Molecular recognition features (MoRFs) are short binding regions located in longer intrinsically disordered protein regions. Although these short regions lack a stable structure in the natural state, they readily undergo disorder-to-order transitions upon binding to their partner molecules. MoRFs play critical roles in the molecular interaction network of a cell, and are associated with many human genetic diseases. Therefore, identification of MoRFs is an important step in understanding functional aspects of these proteins and in finding applications in drug design.

High-throughput intact glycopeptide analysis is crucial for elucidating the physiological and pathological status of the glycans attached to each glycoprotein. Mass spectrometry-based glycoproteomic methods are challenging because of the diversity and heterogeneity of glycan structures. Therefore, we developed an MS1-based site-specific glycoform analysis method named "Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile (Glyco-RIDGE)" for a more comprehensive analysis. This method detects glycopeptide signals as a cluster based on the mass and chromatographic properties of glycopeptides and then searches for each combination of core peptides and glycan compositions by matching their mass and retention time differences. Here, we developed a novel browser-based software named GRable for semi-automated Glyco-RIDGE analysis with significant improvements in glycopeptide detection algorithms, including "parallel clustering." This unique function improved the comprehensiveness of glycopeptide detection and allowed the analysis to focus on specific glycan structures, such as pauci-mannose. The other notable improvement is evaluating the "confidence level" of the GRable results, especially using MS2 information. This function facilitated reduced misassignment of the core peptide and glycan composition and improved the interpretation of the results. Additional improved points of the algorithms are "correction function" for accurate monoisotopic peak picking; one-to-one correspondence of clusters and core peptides even for multiply sialylated glycopeptides; and "inter-cluster analysis" function for understanding the reason for detected but unmatched clusters. The significance of these improvements was demonstrated using purified and crude glycoprotein samples, showing that GRable allowed site-specific glycoform analysis of intact sialylated glycoproteins on a large-scale and in-depth. Therefore, this software will help us analyze the status and changes in glycans to obtain biological and clinical insights into protein glycosylation by complementing the comprehensiveness of MS2-based glycoproteomics. GRable can be freely run online using a web browser via the GlyCosmos Portal (https://glycosmos.org/grable).

The factors that lead to the improvement of gait function in patients with diseases of the central nervous system (CNS) who use a hybrid assistive limb (HAL) are not yet fully understood. The purpose of the present study was to analyze these factors to determine the prognosis of the patients’ gait function. Patients whose CNS disease was within 180 days since onset were designated as the subacute-phase patients, and patients whose disease onset had occurred more than 180 days previously were designated as chronic-phase patients. Fifteen subacute-phase patients and 15 chronic-phase patients were given HAL training. The study analyzed how post-training walking independence in these patients was affected by the following factors: age, disease, lesion area, lower limb function, balance, period until the start of training, number of training sessions, additional rehabilitation, higher-order cognitive dysfunction, HAL model, and the use of a non-weight-bearing walking-aid. In subacute-phase patients, walking independence was related to lower limb function (rs = 0.35). In chronic-phase patients, there was a statistically significant correlation between post-training walking independence and balance (rs = 0.78). In addition, in patients with a severe motor dysfunction that was accompanied by inattention and global cognitive dysfunction, little improvement occurred, even with double-leg model training, because they had difficulty wearing the device. The results demonstrated that the factors that improved walking independence post HAL training differed between patients with subacute- and chronic-stage CNS diseases. The findings may serve as valuable information for future HAL training of patients with CNS diseases.

Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics. We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although “combinatorial explosion” occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/ .