The kinase ZYG-1 phosphorylates the cartwheel protein SAS-5 to drive centriole assembly in C. elegans

Centrioles organize centrosomes, the cell's primary microtubule-organizing centers (MTOCs). Centrioles double in number each cell cycle, and mis-regulation of this process is linked to diseases such as cancer and microcephaly. In C. elegans, centriole assembly is controlled by the Plk4 related-kinase ZYG-1, which recruits the SAS-5-SAS-6 complex. While the kinase activity of ZYG-1 is required for centriole assembly, how it functions has not been established. Here we report that ZYG-1 physically interacts with and phosphorylates SAS-5 on 17 conserved serine and threonine residues in vitro. Mutational scanning reveals that serine 10 and serines 331/338/340 are indispensable for proper centriole assembly. Embryos expressing SAS-5S10A exhibit centriole assembly failure, while those expressing SAS-5S331/338/340A possess extra centrioles. We show that in the absence of serine 10 phosphorylation, the SAS-5-SAS-6 complex is recruited to centrioles, but is not stably incorporated, possibly due to a failure to coordinately recruit the microtubule-binding protein SAS-4. Our work defines the critical role of phosphorylation during centriole assembly and reveals that ZYG-1 might play a role in preventing the formation of excess centrioles.

Exploring the binding mechanism of a small molecular Hsp70-Bim PPI inhibitor through molecular dynamic simulation

CONTEXT

The interface of Hsp70-Bim protein-protein interaction (PPI) has been identified as a specific target for Chronic Myeloid Leukemia (CML) therapy and the specific inhibitors were developed to exhibit in vivo anti-leukemia activities. Herein, we explored the binding mechanism of a Hsp70-Bim inhibitor, 6-(cyclohexylthio)-3-((2-morpholinoethyl) amino)-1-oxo-1H-phenalene-2-carbonitrile (S1g-6), to Hsp70 at the atomic level by MD simulation. TYR-149, THR-222, ALA-223, and GLY-224 on Hsp70 were identified as four key residues that contribute to Hsp70/S1g-6 complex. Moreover, the site mutation validation demonstrated the TYR-149 of Hsp70 is a "hot-spot" in the Hsp70-Bim PPI interface. These results could benefit the design of further inhibitors to occupy the Bim binding site on the Hsp70 surface.

METHODS

The binding mechanism of S1g-6 and Hsp70 was predicted through the molecular dynamics (MD) method by Gromacs-2021.3. The MD simulation was performed with 100-ps NVT and 100-ps NPT ensemble, and the force field was chosen as the Charmm36 force field. The temperature was set as 300 K, the time step was 2 fs and the total MD simulation time was 500 ns.

Normalized L3-based link prediction in protein-protein interaction networks

BACKGROUND

Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle.

RESULTS

In this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.

The Interactome of Protein, DNA, and RNA

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.

An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data

Protein-Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html.