PP: details for methods used

1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Meth. in Enzymolgy, 266, 525-539, 1996

 SWISS-PROT is an annotated protein sequence database established in 1986 and maintained collaboratively, since 1987, by the Department of Medical Biochemistry of the University of Geneva and the EMBL Data Library (now the EMBL Outstation - The European Bioinformatics Institute (EBI)). The SWISS-PROT protein sequence data bank consists of sequence entries. 

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1. A Bairoch, and R Apweiler: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res., 28, 45-48, 2000

1. A Bairoch, and R Apweiler: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res., 28, 45-48, 2000

1. H M Berman, J Westbrook, Z Feng, G Gilliland, T N Bhat, H Weissig, I N Shindyalov, P E Bourne: The Protein Data Bank. Nucleic Acids Research, 28, 235-242, 2000

1. see SWISS-PROT, TREMBL, PDB

 MaxHom builds up a protein family (defined as all closely related proteins likely to have similar structures) in two steps:

1. In sweep 1, sequences are aligned consecutively to the search sequence by a standard dynamic programming method. After each sequence has been added a profile is compiled, and used to align the next sequence. added a profile is compiled, and used to align the next sequence. 
2. In sweep 2, after all sequences with significant homology have been picked from the BLASTP output, the profile is recompiled, and the dynamic programming algorithm starts once again to align consecutively the sequences, this time using the conservation profile as derived after completion of sweep 1. 

1. C Sander, and R Schneider:Database of Homology-Derived Structures and the Structural Meaning of Sequence Alignment. Proteins, 9, 56-68, 1991

1. S Karlin, S F Altschul: Applications and statistics for multiple high-scoring segments in molecular sequences. PNAS, 90,5873-5877,1993

 We are running the iterated PSIblast on a subset of the BIG database with SWISS-PROT + TrEMBL + PDB sequences. The number of iteration, the cut-off thresholds and the particular details of which sequences are used from BIG has been optimised in our group. 

1. S F Altschul, T L Madden, A A Schäffer, J Zhang, Z Zhang, W Miller, and D J Lipman: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 25,3389-3402, 1997

 The following description is from the original ProSite site:

ProSite is a method of determining what is the function of uncharacterized proteins translated from genomic or cDNA sequences. It consists of a database of biologically significant sites, patterns and profiles that help to reliably identify to which known family of protein (if any) a new sequence belongs.

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1. K Hofmann , P Bucher, L Falquet, A Bairoch: The PROSITE database, its status in 1999. Nucleic Acids Res, 27, 215-219, 1999

 The following description is from the original ProDom site (which supplies a rather useful graphical interface to the ProDom database):

The ProDom protein domain database consists of an automatic compilation of homologous domains detected in the SWISS-PROT database by the DOMAINER algorithm (ELL Sonnhammer & D Kahn, Prot. Sci., 1994, 3, 482-492). It has been devised to assist with the analysis of the domain arrangement of proteins. 

ProDom `domains' are inferred on the basis of conserved subsequences as found in various proteins. Such a conservation corresponds frequently, though not always, to genuine structural domains: therefore domain boundaries should be treated with caution. For some domain families experts have been asked to correct domain boundaries on the basis of both sequence and structural information. This expertise will complement the automated process and improve the quality of ProDom domain families. 

1. F Corpet, F Servant, J Gouzy, and D Kahn: ProDom and ProDom-CG: tools for protein domain analysis and whole genome comparisons. Nucleic Acids Res, 28, 267-269, 2000

 The following description is from the original SEG documentation (JC Wootton & S Federhen, 1996, Meth Enzymology, 266, 554-571): 

SEG divides sequences into contrasting segments of low-complexity and high-complexity. Low-complexity segments defined by the algorithm represent "simple sequences" or "compositionally-biased regions". 

Locally-optimized low-complexity segments are produced at defined levels of stringency, based on formal definitions of local compositional complexity. The segment lengths and the number of segments per sequence are determined automatically by the algorithm. 

1. J C Wootton, and S Federhen: Analysis of compositionally biased regions in sequence databases. Methods in Enzymology, 266, 554-571, 1996

1. M Cokol, R Nair, and B Rost:in preparation, 2000

 see PHDsec PHDacc PHDhtm 

1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996

 Secondary structure is predicted by a system of neural networks rating at an expected average accuracy > 72% for the three states helix, strand and loop (Rost & Sander, PNAS, 1993 , 90, 7558-7562; Rost & Sander, JMB, 1993 , 232, 584-599; and Rost & Sander, Proteins, 1994 , 19, 55-72). Evaluated on the same data set, PHDsec is rated at ten percentage points higher three-state accuracy than methods using only single sequence information, and at more than six percentage points higher than, e.g., a method using alignment information based on statistics (Levin, Pascarella, Argos & Garnier, Prot. Engng., 6, 849-54, 1993). 

PHDsec predictions have three main features: 

1. improved accuracy through evolutionary information from multiple sequence alignments 
2. improved beta-strand prediction through a balanced training procedure 
3. more accurate prediction of secondary structure segments by using a multi-level system 

1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996.
2. B Rost, and C Sander: Prediction of protein secondary structure at better than 70% accuracy. J Molecular Biol, 232, 584-599, 1993.
3. B Rost, and C Sander: Combining evolutionary information and neural networks to predict protein secondary structure. Proteins, 19, 55-77, 1994

 Solvent accessibility is predicted by a neural network method rating at a correlation coefficient (correlation between experimentally observed and predicted relative solvent accessibility) of 0.54 cross-validated on a set of 238 globular proteins (Rost & Sander, Proteins, 1994, 20, 216-226). The output of the neural network codes for 10 states of relative accessibility. Expressed in units of the difference between prediction by homology modelling (best method) and prediction at random (worst method), PHDacc is some 26 percentage points superior to a comparable neural network using three output states (buried, intermediate, exposed) and using no information from multiple alignments. 

1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996.
2. B Rost, and C Sander: Conservation and prediction of solvent accessibility in protein families. Proteins, 20, 216-226, 1994

 Transmembrane helices in integral membrane proteins are predicted by a system of neural networks. The shortcoming of the network system is that often too long helices are predicted. These are cut by an empirical filter. The final prediction (Rost et al., Protein Science, 1995, 4, 521-533) has an expected per-residue accuracy of about 95%. The number of false positives, i.e., transmembrane helices predicted in globular proteins, is about 2% (Rost et al. 1996). 

The neural network prediction of transmembrane helices (PHDhtm) is refined by a dynamic programming-like algorithm. This method resulted in correct predictions of all transmembrane helices for 89% of the 131 proteins used in a cross-validation test; more than 98% of the transmembrane helices were correctly predicted. The output of this method is used to predict topology, i.e., the orientation of the N-term with respect to the membrane. The expected accuracy of the topology prediction is > 86%. Prediction accuracy is higher than average for eukaryotic proteins and lower than average for prokaryotes. PHDtopology was more accurate than all other methods tested on identical data sets in 1996 (Rost, Casadio & Fariselli, 1996a and 1996b). 

1. B Rost: PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology, 266, 525-539, 1996.
2. B Rost, P Fariselli, and R Casadio: Topology prediction for helical transmembrane proteins at 86% accuracy. Protein Science, 7, 1704-1718, 1996

1. B Rost: PROF: predicting one-dimensional protein structure by profile based neural networks. unpublished, 2000

1. B Rost: PROF: predicting one-dimensional protein structure by profile based neural networks. unpublished, 2000

1. B Rost: PROF: predicting one-dimensional protein structure by profile based neural networks. unpublished, 2000

 An additional result from the prediction of solvent accessibility is that of protein globularity. That method is not published, yet. For more information, you may have a look at the preliminary preprint. 

1. B Rost:Short yeast ORFs: expressed protein or not? unpublished, 2000

 Remote homologues (0-25% sequence identity) are detected by a novel prediction-based threading method (Rost 1995a and 1995b). The principle idea is to detect similar motifs of secondary structure and accessibility between a sequence of unknown structure and a known fold . For the recognition of similarities between entire folds, the expected accuracy (first hit of alignment list correct) is about 60% (Rost, ISMB95 Proceedings, 1995, AAAI Press, 314-321). If the goal is to correctly detect even short homologous fragments, still about 30% of the first hits are correct (compared to an accuracy of 14% for simple sequence alignments: full paper). Hits with z-scores above 3.0 are more reliable (accuracy > 60%). (Note: a number of similar or better threading services based on similar principles are available through META: http://cubic.bioc.columbia.edu/pp/submit_meta.html). 

1. B Rost: TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures. In: C Rawlings, D Clark, R Altman, L Hunter, T Lengauer, and S Wodak (eds.) The third international conference on Intelligent Systems for Molecular Biology (ISMB), Cambridge, England, Menlo Park, CA: AAAI Press, 314-321, 1995.
2. B Rost, R Schneider, and C Sander: Protein fold recognition by prediction-based threading. J of Molecular Biology, 270, 471-480, 1997

 The following description is from the original COILS site:

COILS is a program that compares a sequence to a database of known parallel two-stranded coiled-coils and derives a similarity score. By comparing this score to the distribution of scores in globular and coiled-coil proteins, the program then calculates the probability that the sequence will adopt a coiled-coil conformation. 

1. A Lupas: Prediction and Analysis of Coiled-Coil Structures. Methods in Enzymology, 266, 513-525, 1996

 The following description is from the original CYSPRED publication:

A neural network-based predictor is trained to distinguish the bonding states of cysteine in proteins starting from the residue chain. Training is performed using 2452 cysteine-containing segments extracted from 641 non homologous proteins of well resolved 3D structure. After a cross-validation procedure efficiency of the prediction scores as high as 72% when the predictor is trained using protein single sequences. The addition of evolutionary information in the form of multiple sequence alignment and a jury of neural networks increase the prediction efficiency up to 81%. Assessment of the goodness of the prediction with a reliability index indicates that more than 60% of the predictions have an accuracy level greater than 90%. A comparison with a statistical method previously described and tested on the same data base shows that the neural network-based predictor is performing with the highest efficiency.

1. P Fariselli, P Riccobelli, and R Casadio: Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins, 36, 340-346, 1999

1. N P Brown, C Leroy, and C Sander: MView: A Web compatible database search or multiple alignment viewer. Bioinformatics, 14, 380-381, 1998

To use the tool, you have to do the following: 

1. Save the PredictProtein results into a file.
2. Load ESPript (see below)
3. Provide the file in which you saved the results in the appropriate boxes of ESPript (click 'Browse ..' boxes in upper half)
4. Run ESPript (click 'Run ESPript' button on lower left corner)

1. P Gouet, E Courcelle, D I Stuart, and F Metoz: ESPript: multiple sequence alignments in PostScript. Bioinformatics, 15, 305-308, 1999