Structure comparison software tools | Protein data analysis
Quantitative comparison of two structures of the same biological macromolecule or complex is a very common but by no means trivial task. One such example is the comparison of a model with the experimentally determined (reference) structure.
Offers a framework for processing commonly used biological data. BioJava consists of several independent modules built using Maven. The software contains state-of-the-art algorithms, several file parsers, and data models to perform various calculations and to facilitate working with the standard data formats. It enables application development and analysis.
Allows users to study the proteomic scale inference of enzyme function. EFICAz can identify functionally discriminating residue (FDR) as residues that discriminate the members of a homo-functional family from a hetero-functional family. It combines the prediction from four independent methods, namely: (1) CHIEFc family-based (FDR) identification, (2) multiple PFAM-based FDR recognition, (3) CHIEFc SIT evaluation and (4) high-specificity multiple PROSITE patterns.
Permits to evaluate and compare macromolecular structures. MOLMOL generates high-quality pictures for documentation or publication, such as ribbon presentations and other schematic, simplified views of complex macromolecular structures. It evaluates the torsion angles of the polypeptide backbone. The tool can determine the principal axes of the global molecular shapes and calculates the corresponding radii of gyration.
Performs a systematic search of the shortest communication pathways that traverse a protein structure. Wordom is a mixed Protein Structure Network (PSN) and Elastic Network Model (ENM)-based strategy, i.e., PSN-ENM, for fast investigation of allosterism in biological systems. The approach was validated on the PDZ2 domain from tyrosine phosphatase 1E (PTP1E) in its free (APO) and peptide-bound states.
Allows user to visualize protein sequence features. GPCRHMM offers several advantages: users can zoom in on a region and zoom-dependent sequence rendering displays residues automatically at a sufficiently high zoom level. Moreover, researchers have control to customize the appearance of the data being displayed.
Aims to build an accurate alignment for the entire length of a target sequence from a set of alternative inputs. MMM brings a method that assists users to identify low-sequence regions of “twilight” and “midnight zones". It detects the alternatively aligned regions from a set of input alignments, and the best scoring regions from a set of alternative segments are combined with the core part of the alignments to produce the final MMM alignment.
Finds a maximum clique in an undirected graph. Maximum Clique Algorithm consists in an improvement to an approximate coloring algorithm. The improvement of this algorithm and the use of upper bounds, reduce the number of steps required to find the maximum clique. The software has been tested on random graphs and benchmark graphs, which were developed as part of the Second DIMACS Challenge.