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1. Ubiquitin‐Modulated Phase Separation of Shuttle Proteins: Does Condensate Formation Promote Protein Degradation?

   https://doi.org/10.1002/bies.202000036  

   Dao, Thuy_P; Castañeda, Carlos_A (September 2020, BioEssays)

   Abstract Liquid‐liquid phase separation (LLPS) has recently emerged as a possible mechanism that enables ubiquitin‐binding shuttle proteins to facilitate the degradation of ubiquitinated substrates via distinct protein quality control (PQC) pathways. Shuttle protein LLPS is modulated by multivalent interactions among their various domains as well as heterotypic interactions with polyubiquitin chains. Here, the properties of three different shuttle proteins (hHR23B, p62, and UBQLN2) are closely examined, unifying principles for the molecular determinants of their LLPS are identified, and how LLPS is connected to their functions is discussed. Evidence supporting LLPS of other shuttle proteins is also found. In this review, it is proposed that shuttle protein LLPS leads to spatiotemporal regulation of PQC activities by mediating the recruitment of PQC machinery (including proteasomes or autophagic components) to biomolecular condensates, assembly/disassembly of condensates, selective enrichment of client proteins, and extraction of ubiquitinated proteins from condensates in cells. 

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2. Protein-Based Hydrogels and Their Biomedical Applications

   https://doi.org/10.3390/molecules28134988  

   Lee, Kok Zhi; Jeon, Juya; Jiang, Bojing; Subramani, Shri Venkatesh; Li, Jingyao; Zhang, Fuzhong (July 2023, Molecules)

   Hydrogels made from proteins are attractive materials for diverse medical applications, as they are biocompatible, biodegradable, and amenable to chemical and biological modifications. Recent advances in protein engineering, synthetic biology, and material science have enabled the fine-tuning of protein sequences, hydrogel structures, and hydrogel mechanical properties, allowing for a broad range of biomedical applications using protein hydrogels. This article reviews recent progresses on protein hydrogels with special focus on those made of microbially produced proteins. We discuss different hydrogel formation strategies and their associated hydrogel properties. We also review various biomedical applications, categorized by the origin of protein sequences. Lastly, current challenges and future opportunities in engineering protein-based hydrogels are discussed. We hope this review will inspire new ideas in material innovation, leading to advanced protein hydrogels with desirable properties for a wide range of biomedical applications. 

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3. Orthogonal Crosslinking: A Strategy to Generate Novel Protein Topology and Function

   https://doi.org/10.1002/chem.202202828  

   Wang, Zheng; Rabb, Johnathan_D; Lin, Qing (November 2022, Chemistry – A European Journal)

   Abstract Compared to the disulfide bond, other naturally occurring intramolecular crosslinks have received little attention, presumably due to their rarity in the vast protein space. Here we presented examples of natural non‐disulfide crosslinks, which we refer to as orthogonal crosslinks, emphasizing their effect on protein topology and function. We summarize recent efforts on expanding orthogonal crosslinks by using either the enzymes that catalyze protein circularization or the genetic code expansion strategy to add electrophilic amino acids site‐specifically in proteins. The advantages and disadvantages of each method are discussed, along with their applications to generate novel protein topology and function. In particular, we highlight our recent work on spontaneous orthogonal crosslinking, in which a carbamate‐based crosslink was generated in situ, and its applications in designing orthogonally crosslinked domain antibodies with their topology‐mimicking bacterial adhesins. 

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4. Pathogenic mutation hotspots in protein kinase domain structure

   https://doi.org/10.1002/pro.4750  

   Medvedev, Kirill E.; Schaeffer, R. Dustin; Pei, Jimin; Grishin, Nick V. (August 2023, Protein Science)

   Abstract Control of eukaryotic cellular function is heavily reliant on the phosphorylation of proteins at specific amino acid residues, such as serine, threonine, tyrosine, and histidine. Protein kinases that are responsible for this process comprise one of the largest families of evolutionarily related proteins. Dysregulation of protein kinase signaling pathways is a frequent cause of a large variety of human diseases including cancer, autoimmune, neurodegenerative, and cardiovascular disorders. In this study, we mapped all pathogenic mutations in 497 human protein kinase domains from the ClinVar database to the reference structure of Aurora kinase A (AURKA) and grouped them by the relevance to the disease type. Our study revealed that the majority of mutation hotspots associated with cancer are situated within the catalytic and activation loops of the kinase domain, whereas non‐cancer‐related hotspots tend to be located outside of these regions. Additionally, we identified a hotspot at residue R371 of the AURKA structure that has the highest number of exclusively non‐cancer‐related pathogenic mutations (21) and has not been previously discussed. 

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5. Improving protein function prediction by learning and integrating representations of protein sequences and function labels

   https://doi.org/10.1093/bioadv/vbae120  

   Boadu, Frimpong; Cheng, Jianlin; Zhu, ed., Shanfeng (August 2024, Bioinformatics Advances)

   Abstract MotivationAs fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. ResultsWe introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms. Availability and implementationhttps://github.com/BioinfoMachineLearning/TransFew. 

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