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1. A Förster resonance energy transfer sensor for live‐cell imaging of mitogen‐activated protein kinase activity in A rabidopsis

   https://doi.org/10.1111/tpj.14164  

   Zaman, Najia ; Seitz, Kati ; Kabir, Mohiuddin ; George‐Schreder, Lauren St. ; Shepstone, Ian ; Liu, Yidong ; Zhang, Shuqun ; Krysan, Patrick J. ( January 2019 , The Plant Journal)

   The catalytic activity of mitogen‐activated protein kinases (MAPKs) is dynamically modified in plants. SinceMAPKs have been shown to play important roles in a wide range of signaling pathways, the ability to monitorMAPKactivity in living plant cells would be valuable. Here, we report the development of a genetically encodedMAPKactivity sensor for use inArabidopsis thaliana. The sensor is composed of yellow and blue fluorescent proteins, a phosphopeptide binding domain, aMAPKsubstrate domain and a flexible linker. Usingin vitrotesting, we demonstrated that phosphorylation causes an increase in the Förster resonance energy transfer (FRET) efficiency of the sensor. TheFRETefficiency can therefore serve as a readout of kinase activity. We also produced transgenic Arabidopsis lines expressing this sensor ofMAPKactivity (SOMA) and performed live‐cell imaging experiments using detached cotyledons. Treatment with NaCl, the synthetic flagellin peptide flg22 and chitin all led to rapid gains inFRETefficiency. Control lines expressing a version ofSOMAin which the phosphosite was mutated to an alanine did not show any substantial changes inFRET. We also expressed the sensor in a conditional loss‐of‐function double‐mutant line for the ArabidopsisMAPKgenesMPK3andMPK6. These experiments demonstrated thatMPK3/6 are necessary for the NaCl‐inducedFRETgain of the sensor, while otherMAPKs are probably contributing to the chitin and flg22‐induced increases inFRET. Taken together, our results suggest thatSOMAis able to dynamically reportMAPKactivity in living plant cells.

    

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2. SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

   https://doi.org/10.1093/bioinformatics/btz926  

   Huang, Xiaoqiang ; Zheng, Wei ; Pearce, Robin ; Zhang, Yang ; Valencia, ed., Alfonso ( December 2019 , Bioinformatics)

   Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses.

   We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities.

   Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe.

   Supplementary data are available at Bioinformatics online.

    

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3. Electron‐deficient p‐benzoyl‐ l ‐phenylalanine derivatives increase covalent chemical capture yields for protein–protein interactions

   https://doi.org/10.1002/pro.3621  

   Joiner, Cassandra M. ; Breen, Meghan E. ; Mapp, Anna K. ( April 2019 , Protein Science)

   The photoactivatable amino acid p‐benzoyl‐l‐phenylalanine (pBpa) has been used for the covalent capture of protein–protein interactions (PPIs)in vitroand in living cells. However, this technique often suffers from poor photocrosslinking yields due to the low reactivity of the active species. Here we demonstrate that the incorporation of halogenated pBpa analogs into proteins leads to increased crosslinking yields for protein–protein interactions. The analogs can be incorporated into live yeast and upon irradiation capture endogenous PPIs. Halogenated pBpas will extend the scope of PPIs that can be captured and expand the toolbox for mapping PPIs in their native environment.

    

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4. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

   https://doi.org/10.1093/bioinformatics/btz328  

   Chen, Muhao ; Ju, Chelsea J. -T ; Zhou, Guangyu ; Chen, Xuelu ; Zhang, Tianran ; Chang, Kai-Wei ; Zaniolo, Carlo ; Wang, Wei ( July 2019 , Bioinformatics)

   Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.

   We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.

   The implementation is available at https://github.com/muhaochen/seq_ppi.git.

   Supplementary data are available at Bioinformatics online.

    

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5. A computational exploration of resilience and evolvability of protein–protein interaction networks

   https://doi.org/10.1038/s42003-021-02867-8  

   Klein, Brennan ; Holmér, Ludvig ; Smith, Keith M. ; Johnson, Mackenzie M. ; Swain, Anshuman ; Stolp, Laura ; Teufel, Ashley I. ; Kleppe, April S. ( December 2021 , Communications Biology)

   Abstract Protein–protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks’ resilience. We use publicly available data of PPIs for E. coli , S. cerevisiae , and H. sapiens , where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience , which highlights the key role of network structures in understanding the evolvability of phenotypic traits. 

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