ArticlePloS one2009
PSPP: a protein structure prediction pipeline for computing clusters.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Combinatorial peptide-based epitope mapping from Ebola virus DNA vaccines and infections reveals residue-level determinants of antibody binding.Human vaccines & immunotherapeutics · 2017Article
- Modeling the Role of Epitope Arrangement on Antibody Binding Stoichiometry in Flaviviruses.Biophysical journal · 2016Article
- Structure-based pKa prediction provides a thermodynamic basis for the role of histidines in pH-induced conformational transitions in dengue virus.Biochemistry and biophysics reports · 2015Article
- Detection of orexin A neuropeptide in biological fluids using a zinc oxide field effect transistor.ACS chemical neuroscience · 2013Article
- A fusion-inhibiting peptide against Rift Valley fever virus inhibits multiple, diverse viruses.PLoS neglected tropical diseases · 2013Article
- Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen.PloS one · 2013Article
- Integrative analysis workflow for the structural and functional classification of C-type lectins.BMC bioinformatics · 2011Article
- Novel plant-derived recombinant human interferons with broad spectrum antiviral activity.Antiviral research · 2011Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster. METHODOLOGY/PRINCIPAL
findingsThe pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP) fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML) formats. So far, the pipeline has been used to study viral and bacterial proteomes.
conclusionsThe standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform resource-intensive ab initio structure prediction.
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