Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

A new, fast algorithm for detecting protein coevolution using maximum compatible cliques

Abstract Background The MatrixMatchMaker algorithm was recently introduced to detect the similarity between phylogenetic trees and thus the coevolution between proteins. MMM finds the largest common submatrices between pairs of phylogenetic distance matrices, and has numerous advantages over existing methods of coevolution detection. However, these advantages came at the cost of a very long execution time. Results In this paper, we show that the problem of finding the maximum submatrix reduces to a multiple maximum clique subproblem on a graph of protein pairs. This allowed us to develop a new algorithm and program implementation, MMMvII, which achieved more than 600× speedup with comparable accuracy to the original MMM. Conclusions MMMvII will thus allow for more more extensive and intricate analyses of coevolution. Availability An implementation of the MMMvII algorithm is available at: http://www.uhnresearch.ca/labs/tillier/MMMWEBvII/MMMWEBvII.ph

Hyperphosphorylation of intrinsically disordered tau protein induces an amyloidogenic shift in its conformational ensemble.

Tau is an intrinsically disordered protein (IDP) whose primary physiological role is to stabilize microtubules in neuronal axons at all stages of development. In Alzheimer's and other tauopathies, tau forms intracellular insoluble amyloid aggregates known as neurofibrillary tangles, a process that appears in many cases to be preceded by hyperphosphorylation of tau monomers. Understanding the shift in conformational bias induced by hyperphosphorylation is key to elucidating the structural factors that drive tau pathology, however, as an IDP, tau is not amenable to conventional structural characterization. In this work, we employ a straightforward technique based on Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) and Hydrogen/Deuterium Exchange (HDX) to provide a detailed picture of residual structure in tau, and the shifts in conformational bias induced by hyperphosphorylation. By comparing the native and hyperphosphorylated ensembles, we are able to define specific conformational biases that can easily be rationalized as enhancing amyloidogenic propensity. Representative structures for the native and hyperphosphorylated tau ensembles were generated by refinement of a broad sample of conformations generated by low-computational complexity modeling, based on agreement with the TRESI-HDX profiles

Data on evolution of intrinsically disordered regions of the human kinome and contribution of FAK1 IDRs to cytoskeletal remodeling

We present data on the evolution of intrinsically disordered regions (IDRs) taking into account the entire human protein kinome. The evolutionary data of the IDRs with respect to the kinase domains (KDs) and kinases as a whole protein (WP) are reported. Further, we have reported its post translational modifications of FAK1 IDRs and their contribution to the cytoskeletal remodeling. We also report the data to build a protein-protein interaction (PPI) network of primary and secondary FAK1-interacting hybrid proteins. Detailed analysis of the data and its effect on FAK1-related functions have been described in “Structural pliability adjacent to the kinase domain highlights contribution of FAK1 IDRs to cytoskeletal remodeling” (Kathiriya et. al., 2016) [1]

Relative deuterium uptake profiles for native and hyperphosphorylated tau at 1.52 s of D₂O exposure.

<p>The native HDX profile (black bars) is shown directly below the tau domain structure and NMR-derived secondary structure map[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120416#pone.0120416.ref018" target="_blank">18</a>]. On the secondary structure map, yellow arrows indicate β-sheet propensity, green cylinders represent residual polypropylene helices and red cylinders denote regions with significant (> 18%) α-helical propensity. The hexapeptide regions are boxed in red. The hyperphosphorylated HDX profile (green and blue bars) is shown below the native profile. Blue bars indicate the presence of at least one phosphate on the segment indicated.</p

Workflow from raw data to deuterium uptake kinetics for typical peptides from the native and hyperphosphorylated protein.

<p><b>A</b>. Raw data for four peptides (columns) each with fits to the isotopic distribution to determine deuterium uptake (filled circles) at three different timepoints (rows). <b>B</b>. The resulting kinetic profile for each peptide, with single exponential fit (solid line) to extract <i>k</i>_obs. The calculated ‘random coil’ profile (dotted line, filled triangles) is shown for comparison.</p

Most representative structures from the native and hyperphosphorylated ensembles, colored by PF.

<p><b>A</b>. Most representative structure for the native ensemble (<i>R</i> = 0.30), which exhibits a global ‘S’-shaped fold with sequestration of the hexapeptides. <b>B</b>. Most representative structure for the hyperphosphosphorylated ensemble (<i>R</i> = 0.29) showing release of the N- and C-termini, full exposure of H2 and a few regions of residual structure, including around H1.</p

Histograms showing the distribution of agreement with the HDX data (Pearson coefficient) within the FRODAN ensembles.

<p>Each ensemble consists of 30,000 candidate structures, initialized based on pdb coordinates provided by the Zweckstetter group [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120416#pone.0120416.ref018" target="_blank">18</a>], and optimized using the FRODAN algorithm. Examples of structures associated with various R-value ‘bins’ are shown above, with the ‘most representative’ structures at the far right. <b>A</b>. The native ensemble. <b>B</b>. The hyperphosphorylated ensemble.</p