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1. 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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2. Temperature-responsive membrane permeability of recombinant fusion protein vesicles

   https://doi.org/10.1039/D3SM00096F  

   Powers, Jackson; Jang, Yeongseon (May 2023, Soft Matter)

   In this study, we investigate the changes in the permeability of the recombinant fusion protein vesicles with different membrane structures as a function of solution temperature. The protein vesicles are self-assembled from recombinant fusion protein complexes composed of an mCherry fused with a glutamic acid-rich leucine zipper and a counter arginine-rich leucine zipper fused with an elastin-like polypeptide (ELP). We have found that the molecular weight cut-off (MWCO) of the protein vesicle membranes varies inversely with solution temperature by monitoring the transport of fluorescent-tagged dextran dyes with different molecular weights. The temperature-responsiveness of the protein vesicle membranes is obtained from the lower critical solution temperature behavior of ELP in the protein building blocks. Consequently, the unique vesicle membrane structures with different single-layered and double-layered ELP organizations impact the sensitivity of the permeability responses of the protein vesicles. Single-layered protein vesicles with the ELP domains facing the interior show more drastic permeability changes as a function of temperature than double-layered protein vesicles in which ELP blocks are buried inside the membranes. This work about the temperature-responsive membrane permeability of unique protein vesicles will provide design guidelines for new biomaterials and their applications, such as drug delivery and synthetic protocell development. 

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3. Structure and dynamics determine G protein coupling specificity at a class A GPCR

   https://doi.org/10.1101/2024.03.28.587240  

   Casiraghi, Marina; Wang, Haoqing; Brennan, Patrick; Habrian, Chris; Hubner, Harald; Schmidt, Maximilian F; Maul, Luis; Pani, Biswaranjan; Bahriz, Sherif MFM; Xu, Bing; et al (March 2024, bioRxiv)

   G protein coupled receptors (GPCRs) exhibit varying degrees of selectivity for different G protein isoforms. Despite the abundant structures of GPCR-G protein complexes, little is known about the mechanism of G protein coupling specificity. The β2-adrenergic receptor is an example of GPCR with high selectivity for Gαs, the stimulatory G protein for adenylyl cyclase, and much weaker for the Gαi family of G proteins inhibiting adenylyl cyclase. By developing a new Gαi-biased agonist (LM189), we provide structural and biophysical evidence supporting that distinct conformations at ICL2 and TM6 are required for coupling of the different G protein subtypes Gαs and Gαi. These results deepen our understanding of G protein specificity and bias and can accelerate the design of ligands that select for preferred signaling pathways. 

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4. Structural motifs in protein cores and at protein–protein interfaces are different

   https://doi.org/10.1002/pro.3996  

   Hadarovich, Anna; Chakravarty, Devlina; Tuzikov, Alexander V.; Ben‐Tal, Nir; Kundrotas, Petras J.; Vakser, Ilya A. (November 2020, Protein Science)

   Abstract Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking. 

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5. HDXBoxeR: An R package for statistical analysis and visualization of multiple Hydrogen-Deuterium Exchange Mass-Spectrometry datasets of different protein states

   https://doi.org/10.1093/bioinformatics/btae479  

   Janowska, Maria K; Reiter, Katherine; Magala, Pearl; Guttman, Miklos; Klevit, Rachel E (July 2024, Bioinformatics)

   Elofsson, Arne (Ed.)

   Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) is a powerful protein characterization technique that provides insights into protein dynamics and flexibility at the peptide level. However, analyzing HDX-MS data presents a significant challenge due to the wealth of information it generates. Each experiment produces data for hundreds of peptides, often measured in triplicate across multiple time points. Comparisons between different protein states create distinct datasets containing thousands of peptides that require matching, rigorous statistical evaluation, and visualization. Our open-source R package, HDXBoxeR, is a comprehensive tool designed to facilitate statistical analysis and comparison of multiple sets among samples and time points for different protein states, along with data visualization. 

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