A Simple Comparison between Specific Protein Secondary Structure Prediction Tools (original) (raw)
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PreSSAPro: A software for the prediction of secondary structure by amino acid properties
Computational Biology and Chemistry, 2007
PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.
Tools for Protein Structure Prediction at the bri-shur. com Web Portal
2012
Internet services on bioinformatics still remain a popular tool for the researchers. Here the authors present a recently developed web-site http://bri-shur.com where several tools and pipelines for protein structure prediction are implemented. The prediction of a structure for a particular protein often requires a sensitive and iterative approach, and the web-site provides an environment for this kind of work. Software that is used in the services includes both free programs available in the Internet and newly developed algorithms. The service on homology screening in PDB for a structure template is implemented using an approach that is alternative to well-known BLAST algorithm and it has some advantages over BLAST. The service on homology modeling uses well-known Nest program. The service on protein energy estimate allows selecting a best template in the set of homologs and adds a functionality of fold recognition to the environment. The design of the site simplifies several of the most useful bioinformatics routines, thus making them available to a large community of researchers. Services are provided free of charge without registration, and the user's privacy is taken care of.
Prediction of protein secondary structure at 80% accuracy
Proteins-structure Function and Bioinformatics, 2000
Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%-80.2% (77.9%-80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matrices as input, while at the output level it predicts on three consecutive residues simultaneously. The predictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is preliminary and awaits evaluation by CASP4. Proteins 2000;41:17-20.
Polymer, 2002
We have developed a new method for the prediction of the protein secondary structure from the amino acid sequence. The method is based on the most recent version (IV) of the standard GOR (J Mol Biol 120 (1978) 97) algorithm. A signi®cant improvement is obtained by combining multiple sequence alignments with the GOR method. Additional improvement in the predictions is obtained by a simple correction of the results when helices or sheets are too short, or if helices and sheets are direct neighbors along the sequence (we require at least one residue of coil state between them). The imposition of the requirement that the prediction must be strong enough, i.e. that the difference between the probability of the predicted (most probable) state and the probability of the second most probable state must be larger than a certain minimum value also improves signi®cantly secondary structure predictions. We have tested our method on 12 different proteins from the Protein Data Bank with known secondary structures. The average quality of the GOR prediction of the secondary structure for these 12 proteins without multiple sequence alignment was 63.4%. The multiple sequence alignments improve the average prediction to 71.9%. The correction for short helices and sheets and coil states separating sheets and helices improve further the average prediction to 74.4%. Setting the 10% minimum difference between the most probable and the second probable conformation leads to 77.0% accuracy of the prediction, while increasing this limit to 20% increases the average accuracy of the secondary structure prediction to 81.2%.
Protein Science, 1995
To improve secondary structure predictions in protein sequences, the information residing in multiple sequence alignments of substituted but structurally related proteins is exploited. A database comprised of 70 protein families and a total of 2,500 sequences, some of which were aligned by tertiary structural superpositions, was used to calculate residue exchange weight matrices within a-helical, P-strand, and coil substructures, respectively. Secondary structure predictions were made based on the observed residue substitutions in local regions of the multiple alignments and the largest possible associated exchange weights in each of the three matrix types. Comparison of the observed and predicted secondary structure on a per-residue basis yielded a mean accuracy of 72.2%. Individual a-helix, P-strand, and coil states were respectively predicted at 66.4,66.7, and 75.8% correctness, representing a well-balanced three-state prediction. The accuracy level, verified by cross-validation through jack-knife tests on all protein families, dropped, on average, to only 70.9%, indicating the rigor of the prediction procedure. On the basis of robustness, conceptual clarity, accuracy, and executable efficiency, the method has considerable advantage, especially with its sole reliance on amino acid substitutions within structurally related proteins.
PSSD: Protein Secondary Structure Database
proteins
Protein Secondary Structure Database (PSSD) is a database that incorporates sequences of secondary structure elements of all proteins which their three dimensional structures are defined by experimental methods such as NMR-Spectroscopy or X-Ray Crystallography and their structural data exists in Brookhaven protein databank. Dictionary of Secondary Structure of Proteins (DSSP) criteria have been used to define both ends of each structural element. At present PSSD includes 290,709 alpha helices, 418,362 beta strands, 571,176 turns and 118,109 helices 3(10) of 21,347 proteins. The following information is given for each entry: (i) PSSD Unique ID, (ii) Description, (iii), Organism source, (iv) Author(s), (v) PDB code, (vi) Cross references to PDB, DSSP and Swiss-Prot databanks, (vii) Sequence of secondary structure element, (viii) number of starting and ending amino acids of each element in its corresponding protein chain, (ix) length of element, (x) the number of the element in its regarding protein chain. A user friendly interface is developed for doing search in database using different combinations of fields mentioned above. Facilities provided in this database allow structure-sequence analysis studies faster, more reliable and suitable. Now, the database is located on IBB Bioinformatics Center (IBC) server. The interface can be accessed via: http://www.ibc.ut.ac.ir/pssd/.
A Comparative Study of Protein Tertiary Structure Prediction Methods
International Journal of Computer Science and Informatics, 2014
Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. The understanding of protein structures is vital to determine the function of a protein and its interaction with DNA, RNA and enzyme. Thus, protein structure is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. In this paper, different types of protein structures and methods for its prediction are described.