Background: Gene targeting by homology-directed repair (HDR) can precisely edit the genome and is a versatile tool for biomedical research. However, the efficiency of HDR-based modification is still low in many model organisms including zebrafish. Recently, long single-stranded DNA (lssDNA) molecules have been developed as efficient alternative donor templates to mediate HDR for the generation of conditional mouse alleles. Here we report a method, zLOST (zebrafish long single-stranded DNA template), which utilises HDR with a long single-stranded DNA template to produce more efficient and precise mutations in zebrafish. Results: The efficiency of knock-ins was assessed by phenotypic rescue at the tyrosinase (tyr) locus and confirmed by sequencing. zLOST was found to be a successful optimised rescue strategy: using zLOST containing a tyr repair site, we restored pigmentation in at least one melanocyte in close to 98% of albino tyr^25del/25del embryos, although more than half of the larvae had only a small number of pigmented cells. Sequence analysis showed that there was precise HDR dependent repair of the tyr locus in these rescued pigmented embryos. Furthermore, quantification of zLOST knock-in efficiency at the rps14, nop56 and th loci by next generation sequencing demonstrated that zLOST showed a clear improvement. We utilised the HDR efficiency of zLOST to precisely model specific human disease mutations in zebrafish with ease. Finally, we determined that this method can achieve a germline transmission rate of up to 31.8%. Conclusions: In summary, these results show that zLOST is a useful method of zebrafish genome editing, particularly for generating desired mutations by targeted DNA knock-in through HDR.

Summary: The WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37 000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPortal and IntAct. In addition, the web site is largely improved for visualization, exploring and data downloading. Availability and implementation: http://www.wdspdb.com/wdsp/ or http://wu.scbb.pkusz.edu.cn/wdsp/. Supplementary information: Supplementary data are available at Bioinformatics online.

Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance. We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors. The software is freely available at http://www.wdspdb.com/IDRMutPred. Supplementary data are available at Bioinformatics online.

WD40 repeat proteins constitute one of the largest families in eukaryotes, and widely participate in various fundamental cellular processes by interacting with other molecules. Based on individual WD40 proteins, previous work has demonstrated that their structural characteristics should confer great potential of interaction and complex formation, and has speculated that they may serve as hubs in the protein-protein interaction (PPI) network. However, what roles the whole family plays in organizing the PPI network, and whether this information can be utilized in complex prediction remain unclear. To address these issues, quantitative and systematic analyses of WD40 proteins from the perspective of PPI networks are highly required. In this work, we built two human PPI networks by using data sets with different confidence levels, and studied the network properties of the whole human WD40 protein family systematically. Our analyses have quantitatively confirmed that the human WD40 protein family, as a whole, tends to be hubs with an odds ratio of about 1.8 or greater, and the network decomposition has revealed that they are prone to enrich near the global center of the whole network with a fold change of two in the median k-values. By integrating expression profiles, we have further shown that WD40 hub proteins are inclined to be intramodular, which is indicative of complex assembling. Based on this information, we have further predicted 1674 potential WD40-associated complexes by choosing a clique-based method, which is more sensitive than others, and an indirect evaluation by co-expression scores has demonstrated its reliability. At the systems level but not sporadic examples’ level, this work has provided rich knowledge for better understanding WD40 proteins’ roles in organizing the PPI network. These findings and predicted complexes can offer valuable clues for prioritizing candidates for further studies.

G-protein-coupled receptors (GPCRs) are a critical family in the human proteome and are involved in various physiological processes. They are also the most important drug target, with approximately 30% of approved drugs acting on such receptors. The members of the family are divided into six classes based on their structural and functional characteristics. Understanding their structural–functional relationships will benefit us in future drug development. In this article, we investigate the features of protein function, structure, and energy that describe the dynamics of the GPCR activation process between different families. GPCRs straddle the cell membrane and transduce signals from outside the membrane into the cell. During the process, the conformational change in GPCRs that is activated by the binding of signal molecules is essential. During the binding process, different types of signal molecules result in different signal transfer efficiencies. Therefore, the GPCR classes show a variety of structures and activation processes. Based on the experimental crystal structures, we modeled the activation process of the β2 adrenergic receptor (β2AR), glucagon receptor (GCGR), and metabotropic glutamate receptor 2 (mGluR2), which represent class A, B, and C GPCRs, respectively. We calculated their activation free-energy landscapes and analyzed the structure–energy–function relationship. The results show a consistent picture of the activation mechanisms between different types of GPCRs. This could also provide us a way to understand other signal transduction proteins.