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1. The intracellular environment affects protein–protein interactions

   https://doi.org/10.1073/pnas.2019918118  

   Speer, Shannon L.; Zheng, Wenwen; Jiang, Xin; Chu, I-Te; Guseman, Alex J.; Liu, Maili; Pielak, Gary J.; Li, Conggang (March 2021, Proceedings of the National Academy of Sciences)

   Protein–protein interactions are essential for life but rarely thermodynamically quantified in living cells. In vitro efforts show that protein complex stability is modulated by high concentrations of cosolutes, including synthetic polymers, proteins, and cell lysates via a combination of hard-core repulsions and chemical interactions. We quantified the stability of a model protein complex, the A34F GB1 homodimer, in buffer,Escherichia colicells andXenopus laevisoocytes. The complex is more stable in cells than in buffer and more stable in oocytes thanE. coli. Studies of several variants show that increasing the negative charge on the homodimer surface increases stability in cells. These data, taken together with the fact that oocytes are less crowded thanE. colicells, lead to the conclusion that chemical interactions are more important than hard-core repulsions under physiological conditions, a conclusion also gleaned from studies of protein stability in cells. Our studies have implications for understanding how promiscuous—and specific—interactions coherently evolve for a protein to properly function in the crowded cellular environment. 

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2. Photo-Switching of Protein Dynamical Collectivity

   https://doi.org/10.3390/photonics8080302  

   Xu, Mengyang; George, Deepu; Jimenez, Ralph; Markelz, Andrea (August 2021, Photonics)

   We examine changes in the picosecond structural dynamics with irreversible photobleaching of red fluorescent proteins (RFP) mCherry, mOrange2 and TagRFP-T. Measurements of the protein dynamical transition using terahertz time-domain spectroscopy show in all cases an increase in the turn-on temperature in the bleached state. The result is surprising given that there is little change in the protein surface, and thus, the solvent dynamics held responsible for the transition should not change. A spectral analysis of the measurements guided by quasiharmonic calculations of the protein absorbance reveals that indeed the solvent dynamical turn-on temperature is independent of the thermal stability/photostate however the protein dynamical turn-on temperature shifts to higher temperatures. This is the first demonstration of switching the protein dynamical turn-on temperature with protein functional state. The observed shift in protein dynamical turn-on temperature relative to the solvent indicates an increase in the required mobile waters necessary for the protein picosecond motions, that is, these motions are more collective. Melting-point measurements reveal that the photobleached state is more thermally stable, and structural analysis of related RFP’s shows that there is an increase in internal water channels as well as a more uniform atomic root mean squared displacement. These observations are consistent with previous suggestions that water channels form with extended light excitation providing O2 access to the chromophore and subsequent fluorescence loss. We report that these same channels increase internal coupling enhancing thermal stability and collectivity of the picosecond protein motions. The terahertz spectroscopic characterization of the protein and solvent dynamical onsets can be applied generally to measure changes in collectivity of protein motions. 

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3. Biophysics-based protein language models for protein engineering

   https://doi.org/10.1038/s41592-025-02776-2  

   Gelman, Sam; Johnson, Bryce; Freschlin, Chase R; Sharma, Arnav; D’Costa, Sameer; Peters, John; Gitter, Anthony; Romero, Philip A (September 2025, Nature Methods)

   Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence–function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL’s ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering. 

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4. Dual-Crystallizable Silk Fibroin/Poly(L-lactic Acid) Biocomposite Films: Effect of Polymer Phases on Protein Structures in Protein-Polymer Blends

   https://doi.org/10.3390/ijms22041871  

   Wang, Fang; Li, Yingying; Gough, Christopher R.; Liu, Qichun; Hu, Xiao (February 2021, International Journal of Molecular Sciences)

   null (Ed.)

   Biopolymer composites based on silk fibroin have shown widespread potential due to their brilliant applications in tissue engineering, medicine and bioelectronics. In our present work, biocomposite nanofilms with different special topologies were obtained through blending silk fibroin with crystallizable poly(L-lactic acid) (PLLA) at various mixture rates using a stirring-reflux condensation blending method. The microstructure, phase components, and miscibility of the blended films were studied through thermal analysis in combination with Fourier-transform infrared spectroscopy and Raman analysis. X-ray diffraction and scanning electron microscope were also used for advanced structural analysis. Furthermore, their conformation transition, interaction mechanism, and thermal stability were also discussed. The results showed that the hydrogen bonds and hydrophobic interactions existed between silk fibroin (SF) and PLLA polymer chains in the blended films. The secondary structures of silk fibroin and phase components of PLLA in composites vary at different ratios of silk to PLLA. The β-sheet content increased with the increase of the silk fibroin content, while the glass transition temperature was raised mainly due to the rigid amorphous phase presence in the blended system. This results in an increase in thermal stability in blended films compared to the pure silk fibroin films. This study provided detailed insights into the influence of synthetic polymer phases (crystalline, rigid amorphous, and mobile amorphous) on protein secondary structures through blending, which has direct applications on the design and fabrication of novel protein–synthetic polymer composites for the biomedical and green chemistry fields. 

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5. Exploring the effect of network topology, mRNA and protein dynamics on gene regulatory network stability

   https://doi.org/10.1038/s41467-020-20472-x  

   Guo, Yipei; Amir, Ariel (December 2021, Nature Communications)

   Abstract Homeostasis of protein concentrations in cells is crucial for their proper functioning, requiring steady-state concentrations to be stable to fluctuations. Since gene expression is regulated by proteins such as transcription factors (TFs), the full set of proteins within the cell constitutes a large system of interacting components, which can become unstable. We explore factors affecting stability by coupling the dynamics of mRNAs and proteins in a growing cell. We find that mRNA degradation rate does not affect stability, contrary to previous claims. However, global structural features of the network can dramatically enhance stability. Importantly, a network resembling a bipartite graph with a lower fraction of interactions that target TFs has a higher chance of being stable. Scrambling the E. coli transcription network, we find that the biological network is significantly more stable than its randomized counterpart, suggesting that stability constraints may have shaped network structure during the course of evolution. 

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