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1. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

   https://doi.org/10.3389/fmolb.2023.1110567  

   Shin, Woong-Hee; Kumazawa, Keiko; Imai, Kenichiro; Hirokawa, Takatsugu; Kihara, Daisuke (February 2023, Frontiers in Molecular Biosciences)

   Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer . 

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2. The role of endoplasmic reticulum stress on reducing recombinant protein production in mammalian cells

   https://doi.org/10.1016/j.bej.2024.109434  

   Splichal, R Chauncey; Chen, Kevin; Walton, S Patrick; Chan, Christina (October 2024, Biochemical Engineering Journal)

   Chen, W (Ed.)

   Therapeutic recombinant protein production relies on industrial scale culture of mammalian cells to produce active proteins in quantities sufficient for clinical use. The combination of stresses from industrial cell culture environment and recombinant protein production can overwhelm the protein synthesis machinery in the endoplasmic reticulum (ER). This leads to a buildup of improperly folded proteins which induces ER stress. Cells respond to ER stress by activating the Unfolded Protein Response (UPR). To restore proteostasis, ER sensor proteins reduce global protein synthesis and increase chaperone protein synthesis, and if that is insufficient the proteins are degraded. If proteostasis is still not restored, apoptosis is initiated. Increasing evidence suggests crosstalk between ER proteostasis and DNA damage repair (DDR) pathways. External factors (e.g., metabolites) from the cellular environment as well as internal factors (e.g., transgene copy number) can impact genome stability. Failure to maintain genome integrity reduces cell viability and in turn protein production. This review focuses on the association between ER stress and processes that affect protein production and secretion. The processes mediated by ER stress, including inhibition of global protein translation, chaperone protein production, degradation of misfolded proteins, DNA repair, and protein secretion, impact recombinant protein production. Recombinant protein production can be reduced by ER stress through increased autophagy and protein degradation, reduced protein secretion, and reduced DDR response. 

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3. Unraveling the role of physicochemical differences in predicting protein–protein interactions

   https://doi.org/10.1063/5.0219501  

   Teimouri, Hamid; Medvedeva, Angela; Kolomeisky, Anatoly B (July 2024, The Journal of Chemical Physics)

   The ability to accurately predict protein–protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein–protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein–protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein–protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein–protein interactions with different organisms, our method yields context-specific features that determine protein–protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase–barstar complex, demonstrating the method’s versatility across different biological systems. Our approach can be applied to predict protein–protein interactions in any biological system, providing an important tool for investigating complex biological processes’ mechanisms. 

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4. The Crystal Structure of Thermal Green Protein Q66E (TGP-E) and Yellow Thermostable Protein (YTP-E) E148D

   https://doi.org/10.3390/synbio2030018  

   Anderson, Matthew R; Padgett, Caitlin M; Ogbeifun, Victoria O; DeVore, Natasha M (September 2024, SynBio)

   Thermal green protein Q66E (TGP-E) has previously shown increased thermal stability compared to thermal green protein (TGP), a thermal stable fluorescent protein produced through consensus and surface protein engineering. In this paper, we describe the protein crystal structure of TGP-E to 2.0 Å. This structure reveals alterations in the hydrogen bond network near the chromophore that may result in the observed increase in thermal stability. We compare the very stable TGP-E protein to the structure of a yellow mutant version of this protein YTP-E E148D. The structure of this mutant protein reveals the rationale for the observed low quantum yield and directions for future protein engineering efforts. 

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5. Computational studies of the principle of dynamic-change-driven protein interactions

   https://doi.org/doi.org/10.1016/j.str.2022.03.008  

   Li, Z.; Mattos, C.; Buck, M. (June 2022, Structure)

   Dynamic allostery emphasizes a role of entropy change manifested as a sole change in protein fluctuations without structural changes. This kind of entropy-driven effect remains largely understudied. The most significant examples involve protein-ligand interactions, leaving protein-protein interactions, which are critical in signaling and other cellular events, largely unexplored. Here we study an example of how protein-protein interaction (binding of Ras to the Ras binding domain [RBD] of the effector protein Raf) affects a subsequent protein association process (Ras dimerization) by quenching Ras internal motions through dynamic allostery. We also investigate the influence of point mutations or ambient temperature, respectively, on the protein dynamics and interaction of two other systems: in adenylate kinase (ADK) and in the EphA2 SAM:Ship2 SAM complex. Based on these examples, we postulate that there are different ways in which dynamic-change-driven protein interactions are manifested and that it is likely a general biological phenomenon. 

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