Eukaryotic cells have developed intricate mechanisms for biomolecule transport, particularly in stressful conditions. This interdisciplinary study delves into unconventional protein secretion (UPS) pathways activated during starvation, facilitating the export of proteins bypassing most of the components of the classical secretory machinery. Specifically, we focus on the underexplored mechanisms of the GRASP\'s role in UPS, particularly in biogenesis and cargo recruitment for the vesicular-like compartment for UPS. Our results show that liquid-liquid phase separation (LLPS) plays a key role in the coacervation of Grh1, the GRASP yeast homologue, under starvation-like conditions. This association seems a precursor to the Compartment for Unconventional Protein Secretion (CUPS) biogenesis. Grh1\'s self-association is regulated by electrostatic, hydrophobic, and hydrogen-bonding interactions. Importantly, our study demonstrates that phase-separated states of Grh1 can recruit UPS cargo under starvation-like situations. Additionally, we explore how the coacervate liquid-to-solid transition could impact cells\' ability to return to normal post-stress states. Our findings offer insights into intracellular protein dynamics and cell adaptive responses to stress.

In the last two decades, our existing notion that most foldable proteins have a unique native state has been challenged by the discovery of metamorphic proteins, which reversibly interconvert between multiple, sometimes highly dissimilar, native states. As the number of known metamorphic proteins increases, several computational and experimental strategies have emerged for gaining insights about their refolding processes and identifying unknown metamorphic proteins amongst the known proteome. In this review, we describe the current advances in biophysically and functionally ascertaining the structural interconversions of metamorphic proteins and how coevolution can be harnessed to identify novel metamorphic proteins from sequence information. We also discuss the challenges and ongoing efforts in using artificial intelligence-based protein structure prediction methods to discover metamorphic proteins and predict their corresponding three-dimensional structures.

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

The mitochondrial outer membrane creates a diffusion barrier between the cytosol and the mitochondrial intermembrane space, allowing the exchange of metabolic products, important for efficient mitochondrial function in neurons. The ganglioside-induced differentiation-associated protein 1 (GDAP1) is a mitochondrial outer membrane protein with a critical role in mitochondrial dynamics and metabolic balance in neurons. Missense mutations in the GDAP1 gene are linked to the most common human peripheral neuropathy, Charcot-Marie-Tooth disease (CMT). GDAP1 is a distant member of the glutathione-S-transferase (GST) superfamily, with unknown enzymatic properties or functions at the molecular level. The structure of the cytosol-facing GST-like domain has been described, but there is no consensus on how the protein interacts with the mitochondrial outer membrane. Here, we describe a model for GDAP1 assembly on the membrane using peptides vicinal to the GDAP1 transmembrane domain. We used oriented circular dichroism spectroscopy (OCD) with synchrotron radiation to study the secondary structure and orientation of GDAP1 segments at the outer and inner surfaces of the outer mitochondrial membrane. These experiments were complemented by small-angle X-ray scattering, providing the first experimental structural models for full-length human GDAP1. The results indicate that GDAP1 is bound into the membrane via a single transmembrane helix, flanked by two peripheral helices interacting with the outer and inner leaflets of the mitochondrial outer membrane in different orientations. Impairment of these interactions could be a mechanism for CMT in the case of missense mutations affecting these segments instead of the GST-like domain.

The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular processes involved in cancer cell pathology. However, the complexity of protein sequences and the diversity of conformations are inherently disordered, which poses great challenges for LLPS protein calculations and experimental research. Herein, we proposed a novel predictor named PredLLPS_PSSM for LLPS protein identification based only on sequence evolution information. Because finding real and reliable samples is the cornerstone of building predictors, we collected anew and collated the LLPS proteins from the latest versions of three databases. By comparing the performance of the position-specific score matrix (PSSM) and word embedding, PredLLPS_PSSM combined PSSM-based information and two deep learning frameworks. Independent tests using three existing independent test datasets and two newly constructed independent test datasets demonstrated the superiority of PredLLPS_PSSM compared with state-of-the-art methods. Furthermore, we tested PredLLPS_PSSM on nine experimentally identified LLPS proteins from three insects that were not included in any of the databases. In addition, the powerful Shapley Additive exPlanation algorithm and heatmap were applied to find the most critical amino acids relevant to LLPS.

Bacterial outer membrane vesicles (OMVs) can be selectively enriched with one or more outer membrane proteins to allow the biophysical characterization of these membrane proteins embedded in the native cellular environment. Unlike reconstituted artificial membrane environments, OMVs maintain the native lipid composition as well as the lipid asymmetry of bacterial outer membranes. Here, we describe in detail the steps necessary to prepare OMVs, which contain high levels of a designated protein of interest, and which are of sufficient homogeneity and purity to perform biophysical characterizations using high-resolution methods such as atomic force microscopy, electron microscopy, or single-molecule force spectroscopy.

Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of proteins to form condensates have been proposed, with some of them probed experimentally through the use of constructs generated by sequence alterations. To broaden the scope of these observations, we established an in silico strategy for understanding on a global level the associations between protein sequence and phase behavior and further constructed machine-learning models for predicting protein liquid-liquid phase separation (LLPS). Our analysis highlighted that LLPS-prone proteins are more disordered, less hydrophobic, and of lower Shannon entropy than sequences in the Protein Data Bank or the Swiss-Prot database and that they show a fine balance in their relative content of polar and hydrophobic residues. To further learn in a hypothesis-free manner the sequence features underpinning LLPS, we trained a neural network-based language model and found that a classifier constructed on such embeddings learned the underlying principles of phase behavior at a comparable accuracy to a classifier that used knowledge-based features. By combining knowledge-based features with unsupervised embeddings, we generated an integrated model that distinguished LLPS-prone sequences both from structured proteins and from unstructured proteins with a lower LLPS propensity and further identified such sequences from the human proteome at a high accuracy. These results provide a platform rooted in molecular principles for understanding protein phase behavior. The predictor, termed DeePhase, is accessible from https://deephase.ch.cam.ac.uk/.

S100 proteins assume a diversity of oligomeric states including large order self-assemblies, with an impact on protein structure and function. Previous work has uncovered that S100 proteins, including S100B, are prone to undergo β-aggregation under destabilizing conditions. This propensity is encoded in aggregation-prone regions (APR) mainly located in segments at the homodimer interface, and which are therefore mostly shielded from the solvent and from deleterious interactions, under native conditions. As in other systems, this characteristic may be used to develop peptides with pharmacological potential that selectively induce the aggregation of S100B through homotypic interactions with its APRs, resulting in functional inhibition through a loss of function. Here we report initial studies towards this goal. We applied the TANGO algorithm to identify specific APR segments in S100B helix IV and used this information to design and synthesize S100B-derived APR peptides. We then combined fluorescence spectroscopy, transmission electron microscopy, biolayer interferometry, and aggregation kinetics and determined that the synthetic peptides have strong aggregation propensity, interact with S100B, and may promote co-aggregation reactions. In this framework, we discuss the considerable potential of such APR-derived peptides to act pharmacologically over S100B in numerous physiological and pathological conditions, for instance as modifiers of the S100B interactome or as promoters of S100B inactivation by selective aggregation.

In order to preserve structure and function, proteins tend to preferentially conserve amino acids at particular sites along the sequence. Because mutations can affect structure and function, the question arises whether the preference of a protein site for a particular amino acid varies between protein homologs, and to what extent that variation depends on sequence divergence. Answering these questions can help in the development of models of sequence evolution, as well as provide insights on the dependence of the fitness effects of mutations on the genetic background of sequences, a phenomenon known as epistasis. Here, I comment on recent computational work providing a systematic analysis of the extent to which the amino acid preferences of proteins depend on the background mutations of protein homologs.

Microfluidics has the potential to transform experimental approaches across the life sciences. In this review, we discuss recent advances enabled by the development and application of microfluidic approaches to protein biophysics. We focus on areas where key fundamental features of microfluidics open up new possibilities and present advantages beyond low volumes and short time-scale analysis, conventionally provided by microfluidics. We discuss the two most commonly used forms of microfluidic technology, single-phase laminar flow and multiphase microfluidics. We explore how the understanding and control of the characteristic physical features of the microfluidic regime, the integration of microfluidics with orthogonal systems and the generation of well-defined microenvironments can be used to develop novel devices and methods in protein biophysics for sample manipulation, functional and structural studies, detection and material processing.