SpecP: A tool for spectral partitioning of protein contact graph.

SpecP is an open-source Python module that performs Spectral Partitioning on Protein Contact Graphs. Protein Contact Graphs are graph theory based representation of the protein structure, where each amino acid forms a 'vertex' and spatial contact of any two amino acids is an 'edge' between them. Spectral partitioning is carried out in SpecP based on the second smallest spectral value (eigen value) of the Protein Contact Graph. The eigen vector corresponding to the second smallest spectral value are partitioned into two clusters based on the sign of the corresponding vector entry. Spectral Partitioning algorithm is repeatedly carried out until the desired numbers of partitions are obtained. SpecP visualizes the spectrally partitioned clusters of protein structure along with the Protein Contact Map and Protein Contact Graph which can be saved for later use. It also possesses an interactive mode whereby the user has the ability to zoom, pan, resize and save these raster images in various image formats (.eps, .jpg, .png) manually. SpecP is a stand-alone extensible tool useful for structural analysis of proteins.

Looking for a sequence based allostery definition: a statistical journey at different resolution scales.

The aim of this work was to detect allosteric hotspots signatures characterizing protein regions acting as the 'key drivers' of global allosteric conformational change. We computationally estimated the relative strength of intra-molecular interaction in allosteric proteins between two putative allostery-susceptible sites using a co-evolution model based upon the optimization of the cross-correlation in terms of free-energy-transfer hydrophobicity scale (Tanford scale) distribution along the chain. Cross-Recurrence Quantification Analysis (Cross-RQA) applied on the sequences of allostery susceptible sites showed evidence of strong interaction amongst allosteric susceptible sites. This could be due to transient weak molecular bonds between allostery susceptible patches enabling regions far-apart to come together. Further, using a large protein dataset, by comparing allosteric protein set with a randomly generated sequence population as well as a generic protein set, we reconfirmed our earlier findings that hydrophobicity patterning (as formalized by Recurrence Quantification Analysis (RQA) descriptors) may serve as determinant of allostery and its relevance in the transmission of allosteric conformational change. We applied RQA to free-energy-transfer hydrophobicity-transformed amino acid sequences of the allostery dataset to extract allostery specific global sequence features. These free-energy-transfer hydrophobicity-based RQA markers proved to be representative of allosteric signatures and not related to the differences between randomly generated and real proteins. These free-energy-transfer hydrophobicity-based RQA markers when evaluated by pattern recognition tools could distinguish allosteric proteins with 92% accuracy.

Sequence signatures of allosteric proteins towards rational design.

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.