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A computer program using network flow algorithms for protein domain decomposition. DomainParser partitions a protein structure into domains. Through utilization of various types of structural information including hydrophobic moment profile, we have developed an effective method for assessing the most probable number of domains a structure may have. The core of this method is a neural network, which is trained to discriminate correctly partitioned domains from incorrectly partitioned domains. When compared with the manual decomposition results given in the SCOP database, DomainParser algorithm achieves higher decomposition accuracy (81.9%) on the same data set.
Assigns domain boundaries in a given structure using the superpositions stored in DBAli. ModDom is a web application relies on the relationship between recurrent structures and structural units to predict domain boundaries. The software consists of the following steps: (i) building of a residue co-occurrence matrix based on structural alignments selected from the DBAli database and (ii) clustering of residue co-occurrences to find common fragments in the query protein structure.
DHcL / Domain Hierarchy and closed Loops
Delineates energy hierarchy of protein domain structure. DHcL is a web server that detects domains at different levels of this hierarchy. It identifies closed loops locks which constitute a structural basis for the protein domain hierarchy. This application can be a useful tool for an express analysis of protein structures and their alternative domain decompositions. It maintains a regularly updated database of domains, closed loop and van der Waals locks for all X-ray structures.
AIDA / Ab Initio Domain Assembly
A fast docking algorithm for assembling multi-domain protein structures, guided by the ab initio folding potential. AIDA can be extended to discontinuous domains (i.e. domains with 'inserted' domains). This server also provides access to a recursive protocol, which combines template-based modeling with domain assembly in an iterative method suitable for automated domain assignment, modeling and assembly for a one-stop structure prediction of multi-domain proteins.
CA algorithm
A clustering-based approach to domain identification, which works equally well on individual chains or entire complexes. The method is simple and fast, taking only a few milliseconds to run, and works by clustering either vectors representing secondary structure elements, or buried alpha-carbon positions, using average-linkage clustering. Each resulting cluster corresponds to a domain of the structure. The method is competitive with others, achieving 70% agreement with SCOP on a large non-redundant data set, and 80% on a set more heavily weighted in multi-domain proteins on which both SCOP and CATH agree.
PAT / Predictor for Antibody Target
An integrated computational framework to predict optimal structural domains and identify target molecules for antibodies. PAT automatically analyses various structural properties, evaluates the folding stability, and identifies possible structured units in a given protein sequence. PAT is able to identify the traditional domains with strongly conserved stretches of protein sequence and putative structural units with parts of the protein that adopt stable folds.
Enables the accurate prediction of residue burial states, which should greatly facilitate protein structure prediction and evaluation. ACRF is a high-order conditional random field model that considers burial states of all residues in a protein simultaneously. It exploits not only the correlation among adjacent residues but also the correlation among long-range residues. ACRF also outperforms the logistic regression model, suggesting the importance of incorporating correlations into the prediction model.
PDP / Protein Domain Parser
A program for automatic identification of domains in protein three-dimensional structures. PDP performance was assessed by three different benchmarks: (i) by comparison with the expert-curated SCOP database of structural domains; (ii) by comparison with a collection of manual domain assignments; and (iii) by comparison with a set of 55 proteins, frequently used as a benchmark for automatic domain assignment. In all these benchmarks PDP identified domains correctly in more than 80% of proteins.
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