Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen’sd = −0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.

Protein phosphorylation plays a fundamental role in most of the cellular regulatory pathways. Experimental identification of protein kinases' (PKs) substrates with their phosphorylation sites is labor-intensive and often limited by the availability and optimization of enzymatic reactions. Recently, large-scale analysis of the phosphoproteome by the mass spectrometry (MS) has become a popular approach. But experimentally, it is still difficult to distinguish the kinase-specific sites on the substrates. In this regard, the in silico prediction of phosphorylation sites with their specific kinases using protein's primary sequences may provide guidelines for further experimental consideration and interpretation of MS phosphoproteomic data. A variety of such tools exists over the Internet and provides the predictions for at most 30 PK subfamilies. We downloaded the verified phosphorylation sites from the public databases and curated the literature extensively for recently found phosphorylation sites. With the hypothesis that PKs in the same subfamily share similar consensus sequences/motifs/functional patterns on substrates, we clustered the 216 unique PKs in 71 PK groups, according to the BLAST results and protein annotations. Then, we applied the group-based phosphorylation scoring (GPS) method on the data set; here, we present a comprehensive PK-specific prediction server GPS, which could predict kinase-specific phosphorylation sites from protein primary sequences for 71 different PK groups. GPS has been implemented in PHP and is available on a www server at http://973-proteinweb.ustc.edu.cn/gps/gps_web/ .

Systematic dissection of the sumoylation proteome is emerging as an appealing but challenging research topic because of the significant roles sumoylation plays in cellular dynamics and plasticity. Although several proteome-scale analyzes have been performed to delineate potential sumoylatable proteins, the bona fide sumoylation sites still remain to be identified. Previously, we carried out a genome-wide analysis of the SUMO substrates in human nucleus using the putative motif ψ-K-X-E and evolutionary conservation. However, a highly specific predictor for in silico prediction of sumoylation sites in any individual organism is still urgently needed to guide experimental design. In this work, we present a computational system SUMOsp—SUMOylation Sites Prediction, based on a manually curated dataset, integrating the results of two methods, GPS and MotifX, which were originally designed for phosphorylation site prediction. SUMOsp offers at least as good prediction performance as the only available method, SUMOplot, on a very large test set. We expect that the prediction results of SUMOsp combined with experimental verifications will propel our understanding of sumoylation mechanisms to a new level. SUMOsp has been implemented on a freely accessible web server at: http://bioinformatics.lcd-ustc.org/sumosp/ .