, which can be used for any isomorphous data pairs, are presented.

Crystallography↗