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1. Quokka : a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome

   https://doi.org/10.1093/bioinformatics/bty522  

   Li, Fuyi ; Li, Chen ; Marquez-Lago, Tatiana_T ; Leier, André ; Akutsu, Tatsuya ; Purcell, Anthony_W ; Ian_Smith, A. ; Lithgow, Trevor ; Daly, Roger_J ; Song, Jiangning ; et al ( June 2018 , Bioinformatics)

   Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis.

   In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation.

   The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/.

   Supplementary data are available at Bioinformatics online.

    

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2. Characterizing protein–surface and protein–nanoparticle conjugates: Activity, binding, and structure

   https://doi.org/10.1063/5.0101406  

   Correira, Joshua M. ; Handali, Paul R. ; Webb, Lauren J. ( September 2022 , The Journal of Chemical Physics)

   Many sensors and catalysts composed of proteins immobilized on inorganic materials have been reported over the past few decades. Despite some examples of functional protein–surface and protein–nanoparticle conjugates, thorough characterization of the biological–abiological interface at the heart of these materials and devices is often overlooked in lieu of demonstrating acceptable system performance. This has resulted in a focus on generating functioning protein-based devices without a concerted effort to develop reliable tools necessary to measure the fundamental properties of the bio–abio interface, such as surface concentration, biomolecular structure, and activity. In this Perspective, we discuss current methods used to characterize these critical properties of devices that operate by integrating a protein into both flat surfaces and nanoparticle materials. We highlight the advantages and drawbacks of each method as they relate to understanding the function of the protein–surface interface and explore the manner in which an informed understanding of this complex interaction leads directly to the advancement of protein-based materials and technology. 

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3. Improving protein structure prediction using templates and sequence embedding

   https://doi.org/10.1093/bioinformatics/btac723  

   Wu, Fandi ; Jing, Xiaoyang ; Luo, Xiao ; Xu, Jinbo ; Martelli, ed., Pier Luigi ( November 2022 , Bioinformatics)

   Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction.

   We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively.

   Available at https://github.com/xluo233/RaptorXFold.

   Supplementary data are available at Bioinformatics online.

    

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4. N‐Terminal Protein Labeling with N ‐Hydroxysuccinimide Esters and Microscale Thermophoresis Measurements of Protein‐Protein Interactions Using Labeled Protein

   https://doi.org/10.1002/cpz1.14  

   Jiang, Hanjie ; Cole, Philip A. ( January 2021 , Current Protocols)

   Protein labeling strategies have been explored for decades to study protein structure, function, and regulation. Fluorescent labeling of a protein enables the study of protein‐protein interactions through biophysical methods such as microscale thermophoresis (MST). MST measures the directed motion of a fluorescently labeled protein in response to microscopic temperature gradients, and the protein's thermal mobility can be used to determine binding affinity. However, the stoichiometry and site specificity of fluorescent labeling are hard to control, and heterogeneous labeling can generate inaccuracies in binding measurements. Here, we describe an easy‐to‐apply protocol for high‐stoichiometric, site‐specific labeling of a protein at its N‐terminus withN‐hydroxysuccinimide (NHS) esters as a means to measure protein‐protein interaction affinity by MST. This protocol includes guidelines for NHS ester labeling, fluorescent‐labeled protein purification, and MST measurement using a labeled protein. As an example of the entire workflow, we additionally provide a protocol for labeling a ubiquitin E3 enzyme and testing ubiquitin E2‐E3 enzyme binding affinity. These methods are highly adaptable and can be extended for protein interaction studies in various biological and biochemical circumstances. © 2021 Wiley Periodicals LLC.

   This article was corrected on 18 July 2022. See the end of the full text for details.

   Basic Protocol 1: Labeling a protein of interest at its N‐terminus with NHS esters through stepwise reaction

   Alternate Protocol: Labeling a protein of interest at its N‐terminus with NHS esters through a one‐pot reaction

   Basic Protocol 2: Purifying the N‐terminal fluorescent‐labeled protein and determining its concentration and labeling efficiency

   Basic Protocol 3: Using MST to determine the binding affinity of an N‐terminal fluorescent‐labeled protein to a binding partner.

   Basic Protocol 4: NHS ester labeling of ubiquitin E3 ligase WWP2 and measurement of the binding affinity between WWP2 and an E2 conjugating enzyme by the MST binding assay

    

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5. Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST

   https://doi.org/10.1002/cpz1.494  

   Alnabati, Eman ; Terashi, Genki ; Kihara, Daisuke ( July 2022 , Current Protocols)

   An increasing number of protein structures are determined by cryo‐electron microscopy (cryo‐EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo‐EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is ade novoprotein modeling tool designed for EM maps with resolution of 3–5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree‐graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

   Basic Protocol 1: Protein structure model fitting using VESPER

   Alternate Protocol: Atomic model fitting using VESPER web server

   Basic Protocol 2: Proteinde novomodeling using MAINMAST

    

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