We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions

SignalP 4.0: discriminating signal peptides from transmembrane regions

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

/pdf/string-v10-protein-protein-interaction-networks-integrated-2gg77rxec0.pdf

STRING v10: protein–protein interaction networks, integrated over the tree of life

Protein identification and analysis software performs a central role in the investigation of proteins from two-dimensional (2-D) gels and mass spectrometry. For protein identification, the user matches certain empirically acquired information against a protein database to define a protein as already known or as novel. For protein analysis, information in protein databases can be used to predict certain properties about a protein, which can be useful for its empirical investigation. The two processes are thus complementary. Although there are numerous programs available for those applications, we have developed a set of original tools with a few main goals in mind. Specifically, these are: 1. To utilize the extensive annotation available in the Swiss-Prot database wherever possible, in particular the position-specific annotation in the Swiss-Prot feature tables to take into account posttranslational modifications and protein processing. 2. To develop tools specifically, but not exclusively, applicable to proteins prepared by two dimensional gel electrophoresis and peptide mass fingerprinting experiments. 3. To make all tools available on the World-Wide Web (WWW), and freely usable by the scientific community. In this chapter we give details about protein identification and analysis software that is available through the ExPASy World Wide Web server.

Protein identification and analysis tools in the ExPASy server

MOLMOL: a program for display and analysis of macromolecular structures.

https://www.cbs.dtu.dk/services/SignalP-4.1/paper-3.0.pdf

Improved Prediction of Signal Peptides: SignalP 3.0

The Protein Data Bank currently contains about 600 data sets of three-dimensional protein coordinates determined by X-ray crystallography or NMR. There is considerable redundancy in the data base, as many protein pairs are identical or very similar in sequence. However, statistical analyses of protein sequence-structure relations require nonredundant data. We have developed two algorithms to extract from the data base representative sets of protein chains with maximum coverage and minimum redundancy. The first algorithm focuses on optimizing a particular property of the selected proteins and works by successive selection of proteins from an ordered list and exclusion of all neighbors of each selected protein. The other algorithm aims at maximizing the size of the selected set and works by successive thinning out of clusters of similar proteins. Both algorithms are generally applicable to other data bases in which criteria of similarity can be defined and relate to problems in graph theory. The largest nonredundant set extracted from the current release of the Protein Data Bank has 155 protein chains. In this set, no two proteins have sequence similarity higher than a certain cutoff (30% identical residues for aligned subsequences longer than 80 residues), yet all structurally unique protein families are represented. Periodically updated lists of representative data sets are available by electronic mail from the file server "netserv@embl-heidelberg.de." The selection may be useful in statistical approaches to protein folding as well as in the analysis and documentation of the known spectrum of three-dimensional protein structures.

/pdf/selection-of-representative-protein-data-sets-5dyzvrbn65.pdf

Selection of representative protein data sets

To reduce redundancy in the Protein Data Bank of 3D protein structures, which is caused by many homologous proteins in the data bank, we have selected a representative set of structures. The selection algorithm was designed to (1) select as many nonhomologous structures as possible, and (2) to select structures of good quality. The representative set may reduce time and effort in statistical analyses.

/pdf/enlarged-representative-set-of-protein-structures-rbvyh8tuzt.pdf

Enlarged representative set of protein structures

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry of protein samples from two-dimensional (2-D) gels in conjunction with protein sequence database searches is frequently used to identify proteins. Moreover, the automatic analysis of complete 2-D gels with hundreds and even thousands of protein spots ("proteome analysis") is possible, without human intervention, with the availability of highly accurate mass spectrometry instruments, and high-throughput facilities for preparation and handling of protein samples from 2-D gels. However, the lack of software for precise automatic analysis and annotation of mass spectra, as well as software for in-batch sequence database queries, is increasingly becoming a significant bottleneck for the proteomics work flow. In the present paper we outline an algorithm for reliable, accurate, and automatic evaluation of mass spectrometric data and database searches. We show here that simply selecting from the sequence database the protein that has the most matching fragment masses often leads to false-positive results. Reliable protein identification is dependent on several parameters: the accuracy of fragment mass determination, the number of masses submitted for query, the mass distribution of query masses, the number of masses matching between sample and database protein, the size of the sequence database, and the kind and number of modifications considered. Using these parameters, we derive a simple statistical estimation that can be used to calculate the probability of true-positive protein identification.

Reliable automatic protein identification from matrix-assisted laser desorption/ionization mass spectrometric peptide fingerprints.

https://cbio.mskcc.org/publications/papers/sander/135.pdf

A Sequence Property Approach to Searching Protein Databases

PDBselect (http://bioinfo.tg.fh-giessen.de/pdbselect/) is a list of representative protein chains with low mutual sequence identity selected from the protein data bank (PDB) to enable unbiased statistics. The list increased from 155 chains in 1992 to more than 4500 chains in 2009. PDBfilter-select is an online service to generate user-defined selections.

/pdf/pdbselect-1992-2009-and-pdbfilter-select-50phc9omvz.pdf

Uwe Hobohm

Papers

Selection of representative protein data sets

Enlarged representative set of protein structures

Reliable automatic protein identification from matrix-assisted laser desorption/ionization mass spectrometric peptide fingerprints.

A Sequence Property Approach to Searching Protein Databases

PDBselect 1992–2009 and PDBfilter-select