Electric Fields and Enzyme Catalysis.

What happens inside an enzyme's active site to allow slow and difficult chemical reactions to occur so rapidly? This question has occupied biochemists' attention for a long time. Computer models of increasing sophistication have predicted an important role for electrostatic interactions in enzymatic reactions, yet this hypothesis has proved vexingly difficult to test experimentally. Recent experiments utilizing the vibrational Stark effect make it possible to measure the electric field a substrate molecule experiences when bound inside its enzyme's active site. These experiments have provided compelling evidence supporting a major electrostatic contribution to enzymatic catalysis. Here, we review these results and develop a simple model for electrostatic catalysis that enables us to incorporate disparate concepts introduced by many investigators to describe how enzymes work into a more unified framework stressing the importance of electric fields at the active site.

PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network.

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.

Mutational biases favor complexity increases in protein interaction networks after gene duplication.

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.

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter.

The Amino Acid-Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced modification of AlphaFold2 . Our experimental results reveal acid-induced conformational changes that dislodge the Cterminus from the permeation pathway coupled with rearrangement of helices that enables isomerization between inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminates the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different families in the LeuT-fold.

Learning the molecular grammar of protein condensates from sequence determinants and embeddings.

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/.

Targeting S100B with Peptides Encoding Intrinsic Aggregation-Prone Sequence Segments.

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 ß-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.

Active Site-Induced Evolutionary Constraints Follow Fold Polarity Principles in Soluble Globular Enzymes.

A recent analysis of evolutionary rates in >500 globular soluble enzymes revealed pervasive conservation gradients toward catalytic residues. By looking at amino acid preference profiles rather than evolutionary rates in the same data set, we quantified the effects of active sites on site-specific constraints for physicochemical traits. We found that conservation gradients respond to constraints for polarity, hydrophobicity, flexibility, rigidity and structure in ways consistent with fold polarity principles; while sites far from active sites seem to experience no physicochemical constraint, rather being highly variable and favoring amino acids of low metabolic cost. Globally, our results highlight that amino acid variation contains finer information about protein structure than usually regarded in evolutionary models, and that this information is retrievable automatically with simple fits. We propose that analyses of the kind presented here incorporated into models of protein evolution should allow for better description of the physical chemistry that underlies molecular evolution.

Detection and sequence/structure mapping of biophysical constraints to protein variation in saturated mutational libraries and protein sequence alignments with a dedicated server.

BACKGROUND: Protein variability can now be studied by measuring high-resolution tolerance-to-substitution maps and fitness landscapes in saturated mutational libraries. But these rich and expensive datasets are typically interpreted coarsely, restricting detailed analyses to positions of extremely high or low variability or dubbed important beforehand based on existing knowledge about active sites, interaction surfaces, (de)stabilizing mutations, etc. RESULTS: Our new webserver PsychoProt (freely available without registration at http://psychoprot.epfl.ch or at http://lucianoabriata.altervista.org/psychoprot/index.html ) helps to detect, quantify, and sequence/structure map the biophysical and biochemical traits that shape amino acid preferences throughout a protein as determined by deep-sequencing of saturated mutational libraries or from large alignments of naturally occurring variants. DISCUSSION: We exemplify how PsychoProt helps to (i) unveil protein structure-function relationships from experiments and from alignments that are consistent with structures according to coevolution analysis, (ii) recall global information about structural and functional features and identify hitherto unknown constraints to variation in alignments, and (iii) point at different sources of variation among related experimental datasets or between experimental and alignment-based data. Remarkably, metabolic costs of the amino acids pose strong constraints to variability at protein surfaces in nature but not in the laboratory. This and other differences call for caution when extrapolating results from in vitro experiments to natural scenarios in, for example, studies of protein evolution. CONCLUSION: We show through examples how PsychoProt can be a useful tool for the broad communities of structural biology and molecular evolution, particularly for studies about protein modeling, evolution and design.

Protein hydrogen exchange at residue resolution by proteolytic fragmentation mass spectrometry analysis.

Hydrogen exchange technology provides a uniquely powerful instrument for measuring protein structural and biophysical properties, quantitatively and in a nonperturbing way, and determining how these properties are implemented to produce protein function. A developing hydrogen exchange-mass spectrometry method (HX MS) is able to analyze large biologically important protein systems while requiring only minuscule amounts of experimental material. The major remaining deficiency of the HX MS method is the inability to deconvolve HX results to individual amino acid residue resolution. To pursue this goal we used an iterative optimization program (HDsite) that integrates recent progress in multiple peptide acquisition together with previously unexamined isotopic envelope-shape information and a site-resolved back-exchange correction. To test this approach, residue-resolved HX rates computed from HX MS data were compared with extensive HX NMR measurements, and analogous comparisons were made in simulation trials. These tests found excellent agreement and revealed the important computational determinants.

Rational modification of protein stability by targeting surface sites leads to complicated results.

The rational modification of protein stability is an important goal of protein design. Protein surface electrostatic interactions are not evolutionarily optimized for stability and are an attractive target for the rational redesign of proteins. We show that surface charge mutants can exert stabilizing effects in distinct and unanticipated ways, including ones that are not predicted by existing methods, even when only solvent-exposed sites are targeted. Individual mutation of three solvent-exposed lysines in the villin headpiece subdomain significantly stabilizes the protein, but the mechanism of stabilization is very different in each case. One mutation destabilizes native-state electrostatic interactions but has a larger destabilizing effect on the denatured state, a second removes the desolvation penalty paid by the charged residue, whereas the third introduces unanticipated native-state interactions but does not alter electrostatics. Our results show that even seemingly intuitive mutations can exert their effects through unforeseen and complex interactions.

A Genetically Encoded ß-Lactamase Reporter for Ultrasensitive (129) Xe NMR in Mammalian Cells.

Molecular imaging holds considerable promise for elucidating biological processes in normal physiology as well as disease states, but requires noninvasive methods for identifying analytes at sub-micromolar concentrations. Particularly useful are genetically encoded, single-protein reporters that harness the power of molecular biology to visualize specific molecular processes, but such reporters have been conspicuously lacking for in vivo magnetic resonance imaging (MRI). Herein, we report TEM-1 ß-lactamase (bla) as a single-protein reporter for hyperpolarized (HP) (129) Xe NMR, with significant saturation contrast at 0.1 µm. Xenon chemical exchange saturation transfer (CEST) interactions with the primary allosteric site in bla give rise to a unique saturation peak at 255 ppm, well removed (≈60 ppm downfield) from the (129) Xe-H2 O peak. Useful saturation contrast was also observed for bla expressed in bacterial cells and mammalian cells.

Metamorphic proteins and how to find them.

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.

The potential role of liquid-liquid phase separation in the cellular fate of the compartments for unconventional protein secretion.

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.

Structure and Function of Auxin Transporters.

Auxins, a group of central hormones in plant growth and development, are transported by a diverse range of transporters with distinct biochemical and structural properties. This review summarizes the current knowledge on all known auxin transporters with respect to their biochemical and biophysical properties and the methods used to characterize them. In particular, we focus on the recent advances that were made concerning the PIN-FORMED family of auxin exporters. Insights derived from solving their structures have improved our understanding of the auxin export process, and we discuss the current state of the art on PIN-mediated auxin transport, including the use of biophysical methods to examine their properties. Understanding the mechanisms of auxin transport is crucial for understanding plant growth and development, as well as for the development of more effective strategies for crop production and plant biotechnology.

Conformational analysis of membrane-proximal segments of GDAP1 in a lipidic environment using synchrotron radiation suggests a mode of assembly at the mitochondrial outer membrane.

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.

Preparation of Protein-Enriched Outer Membrane Vesicles from Escherichia Coli for In Situ Structural Biology of Outer Membrane Proteins.

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.

Reconstitution of Membrane Proteins into Platforms Suitable for Biophysical and Structural Analyses.

Integral membrane proteins have historically been challenging targets for biophysical research due to their low solubility in aqueous solution. Their importance for chemical and electrical signaling between cells, however, makes them fascinating targets for investigators interested in the regulation of cellular and physiological processes. Since membrane proteins shunt the barrier imposed by the cell membrane, they also serve as entry points for drugs, adding pharmaceutical research and development to the interests. In recent years, detailed understanding of membrane protein function has significantly increased due to high-resolution structural information obtained from single-particle cryo-EM, X-ray crystallography, and NMR. In order to further advance our mechanistic understanding on membrane proteins as well as foster drug development, it is crucial to generate more biophysical and functional data on these proteins under defined conditions. To that end, different techniques have been developed to stabilize integral membrane proteins in native-like environments that allow both structural and biophysical investigations-amphipols, lipid bicelles, and lipid nanodiscs. In this chapter, we provide detailed protocols for the reconstitution of membrane proteins according to these three techniques. We also outline some of the possible applications of each technique and discuss their advantages and possible caveats.

Gene Families, Epistasis and the Amino Acid Preferences of Protein Homologs.

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 for Protein Biophysics.

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.