CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first-or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

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Sequence similarity between a translated nucleotide sequence and a known biological protein can provide strong evidence for the presence of a homologous coding region, even between distantly related genes. The computer program BLASTX performed conceptual translation of a nucleotide query sequence followed by a protein database search in one programmatic step. We characterized the sensitivity of BLASTX recognition to the presence of substitution, insertion and deletion errors in the query sequence and to sequence divergence. Reading frames were reliably identified in the presence of 1% query errors, a rate that is typical for primary sequence data. BLASTX is appropriate for use in moderate and large scale sequencing projects at the earliest opportunity, when the data are most prone to containing errors.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anticoagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multi- We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches.This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

The advent of high-throughput proteomics has enabled the identification of ever increasing numbers of proteins. Correspondingly, the number of publications centered on these protein identifications has increased dramatically. With the first results of the HUPO Plasma Proteome Project being analyzed and many other large-scale proteomics projects about to disseminate their data, this trend is not likely to flatten out any time soon. However, the publication mechanism of these identified proteins has lagged behind in technical terms. Often very long lists of identifications are either published directly with the article, resulting in both a voluminous and rather tedious read, or are included on the publisher's website as supplementary information. In either case, these lists are typically only provided as portable document format documents with a custom-made layout, making it practically impossible for computer programs to interpret them, let alone efficiently query them. Here we propose the proteomics identifications (PRIDE) database (http://www.ebi.ac.uk/pride) as a means to finally turn publicly available data into publicly accessible data. PRIDE offers a web-based query interface, a user-friendly data upload facility, and a documented application programming interface for direct computational access. The complete PRIDE database, source code, data, and support tools are freely available for web access or download and local installation.

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anticoagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multi

With the availability of a nearly complete sequence of the human genome, aligning expressed sequence tags (EST) to the genomic sequence has become a practical and powerful strategy for gene prediction. Elucidating gene structure is a complex problem requiring the identification of splice junctions, gene boundaries, and alternative splicing variants. We have developed a software tool, Transcript Assembly Program (TAP), to delineate gene structures using genomically aligned EST sequences. TAP assembles the joint gene structure of the entire genomic region from individual splice junction pairs, using a novel algorithm that uses the EST-encoded connectivity and redundancy information to sort out the complex alternative splicing patterns. A method called polyadenylation site scan (PASS) has been developed to detect poly-A sites in the genome. TAP uses these predictions to identify gene boundaries by segmenting the joint gene structure at polyadenylated terminal exons. Reconstructing 1007 known transcripts, TAP scored a sensitivity (Sn) of 60% and a specificity (Sp) of 92% at the exon level. The gene boundary identification process was found to be accurate 78% of the time. TAP also reports alternative splicing patterns in EST alignments. An analysis of alternative splicing in 1124 genic regions suggested that more than half of human genes undergo alternative splicing. Surprisingly, we saw an absolute majority of the detected alternative splicing events affect the coding region. Furthermore, the evolutionary conservation of alternative splicing between human and mouse was analyzed using an EST-based approach. (See