Dataset Information

CHOP: parsing proteins into structural domains.

ABSTRACT: Sequence-based domain assignment is one of the most important and challenging problems in structural biology. We have developed a method, CHOP, that chops proteins into domain-like fragments. The basic idea is to cut proteins from entirely sequenced organisms beginning from very reliable experimental information (Protein Data Bank), proceeding to expert annotations of domain-like regions (Pfam-A) and completing through cuts based on termini of native protein ends. The CHOP server takes protein sequences as input and returns the dissections supported by homology transfer. CHOP results are precompiled for many entirely sequenced proteomes. The service is available at http://www.rostlab.org/services/CHOP/.

SUBMITTER: Liu J

PROVIDER: S-EPMC441619 | biostudies-literature | 2004 Jul

REPOSITORIES: biostudies-literature

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CHOP: parsing proteins into structural domains.

Liu Jinfeng J Rost Burkhard B

Nucleic acids research 20040701 Web Server issue

Sequence-based domain assignment is one of the most important and challenging problems in structural biology. We have developed a method, CHOP, that chops proteins into domain-like fragments. The basic idea is to cut proteins from entirely sequenced organisms beginning from very reliable experimental information (Protein Data Bank), proceeding to expert annotations of domain-like regions (Pfam-A) and completing through cuts based on termini of native protein ends. The CHOP server takes protein s ...[more]

PMID: 15215452

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Project description:Proteins of the tetraspanin family contain four transmembrane domains (TM1-4) linked by two extracellular loops and a short intracellular loop, and have short intracellular N- and C-termini. While structure and function analysis of the larger extracellular loop has been performed, the organization and role of transmembrane domains have not been systematically assessed.Among 28 human tetraspanin proteins, the TM1-3 sequences display a distinct heptad repeat motif (abcdefg)n. In TM1, position a is occupied by structurally conserved bulky residues and position d contains highly conserved Asn and Gly residues. In TM2, position a is occupied by conserved small residues (Gly/Ala/Thr), and position d has a conserved Gly and two bulky aliphatic residues. In TM3, three a positions of the heptad repeat are filled by two leucines and a glutamate/glutamine residue, and two d positions are occupied by either Phe/Tyr or Val/Ile/Leu residues. No heptad motif is apparent in TM4 sequences. Mutations of conserved glycines in human CD9 (Gly25 and Gly32 in TM1; Gly67 and Gly74 in TM2) caused aggregation of mutant proteins inside the cell. Modeling of the TM1-TM2 interface in CD9, using a novel algorithm, predicts tight packing of conserved bulky residues against conserved Gly residues along the two helices. The homodimeric interface of CD9 was mapped, by disulfide cross-linking of single-cysteine mutants, to the vicinity of residues Leu14 and Phe17 in TM1 (positions g and c) and Gly77, Gly80 and Ala81 in TM2 (positions d, g and a, respectively). Mutations of a and d residues in both TM1 and TM2 (Gly25, Gly32, Gly67 and Gly74), involved in intramolecular TM1-TM2 interaction, also strongly diminished intermolecular interaction, as assessed by cross-linking of Cys80.Our results suggest that tetraspanin intra- and intermolecular interactions are mediated by conserved residues in adjacent, but distinct regions of TM1 and TM2. A key structural element that defines TM1-TM2 interaction in tetraspanins is the specific packing of bulky residues against small residues.

Project description:BACKGROUND: Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure. RESULTS: We present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy. CONCLUSION: Our graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.

Dataset Information

CHOP: parsing proteins into structural domains.

Publications

CHOP: parsing proteins into structural domains.

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