Can computationally designed protein sequences improve secondary structure prediction?

Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could be significantly enhanced by using a large database of computationally designed sequences.

A toll-like receptor-4-interacting surfactant protein-A-derived peptide suppresses tumor necrosis factor-α release from mouse JAWS II dendritic cells.

Surfactant protein-A (SP-A) and Toll-like receptor-4 (TLR4) proteins are recognized as pathogen-recognition receptors. An exaggerated activation of TLR4 induces inflammatory response, whereas SP-A protein down-regulates inflammation. We hypothesized that SP-A-TLR4 interaction may lead to inhibition of inflammation.

Unraveling the conundrum of seemingly discordant protein-protein interaction datasets.

Most high-throughput experimental results of protein-protein interactions (PPIs) are seemingly inconsistent with each other. In this article, we re-evaluated these contradictions within the context of the underlying domain-domain interactions (DDIs) for two Escherichia coli and four Saccharomyces cerevisiae PPI datasets derived from high-throughput (yeast two-hybrid and tandem affinity purification) experimental platforms. For shared DDIs across pairs of compared datasets, we observed a remarkably high pair-wise correlation (Pearson correlation coefficient between 0.

PSPP: a protein structure prediction pipeline for computing clusters.

Background: Protein 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.

FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator.

Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary.

Combining sequence and structural profiles for protein solvent accessibility prediction.

Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein.

A fast algorithm for low-resolution protein structure prediction.

We propose a new approach for the protein tertiary structure prediction based on the concept of mini-threading. The method identifies useful fragments in Protein Data Bank (PDB) with variable lengths and retrieves spatial restraints. The multidimensional scaling method and least-squares minimization are used to build coarse-grain structural models.

MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction.

Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches.