Protein secondary structure: Entropy, correlations and prediction

Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone, or by non-local tertiary interactions? To answer this question we have measured the entropy densities of primary structure and secondary structure sequences, and the local inter-sequence mutual information density. We find that the important inter-sequence interactions are short ranged, that correlations between neighboring amino acids are essentially uninformative, and that only 1/4 of the total information needed to determine the secondary structure is available from local inter-sequence correlations. Since the remaining information must come from non-local interactions, this observation supports the view that the majority of most proteins fold via a cooperative process where secondary and tertiary structure form concurrently. To provide a more direct comparison to existing secondary structure prediction methods, we construct a simple hidden Markov model (HMM) of the sequences. This HMM achieves a prediction accuracy comparable to other single sequence secondary structure prediction algorithms, and can extract almost all of the inter-sequence mutual information. This suggests that these algorithms are almost optimal, and that we should not expect a dramatic improvement in prediction accuracy. However, local correlations between secondary and primary structure are probably of under-appreciated importance in many tertiary structure prediction methods, such as threading.Comment: 8 pages, 5 figure

SCOPe: Structural Classification of Proteins--extended, integrating SCOP and ASTRAL data and classification of new structures.

Structural Classification of Proteins-extended (SCOPe, http://scop.berkeley.edu) is a database of protein structural relationships that extends the SCOP database. SCOP is a manually curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. Development of the SCOP 1.x series concluded with SCOP 1.75. The ASTRAL compendium provides several databases and tools to aid in the analysis of the protein structures classified in SCOP, particularly through the use of their sequences. SCOPe extends version 1.75 of the SCOP database, using automated curation methods to classify many structures released since SCOP 1.75. We have rigorously benchmarked our automated methods to ensure that they are as accurate as manual curation, though there are many proteins to which our methods cannot be applied. SCOPe is also partially manually curated to correct some errors in SCOP. SCOPe aims to be backward compatible with SCOP, providing the same parseable files and a history of changes between all stable SCOP and SCOPe releases. SCOPe also incorporates and updates the ASTRAL database. The latest release of SCOPe, 2.03, contains 59 514 Protein Data Bank (PDB) entries, increasing the number of structures classified in SCOP by 55% and including more than 65% of the protein structures in the PDB

SIFTER search: a web server for accurate phylogeny-based protein function prediction.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded

An unappreciated role for RNA surveillance

BACKGROUND: Nonsense-mediated mRNA decay (NMD) is a eukaryotic mRNA surveillance mechanism that detects and degrades mRNAs with premature termination codons (PTC(+ )mRNAs). In mammals, a termination codon is recognized as premature if it lies more than about 50 nucleotides upstream of the final intron position. More than a third of reliably inferred alternative splicing events in humans have been shown to result in PTC(+ )mRNA isoforms. As the mechanistic details of NMD have only recently been elucidated, we hypothesized that many PTC(+ )isoforms may have been cloned, characterized and deposited in the public databases, even though they would be targeted for degradation in vivo. RESULTS: We analyzed the human alternative protein isoforms described in the SWISS-PROT database and found that 144 (5.8% of 2,483) isoform sequences amenable to analysis, from 107 (7.9% of 1,363) SWISS-PROT entries, derive from PTC(+ )mRNA. CONCLUSIONS: For several of the PTC(+ )isoforms we identified, existing experimental evidence can be reinterpreted and is consistent with the action of NMD to degrade the transcripts. Several genes with mRNA isoforms that we identified as PTC(+ )- calpain-10, the CDC-like kinases (CLKs) and LARD - show how previous experimental results may be understood in light of NMD

Biases in Illumina transcriptome sequencing caused by random hexamer priming

Generation of cDNA using random hexamer priming induces biases in the nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer. The bias is independent of organism and laboratory and impacts the uniformity of the reads along the transcriptome. We provide a read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias