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T Madej

Publications and source records attributed to T Madej.

9 recordsLinked to original sources
PubMed2000

MMDB: 3D structure data in Entrez.

Three-dimensional structures are now known for roughly half of all protein families. It is thus quite likely, in searching sequence databases, that one will encounter a homolog with known structure and be able to use this information to infer structure-function properties. The goal of Entrez's 3D structure database is to make this information accessible and useful to molecular biologists. To this end, Entrez's search engine provides three powerful features: (i) Links between databases; one may search by term matching in Medline((R)), for example, and link to 3D structures reported in these articles. (ii) Sequence and structure neighbors; one may select all sequences similar to one of interest, for example, and link to any known 3D structures. (iii) Sequence and structure visualization; identifying a homolog with known structure, one may view a combined molecular-graphic and alignment display, to infer approximate 3D structure. Entrez's MMDB (Molecular Modeling DataBase) may be accessed at: http://www.ncbi.nlm.nih.gov/Entrez/structure.html

Amino Acid Sequence↗
PubMed1999

MMDB: Entrez's 3D structure database.

The three dimensional structures for representatives of nearly half of all protein families are now available in public databases. Thus, no matter which protein one investigates, it is increasingly likely that the 3D structure of a homolog will be known and may reveal unsuspected structure-function relationships. The goal of Entrez's 3D-structure database is to make this information accessible and usable by molecular biologists (http://www.ncbi.nlm.nih.gov/Entrez). To this end Entrez provides two major analysis tools, a search engine based on sequence and structure 'neighboring' and an integrated visualization system for sequence and structure alignments. From a protein's sequence 'neighbors' one may rapidly identify other members of a protein family, including those where 3D structure is known. By comparing aligned sequences and/or structures in detail, using the visualization system, one may identify conserved features and perhaps infer functional properties. Here we describe how these analysis tools may be used to investigate the structure and function of newly discovered proteins, using the PTEN gene product as an example.

Amino Acid Sequence↗
PubMed1997

Accidental oral poisoning with carbamate in a 26-year-old patient.

Acute, accidental, oral poisoning with carbofuran in a chronic alcoholic was presented. In clinical picture muscarinic and nicotinic symptoms dominated. Toxicological examination revealed non acetylcholinesterase activity. A role of an interview and physical symptoms in diagnosing the poisoning with cholinesterase inhibitors, a specific treatment conduct and its side effects were discussed. So far six cases of poisoning with cholinesterase inhibitors have been treated at II Department of Internal Diseases and Acute Poisoning in Tarnów. The case presented below was characteristic for a considerable intensification of side effects during the treatment conduct. It was the reason for presenting this case.

Adult↗
PubMed1996

Surprising similarities in structure comparison.

Examination of a protein's structural 'neighbors' can reveal distant evolutionary relationships that are otherwise undetectable, and perhaps suggest unsuspected functional properties. In the past, such analyses have often required specialized software and computer skills, but new structural comparison methods, developed in the past two years, increasingly offer this opportunity to structural and molecular biologists in general. These methods are based on similarity-search algorithms that are fast enough to have effectively removed the computer-time limitation for structure-structure search and alignment, and have made it possible for several groups to conduct systematic comparisons of all publicly available structures, and offer this information via the World Wide Web. Furthermore, and perhaps surprisingly given the difficulty of the structure-comparison problem, these groups seem to have converged on quite similar approaches with respect to both fast search algorithms and the identification of statistically significant similarities.

Computer Communication Networks↗
PubMed1995

Threading analysis suggests that the obese gene product may be a helical cytokine.

The ob gene encodes a protein that, in mutant form, is associated with obesity and type II diabetes in mice. Sequence analysis has revealed no similarities to other proteins, however, and no clues as to possible functions. The possibility nonetheless remains that ob is functionally or ancestrally related to other proteins, whose sequences are divergent to the point that only a comparison of three-dimensional structures might detect relationship. To explore this possibility, we conduct a 'threading' search of a 3-dimensional structure database, to determine whether the ob protein might adopt a fold similar to any known structure. This search reveals that the ob sequence is compatible, at a significance level of P < 0.05, with structures from the family of helical cytokines that includes interleukin-2 and growth hormone. A structural model of ob based upon these results is physically and biologically plausible and leads to testable predictions, including the prediction that ob may activate the JAK-STAT pathway, via binding to a receptor resembling those of the cytokine family.

Amino Acid Sequence↗
PubMed1995

Threading a database of protein cores.

We present an analysis of 10 blind predictions prepared for a recent conference, "Critical Assessment of Techniques for Protein Structure Prediction." The sequences of these proteins are not detectably similar to those of any protein in the structure database then available, but we attempted, by a threading method, to recognize similarity to known domain folds. Four of the 10 proteins, as we subsequently learned, do indeed show significant similarity to then-known structures. For 2 of these proteins the predictions were accurate, in the sense that a similar structure was at or near the top of the list of threading scores, and the threading alignment agreed well with the corresponding structural alignment. For the best predicted model mean alignment error relative to the optimal structural alignment was 2.7 residues, arising entirely from small "register shifts" of strands or helices. In the analysis we attempt to identify factors responsible for these successes and failures. Since our threading method does not use gap penalties, we may readily distinguish between errors arising from our prior definition of the "cores" of known structures and errors arising from inherent limitations in the threading potential. It would appear from the results that successful substructure recognition depends most critically on accurate definition of the "fold" of a database protein. This definition must correctly delineate substructures that are, and are not, likely to be conserved during protein evolution.

Amino Acid Sequence↗
PubMed1993

Hamiltonians for protein tertiary structure prediction based on three-dimensional environment principles.

We describe a computational approach to protein tertiary structure prediction that combines ideas from the three-dimensional (3D) profile method of Bowie, Lüthy and Eisenberg and the associative memory Hamiltonians of Friedrichs and Wolynes. The ultimate goal of our work is to extend and generalize the capabilities of these heuristics so as to be able to predict novel structures that might be found in nature or designed proteins. In our approach we approximate the interactions between residues through a pseudo-potential function similar to an associative memory Hamiltonian. This function is constructed based on 3D environment principles. Favorable inter-residue contacts for each residue in a target protein are inferred by using 3D environment propensities of the residues and a collection of 3D environment templates derived from a dataset of protein crystal structures. A Hamiltonian encoding this information is used to guide an optimization phase via molecular dynamics with annealing, which then leads to the folded structure. With our algorithm we can recover the structure of dataset proteins and have also succeeded in constructing the fold for a protein with little sequence similarity to any dataset protein.

Aprotinin↗