Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

Engineering a thermophilic luciferase variant from Photuris pennsylvanica into a mesophilic-like enzyme for expanded applications potential

Luciferase, known for its exceptional catalytic bioluminescent properties, has been widely utilized in diverse applications within biotechnology and medical research. Currently, enhancing thermostability and catalytic activity is a primary focus for optimizing luciferase modifications to further expand its detection range and accuracy. This study revealed a highly thermostable luciferase variant from Photuris pennsylvanica, Ppe146-1H2, which inherently exhibits thermophilic enzyme characteristics that are not conducive for optimal catalytic performance in practical applications. Building upon structural analysis, this research engineered Ppe146-1H2 into Ppe146-LGR via the residue substitutions I422L, D435G, and I519R. Ppe146-LGR retained notably thermostability, exhibiting a melting temperature (Tm value) of 75.3 ± 0.3 °C. Additionally, the variant demonstrated efficient catalytic activity at moderate temperatures, exhibiting 3.8 and 3.7-fold higher catalytic efficiencies towards D-luciferin and ATP at 37 °C compared to Ppe146-1H2. Overall, Ppe146-LGR displayed mesophilic-like catalytic activity and thermophilic-like thermostability simultaneously. In addition to enhanced catalytic properties, Ppe146-LGR emitted longer-wavelength light (580 nm) and operated optimally at near-neutral pH, coordinating with the current demands of luciferase applications. Through validation via rapid bacterial detection and reporter gene assays, it has been demonstrated that Ppe146-LGR holds promise as a valuable tool in the field of bioluminescence technology.

Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction

In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.

Comparison of structural networks across homologous proteins

Protein sequence determines its structure and function. The indirect relationship between protein function and structure lies deep-rooted in the structural topology that has evolved into performing optimal function. The evolution of structure and its interconnectivity has been conventionally studied by comparing the root means square deviation between protein structures at the backbone level. Two factors that are necessary for the quantitative comparison of non-covalent interactions are (a) explicit inclusion of the coordinates of side-chain atoms and (b) consideration of multiple structures from the conformational landscape to account for structural variability. We have recently addressed these fundamental issues by investigating the alteration of inter-residue interactions across an ensemble of protein structure networks through a graph spectral approach. In this study, we have developed a rigorous method to compare the structure networks of homologous proteins, with a wide range of sequence identity percentages. A range of dissimilarity measures that show the extent of change in the network across homologous structures are generated, which also includes the comparison of the protein structure variability. We discuss in detail, scenarios where the variation of structure is not accompanied by loss or gain of the overall network and its vice versa. The sequence-based phylogeny among the homologs is also compared with the lineage obtained from information from such a robust structure comparison. In summary, we can obtain a quantitative comparison score for the structure networks of homologous proteins, which also enables us to study the evolution of protein function based on the variation of their topologies.

Revealing Missing Protein-Ligand Interactions Using AlphaFold Predictions

Protein-ligand interactions represent an essential step to understand the bases of molecular recognition, an intense field of research in many scientific areas. Structural biology has played a central role in unveiling protein-ligand interactions, but current techniques are still not able to reliably describe the interactions of ligands with highly flexible regions. In this work, we explored the capacity of AlphaFold2 (AF2) to estimate the presence of interactions between ligands and residues belonging to disordered regions. As these interactions are missing in the crystallographic-derived structures, we called them "ghost interactions". Using a set of protein structures experimentally obtained after AF2 was trained, we found that the obtained models are good predictors of regions associated with order-disorder transitions. Additionally, we found that AF2 predicts residues making ghost interactions with ligands, which are mostly buried and show differential evolutionary conservation with the rest of the residues located in the flexible region. Our findings could fuel current areas of research that consider, given their biological relevance and their involvement in diseases, intrinsically disordered proteins as potentially valuable targets for drug development.

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations

Intrinsically disordered proteins (IDPs) are closely associated with a number of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Due to the highly dynamic nature of IDPs, their structural determination and conformational exploration pose significant challenges for both experimental and computational research. Recently, the integration of machine learning with molecular dynamics (MD) simulations has emerged as a promising methodology for efficiently exploring the conformation spaces of IDPs. In this viewpoint, we briefly review recently developed autoencoder-based models designed to enhance the conformational exploration of IDPs through embedding and latent sampling. We highlight the capability of autoencoders in expanding the conformations sampled by MD simulations and discuss their limitations due to the non-Gaussian latent space distribution and the limited conformational diversity of training conformations. Potential strategies to overcome these limitations are also discussed.

RNA adapts its flexibility to efficiently fold and resist unfolding

Recent studies have demonstrated that the mechanisms through which biopolymers like RNA interconvert between multiple folded structures are critical for their cellular functions. A major obstacle to elucidating these mechanisms is the lack of experimental approaches that can resolve these interconversions between functionally relevant biomolecular structures. Here, we dissect the complete set of structural rearrangements executed by an ultra-stable RNA, the UUCG stem-loop, at the single-molecule level using a nano-electronic device with microsecond time resolution. We show that the stem-loop samples at least four conformations along two folding pathways leading to two distinct folded structures, only one of which has been previously observed. By modulating its flexibility, the stem-loop can adaptively select between these pathways, enabling it to both fold rapidly and resist unfolding. This paradigm of stabilization through compensatory changes in flexibility broadens our understanding of stable RNA structures and is expected to serve as a general strategy employed by all biopolymers.

Pathogenic Mutation ΔK280 Promotes Hydrophobic Interactions Involving Microtubule-Binding Domain and Enhances Liquid-Liquid Phase Separation of Tau

Liquid-liquid phase separation (LLPS) of tau protein can initiate its aggregation which is associated with Alzheimer's disease. The pathogenic mutation ΔK280 can enhance the aggregation of K18, a truncated tau variant comprising the microtubule-binding domain. However, the impact of ΔK280 on K18 LLPS and underlying mechanisms are largely unexplored. Herein, the conformational ensemble and LLPS of ΔK280 K18 through multiscale molecular simulations and microscopy experiments are investigated. All-atom molecular dynamic simulations reveal that ΔK280 significantly enhances the collapse degree and β-sheet content of the K18 monomer, indicating that ΔK280 mutation may promote K18 LLPS, validated by coarse-grained phase-coexistence simulations and microscopy experiments. Importantly, ΔK280 mutation promotes β-sheet formation of six motifs (especially PHF6), increases the hydrophobic solvent exposure of PHF6* and PHF6, and enhances hydrophobic, hydrogen bonding, and cation-π interactions involving most of the motifs, thus facilitating the phase separation of K18. Notably, ΔK280 alters the interaction network among the six motifs, inducing the formation of K18 conformations with high β-sheet contents and collapse degree. Coarse-grained simulations on full-length tau reveal that ΔK280 promotes tau LLPS by enhancing the hydrophobic interactions involving the microtubule-binding domain. These findings offer detailed mechanistic insights into ΔK280-induced tau pathogenesis, providing potential targets for therapeutic intervention.

Conformational analysis of the IQSEC2 protein by statistical thermodynamics

Mutations in the IQSEC2 gene result in severe intellectual disability, epilepsy and autism. The primary function of IQSEC2 is to serve as a guanine exchange factor (GEF) controlling the activation of ARF6 which in turn mediates membrane trafficking and synaptic connections between neurons. As IQSEC2 is a large intrinsically disordered protein little is known of the structure of the protein and how this influences its function. Understanding this structure and function relationship is critical for the development of novel therapies to treat IQSEC2 disease. We therefore sought to identify IQSEC2 conformers in unfolded and folded states and analyze how conformers differ when binding to ARF6 and thereby influence GEF catalysis. We simulated the folding process of IQSEC2 by accelerated molecular dynamics (aMD). Following the ensemble method of Gibbs, we proposed that the number of microstates in the ensemble replicating a protein macroscopic system is the total number of MD snapshots sampled on the production MD trajectory. We divided the entire range of reaction coordinate into a series of consecutive, non-overlapping bins. Thermal fluctuations of biomolecules in local equilibrium states are Gaussian in form. To predict the free energy and entropy of different conformational states using statistical thermodynamics, the density of states was estimated taking into account how many MD snapshots constitute each conformational state. IQSEC2 dimers derived from the most stable folded and unfolded conformers of IQSEC2 were generated by protein-protein docking and then used to construct IQSEC2-ARF6 encounter complexes. We suggest that IQSEC2 folding and dimerization are two competing processes that may be used by nature to regulate the process of GDP exchange on ARF6 catalyzed by IQSEC2.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.

A low-barrier proton shared between two aspartates acts as a conformational switch that changes the substrate specificity of the β-lactamase BlaC

Serine β-lactamases inactivate β-lactam antibiotics in a two-step mechanism comprising acylation and deacylation. For the deacylation step, a water molecule is activated by a conserved glutamate residue to release the adduct from the enzyme. The third-generation cephalosporin ceftazidime is a poor substrate for the class A β-lactamase BlaC from Mycobacterium tuberculosis but it can be hydrolyzed faster when the active site pocket is enlarged, as was reported for mutant BlaC P167S. The conformational change in the Ω-loop of the P167S mutant displaces the conserved glutamate (Glu166), suggesting it is not required for deacylation of the ceftazidime adduct. Here, we report the characterization of wild type BlaC and BlaC E166A at various pH values. The presence of Glu166 strongly enhances activity against nitrocefin but not ceftazidime, indicating it is indeed not required for deacylation of the adduct of the latter substrate. At high pH wild type BlaC was found to exist in two states, one of which converts ceftazidime much faster, resembling the open state previously reported for the BlaC mutant P167S. The pH-dependent switch between the closed and open states is caused by the loss at high pH of a low-barrier hydrogen bond, a proton shared between Asp172 and Asp179. These results illustrate how readily shifts in substrate specificity can occur as a consequence of subtle changes in protein structure.

Structural and biophysical dissection of RNA conformational ensembles

RNA's ability to form and interconvert between multiple secondary and tertiary structures is critical to its functional versatility and the traditional view of RNA structures as static entities has shifted towards understanding them as dynamic conformational ensembles. In this review we discuss RNA structural ensembles and their dynamics, highlighting the concept of conformational energy landscapes as a unifying framework for understanding RNA processes such as folding, misfolding, conformational changes, and complex formation. Ongoing advancements in cryo-electron microscopy and chemical probing techniques are significantly enhancing our ability to investigate multiple structures adopted by conformationally dynamic RNAs, while traditional methods such as nuclear magnetic resonance spectroscopy continue to play a crucial role in providing high-resolution, quantitative spatial and temporal information. We discuss how these methods, when used synergistically, can provide a comprehensive understanding of RNA conformational ensembles, offering new insights into their regulatory functions.

Insights into the specific feature of the electrostatic recognition binding mechanism between BM2 and BM1: a molecular dynamics simulation study

Matrix protein 2 (M2) and matrix protein 1 (M1) of the influenza B virus are two important proteins, and the interactions between BM2 and BM1 play an important role in the process of virus assembly and replication. However, the interaction details between BM2 and BM1 are still unclear at the atomic level. Here, we constructed the BM2-BM1 complex system using homology modelling and molecular docking methods. Molecular dynamics (MD) simulations were used to illustrate the binding mechanism between BM2 and BM1. The results identify that the eight polar residues (E88B, E89B, H119BM1, E94B, R101BM1, K102BM1, R105BM1, and E104B) play an important role in stabilizing the binding through the formation of hydrogen bond networks and salt-bridge interactions at the binding interface. Furthermore, based on the simulation results and the experimental facts, the mutation experiments were designed to verify the influence of the mutation of residues both within and outside the effector domain. The mutations directly or indirectly disrupt interactions between polar residues, thus affecting viral assembly and replication. The results could help us understand the details of the interactions between BM2 and BM1 and provide useful information for the anti-influenza drug design.

Computational investigations of potential inhibitors of monkeypox virus envelope protein E8 through molecular docking and molecular dynamics simulations

The World Health Organization (WHO) has declared the monkeypox outbreak a public health emergency, as there is no specific therapeutics for monkeypox virus (MPXV) disease. This study focused on docking various commercial drugs and plant-derived compounds against the E8 envelope protein crucial for MPXV attachment and pathogenesis. The target protein structure was modeled based on the vaccinia virus D8L protein. Notably, maraviroc and punicalagin emerged as potential ligands, with punicalagin exhibiting higher binding affinity (- 9.1 kcal/mol) than maraviroc (- 7.8 kcal/mol). Validation through 100 ns molecular dynamics (MD) simulations demonstrated increased stability of the E8-punicalagin complex, with lower RMSD, RMSF, and Rg compared to maraviroc. Enhanced hydrogen bonding, lower solvent accessibility, and compact motions also attributed to higher binding affinity and stability of the complex. MM-PBSA calculations revealed van der Waals, electrostatic, and non-polar solvation as principal stabilizing energies. The binding energy decomposition per residue favored stable interactions between punicalagin and the protein's active site residues (Arg20, Phe56, Glu228, Tyr232) compared to maraviroc. Overall study suggests that punicalagin can act as a potent inhibitor against MPXV. Further research and experimental investigations are warranted to validate its efficacy and safety.

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.

Are Protein Conformational Ensembles in Agreement with Experimental Data? A Geometrical Interpretation of the Problem

The conformational variability of biological macromolecules can play an important role in their biological function. Therefore, understanding conformational variability is expected to be key for predicting the behavior of a particular molecule in the context of organism-wide studies. Several experimental methods have been developed and deployed for accessing this information, and computational methods are continuously updated for the profitable integration of different experimental sources. The outcome of this endeavor is conformational ensembles, which may vary significantly in properties and composition when different ensemble reconstruction methods are used, and this raises the issue of comparing the predicted ensembles against experimental data. In this article, we discuss a geometrical formulation to provide a framework for understanding the agreement of an ensemble prediction to the experimental observations.

NMR and Single-Molecule FRET Insights into Fast Protein Motions and Their Relation to Function

Proteins often undergo large-scale conformational transitions, in which secondary and tertiary structure elements (loops, helices, and domains) change their structures or their positions with respect to each other. Simple considerations suggest that such dynamics should be relatively fast, but the functional cycles of many proteins are often relatively slow. Sophisticated experimental methods are starting to tackle this dichotomy and shed light on the contribution of large-scale conformational dynamics to protein function. In this review, we focus on the contribution of single-molecule Förster resonance energy transfer and nuclear magnetic resonance (NMR) spectroscopies to the study of conformational dynamics. We briefly describe the state of the art in each of these techniques and then point out their similarities and differences, as well as the relative strengths and weaknesses of each. Several case studies, in which the connection between fast conformational dynamics and slower function has been demonstrated, are then introduced and discussed. These examples include both enzymes and large protein machines, some of which have been studied by both NMR and fluorescence spectroscopies.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Mining the Dynamical Properties of Substrate and FAD Binding Pockets of LSD1: Hints for New Inhibitor Design Direction

Lysine-specific demethylase 1 (LSD1), a highly sophisticated epigenetic regulator, orchestrates a range of critical cellular processes, holding promising therapeutic potential for treating diverse diseases. However, the clinical research progress targeting LSD1 is very slow. After 20 years of research, only one small-molecule drug, BEA-17, targeting the degradation of LSD1 and CoREST has been approved by the U.S. Food and Drug Administration. The primary reason for this may be the lack of abundant structural data regarding its intricate functions. To gain a deeper understanding of its conformational dynamics and guide the drug design process, we conducted molecular dynamics simulations to explore the conformational states of LSD1 in the apo state and under the influence of cofactors of flavin adenine dinucleotide (FAD) and CoREST. Our results showed that, across all states, the substrate binding pocket exhibited high flexibility, whereas the FAD binding pocket remained more stable. These distinct dynamical properties are essential for LSD1's ability to bind various substrates while maintaining efficient demethylation activity. Both pockets can be enlarged by merging with adjacent pockets, although only the substrate binding pocket can shrink into smaller pockets. These new pocket shapes can inform inhibitor design, particularly for selectively FAD-competitive inhibitors of LSD1, given the presence of numerous FAD-dependent enzymes in the human body. More interestingly, in the absence of FAD binding, the united substrate and FAD binding pocket are partitioned by the conserved residue of Tyr761, offering valuable insights for the design of inhibitors that disrupt the crucial steric role of Tyr761 and the redox role of FAD. Additionally, we identified pockets that positively or negatively correlate with the substrate and FAD binding pockets, which can be exploited for the design of allosteric or concurrent inhibitors. Our results reveal the intricate dynamical properties of LSD1 as well as multiple novel conformational states, which deepen our understanding of its sophisticated functions and aid in the rational design of new inhibitors.

Computational investigation of the inhibitory interaction of IRF3 and SARS-CoV-2 accessory protein ORF3b

ORF3b is one of the SARS-CoV-2 accessory proteins. Previous experimental study suggested that ORF3b prevents IRF3 translocating to nucleus. However, the biophysical mechanism of ORF3b-IRF3 interaction is elusive. Here, we explored the conformation ensemble of ORF3b using all-atom replica exchange molecular dynamics simulation. Disordered ORF3b has mixed α-helix, β-turn and loop conformers. The potential ORF3b-IRF3 binding modes were searched by docking representative ORF3b conformers with IRF3, and 50 ORF3b-IRF3 complex poses were screened using molecular dynamics simulations ranging from 500 to 1000 ns. We found that ORF3b binds IRF3 predominantly on its CBP binding and phosphorylated pLxIS motifs, with CBP binding site has the highest binding affinity. The ORF3b-IRF3 binding residues are highly conserved in SARS-CoV-2. Our results provided biophysics insights into ORF3b-IRF3 interaction and explained its interferon antagonism mechanism.

Ion-Concentration-Hopping Heterolayer Gel for Ultrahigh Gradient Energy Conversion

Conventional solid ion channel systems relying on single one- or two-dimensional confined nanochannels enabled selective and ultrafast convective ion transport. However, due to intrinsic solid channel stacking, these systems often face pore-pore polarization and ion concentration blockage, thereby restricting their efficiency in macroscale ion transport. Here, we constructed a soft heterolayer-gel system that integrated an ion-selective hydrogel layer with a water-barrier organogel layer, achieving ultrahigh cation selectivity and flux and effectively providing high-efficiency gradient energy conversion on a macroscale order of magnitude. Specifically, the hydrogel layer featured an unconfined 3D network, where the fluctuations of highly hydrated polyelectrolyte chains driven by thermal dynamics enhanced cation selectivity and mitigated transfer energy barriers. Such chain fluctuation mechanisms facilitated ion-cluster internal transmission, thereby enhancing ion concentration hopping for more efficient ion-selective transport. Compared to the existing rigid nanochannel-based gradient energy conversion systems, such a heterogel-based power generator exhibited a record power density of 192.90 and 1.07 W/m2 at the square micrometer scale and square centimeter scale, respectively (under a 500-fold artificial solution). We anticipate that such heterolayer gels would be a promising candidate for energy separation and storage applications.

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods.

Multilevel superposition for deciphering the conformational variability of protein ensembles

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Hierarchical consciousness: the Nested Observer Windows model

Foremost in our experience is the intuition that we possess a unified conscious experience. However, many observations run counter to this intuition: we experience paralyzing indecision when faced with two appealing behavioral choices, we simultaneously hold contradictory beliefs, and the content of our thought is often characterized by an internal debate. Here, we propose the Nested Observer Windows (NOW) Model, a framework for hierarchical consciousness wherein information processed across many spatiotemporal scales of the brain feeds into subjective experience. The model likens the mind to a hierarchy of nested mosaic tiles-where an image is composed of mosaic tiles, and each of these tiles is itself an image composed of mosaic tiles. Unitary consciousness exists at the apex of this nested hierarchy where perceptual constructs become fully integrated and complex behaviors are initiated via abstract commands. We define an observer window as a spatially and temporally constrained system within which information is integrated, e.g. in functional brain regions and neurons. Three principles from the signal analysis of electrical activity describe the nested hierarchy and generate testable predictions. First, nested observer windows disseminate information across spatiotemporal scales with cross-frequency coupling. Second, observer windows are characterized by a high degree of internal synchrony (with zero phase lag). Third, observer windows at the same spatiotemporal level share information with each other through coherence (with non-zero phase lag). The theoretical framework of the NOW Model accounts for a wide range of subjective experiences and a novel approach for integrating prominent theories of consciousness.

Recent advances in de novo computational design and redesign of intrinsically disordered proteins and intrinsically disordered protein regions

In the early 2000s, the concept of "unstructured biology" has emerged to be an important field in protein science by generating various new research directions. Many novel strategies and methods have been developed that are focused on effectively identifying/predicting intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs), identifying their potential functions, disorder based drug design etc. Due to the range of functions of IDPs/IDPRs and their involvement in various debilitating diseases they are of contemporary interest to the scientific community. Recent researches are focused on designing/redesigning specific IDPs/IDPRs de novo. These de novo design/redesigns of IDPs/IDPRs are carried out by altering compositional biases and specific sequence patterning parameters. The main focus of these researches is to influence specific molecular functions, phase behavior, cellular phenotypes etc. In this review, we first provide the differences of natively folded and natively unfolded or IDPs with respect to their potential energy landscapes. Here, we provide current understandings on the different computational design strategies and methods that have been utilized in de novo design and redesigns of IDPs and IDPRs. Finally, we conclude the review by discussing the challenges that have been faced during the computational design/design attempts of IDPs/IDPRs.

Role of ATP Hydrolysis and Product Release in the Translocation Mechanism of SARS-CoV-2 NSP13

In response to the emergence of COVID-19, caused by SARS-CoV-2, there has been a growing interest in understanding the functional mechanisms of the viral proteins to aid in the development of new therapeutics. Nonstructural protein 13 (nsp13) helicase is an attractive target for antivirals because it is essential for viral replication and has a low mutation rate, yet the structural mechanisms by which this enzyme binds and hydrolyzes ATP to cause unidirectional RNA translocation remain elusive. Using Gaussian accelerated molecular dynamics (GaMD), we generated comprehensive conformational ensembles of all substrate states along the ATP-dependent cycle. Shape-GMM clustering of the protein yields four protein conformations that describe an opening and closing of both the ATP pocket and the RNA cleft that is achieved through a combination of conformational selection and induction along the ATP hydrolysis cycle. Furthermore, three protein-RNA conformations are observed that implicate motifs Ia, IV, and V as playing a pivotal role in an ATP-dependent inchworm translocation mechanism. Finally, based on a linear discriminant analysis of protein conformations, we identify L405 as a pivotal residue for the opening and closing mechanism and propose a L405D mutation as a way to disrupt translocation. This research enhances our understanding of nsp13's role in viral replication and could contribute to the development of antiviral strategies.

From Monomers to Hexamers: A Theoretical Probability of the Neighbor Density Approach to Dissect Protein Oligomerization in Cells

Deciphering the oligomeric state of proteins within cells is pivotal to understanding their role in intricate cellular processes. With the recent advances in single-molecule localization microscopy, previous efforts have harnessed protein location density approaches, coupled with simulations, to extract membrane protein oligomeric states in cells, highlighting the value of such techniques. However, a comprehensive theoretical approach that can be universally applied across different proteins (e.g., membrane and cytosolic proteins) remains elusive. Here, we introduce the theoretical probability of neighbor density (PND) as a robust tool to discern protein oligomeric states in cellular environments. Utilizing our approach, the theoretical PND was validated against simulated data for both membrane and cytosolic proteins, consistently aligning with experimental baselines for membrane proteins. This congruence was maintained even when adjusting for protein concentrations or exploring proteins of various oligomeric states. The strength of our method lies not only in its precision but also in its adaptability, accommodating diverse cellular protein scenarios without compromising the accuracy. The development and validation of the theoretical PND facilitate accurate protein oligomeric state determination and bolster our understanding of protein-mediated cellular functions.

Oliveira AS, Mulholland AJ, Serapian SA, Colombo G. Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 μs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet β5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

Excited States of apo-Guanidine-III Riboswitch Contribute to Guanidinium Binding through Both Conformational and Induced-Fit Mechanisms

Riboswitches are mRNA segments that regulate gene expression through conformational changes driven by their cognate ligand binding. The ykkC motif forms a riboswitch class that selectively senses a guanidinium ion (Gdm+) and regulates the downstream expression of proteins which aid in the efflux of excess Gdm+ from the cells. The aptamer domain (AD) of the guanidine-III riboswitch forms an H-type pseudoknot with a triple helical domain that binds a Gdm+. We studied the binding of Gdm+ to the AD of the guanidine (ykkC)-III riboswitch using computer simulations to probe the specificity of the riboswitch to Gdm+ binding. We show that Gdm+ binding is a fast process occurring on the nanosecond time scale, with minimal conformational changes to the AD. Using machine learning and Markov-state models, we identified the excited conformational states of the AD, which have a high Gdm+ binding propensity, making the Gdm+ binding landscape complex exhibiting both conformational selection and induced-fit mechanisms. The proposed apo-AD excited states and their role in the ligand-sensing mechanism are amenable to experimental verification. Further, targeting these excited-state conformations in discovering new antibiotics can be explored.

Structural heterogeneity of the ion and lipid channel TMEM16F

Transmembrane protein 16 F (TMEM16F) is a Ca2+-activated homodimer which functions as an ion channel and a phospholipid scramblase. Despite the availability of several TMEM16F cryogenic electron microscopy (cryo-EM) structures, the mechanism of activation and substrate translocation remains controversial, possibly due to restrictions in the accessible protein conformational space. In this study, we use atomic force microscopy under physiological conditions to reveal a range of structurally and mechanically diverse TMEM16F assemblies, characterized by variable inter-subunit dimerization interfaces and protomer orientations, which have escaped prior cryo-EM studies. Furthermore, we find that Ca2+-induced activation is associated to stepwise changes in the pore region that affect the mechanical properties of transmembrane helices TM3, TM4 and TM6. Our direct observation of membrane remodelling in response to Ca2+ binding along with additional electrophysiological analysis, relate this structural multiplicity of TMEM16F to lipid and ion permeation processes. These results thus demonstrate how conformational heterogeneity of TMEM16F directly contributes to its diverse physiological functions.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

High-resolution double vision of the allosteric phosphatase PTP1B

Protein tyrosine phosphatase 1B (PTP1B) plays important roles in cellular homeostasis and is a highly validated therapeutic target for multiple human ailments, including diabetes, obesity and breast cancer. However, much remains to be learned about how conformational changes may convey information through the structure of PTP1B to enable allosteric regulation by ligands or functional responses to mutations. High-resolution X-ray crystallography can offer unique windows into protein conformational ensembles, but comparison of even high-resolution structures is often complicated by differences between data sets, including non-isomorphism. Here, the highest resolution crystal structure of apo wild-type (WT) PTP1B to date is presented out of a total of ∼350 PTP1B structures in the PDB. This structure is in a crystal form that is rare for PTP1B, with two unique copies of the protein that exhibit distinct patterns of conformational heterogeneity, allowing a controlled comparison of local disorder across the two chains within the same asymmetric unit. The conformational differences between these chains are interrogated in the apo structure and between several recently reported high-resolution ligand-bound structures. Electron-density maps in a high-resolution structure of a recently reported activating double mutant are also examined, and unmodeled alternate conformations in the mutant structure are discovered that coincide with regions of enhanced conformational heterogeneity in the new WT structure. These results validate the notion that these mutations operate by enhancing local dynamics, and suggest a latent susceptibility to such changes in the WT enzyme. Together, these new data and analysis provide a detailed view of the conformational ensemble of PTP1B and highlight the utility of high-resolution crystallography for elucidating conformational heterogeneity with potential relevance for function.

Stimulus-Induced Relief of Intentionally Incorporated Frustration Drives Refolding of a Water-Soluble Biomimetic Foldamer

Frustrated, or nonoptimal, interactions have been proposed to be essential to a protein's ability to display responsive behavior such as allostery, conformational signaling, and signal transduction. However, the intentional incorporation of frustrated noncovalent interactions has not been explored as a design element in the field of dynamic foldamers. Here, we report the design, synthesis, characterization, and molecular dynamics simulations of the first dynamic water-soluble foldamer that, in response to a stimulus, exploits relief of frustration in its noncovalent network to structurally rearrange from a pleated to an intercalated columnar structure. Thus, relief of frustration provides the energetic driving force for structural rearrangement. This work represents a previously unexplored design element for the development of stimulus-responsive systems that has potential application to materials chemistry, synthetic biology, and molecular machines.

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Protein engineering of multi-enzyme virus-like particle nanoreactors for enhanced chiral alcohol synthesis

In the past decade, virus-like particles (VLPs) that can encapsulate single or multiple enzymes have been studied extensively as typical nanoreactors for biocatalysis in vitro, yet their catalytic efficiencies are usually inadequate for real applications. These biocatalytic nanoreactors should be engineered like their free-enzyme counterparts to improve their catalytic performance for potential applications. Herein we engineer biocatalytic VLPs for the enhanced synthesis of chiral alcohols. Different methods including directed evolution were applied to the entire bacteriophage P22 VLPs (except the coat protein), which encapsulated a carbonyl reductase from Scheffersomyces stipitis (SsCR) and a glucose dehydrogenase from Bacillus megaterium (BmGDH) in their capsids. The best variant, namely M5, showed an enhanced turnover frequency (TOF, min-1) up to 15-fold toward the majority of tested aromatic prochiral ketones, and gave up to 99% enantiomeric excess in the synthesis of chiral alcohol pharmaceutical intermediates. A comparison with the mutations of the free-enzyme counterparts showed that the same amino acid mutations led to different changes in the catalytic efficiencies of free and confined enzymes. Finally, the engineered M5 nanoreactor showed improved efficiency in the scale-up synthesis of chiral alcohols. The conversions of three substrates catalyzed by M5 were all higher than those catalyzed by the wild-type nanoreactor, demonstrating that enzyme-encapsulating VLPs can evolve to enhance their catalytic performance for potential applications.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

The Role of ATP Hydrolysis and Product Release in the Translocation Mechanism of SARS-CoV-2 NSP13

In response to the emergence of COVID-19, caused by SARS-CoV-2, there has been a growing interest in understanding the functional mechanisms of the viral proteins to aid in the development of new therapeutics. Non-structural protein 13 (Nsp13) helicase is an attractive target for antivirals because it is essential for viral replication and has a low mutation rate; yet, the structural mechanisms by which this enzyme binds and hydrolyzes ATP to cause unidirectional RNA translocation remain elusive. Using Gaussian accelerated molecular dynamics (GaMD), we generated a comprehensive conformational ensemble of all substrate states along the ATP-dependent cycle. ShapeGMM clustering of the protein yields four protein conformations that describe an opening and closing of both the ATP pocket and RNA cleft. This opening and closing is achieved through a combination of conformational selection and induction along the ATP cycle. Furthermore, three protein-RNA conformations are observed that implicate motifs Ia, IV, and V as playing a pivotal role in an ATP-dependent inchworm translocation mechanism. Finally, based on a linear discriminant analysis of protein conformations, we identify L405 as a pivotal residue for the opening and closing mechanism and propose a L405D mutation as a way of testing our proposed mechanism. This research enhances our understanding of nsp13's role in viral replication and could contribute to the development of antiviral strategies.

Modulation of a conformational ensemble by a small molecule that inhibits key protein-protein interactions involved in cell adhesion

Small molecules that regulate protein-protein interactions can be valuable drugs; however, the development of such small molecules is challenging as the molecule must interfere with an interaction that often involves a large surface area. Herein, we propose that modulating the conformational ensemble of the proteins participating in a given interaction, rather than blocking the interaction by directly binding to the interface, is a relevant strategy for interfering with a protein-protein interaction. In this study, we applied this concept to P-cadherin, a cell surface protein forming homodimers that are essential for cell-cell adhesion in various biological contexts. We first determined the crystal structure of P-cadherin with a small molecule inhibitor whose inhibitory mechanism was unknown. Molecular dynamics simulations suggest that the inhibition of cell adhesion by this small molecule results from modulation of the conformational ensemble of P-cadherin. Our study demonstrates the potential of small molecules altering the conformation ensemble of a protein as inhibitors of biological relevant protein-protein interactions.

Recent Progress in Antibody Epitope Prediction

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

How aberrant N-glycosylation can alter protein functionality and ligand binding: An atomistic view

Protein-assembly defects due to an enrichment of aberrant conformational protein variants are emerging as a new frontier in therapeutics design. Understanding the structural elements that rewire the conformational dynamics of proteins and pathologically perturb functionally oriented ensembles is important for inhibitor development. Chaperones are hub proteins for the assembly of multiprotein complexes and an enrichment of aberrant conformers can affect the cellular proteome, and in turn, phenotypes. Here, we integrate computational and experimental tools to investigte how N-glycosylation of specific residues in glucose-regulated protein 94 (GRP94) modulates internal dynamics and alters the conformational fitness of regions fundamental for the interaction with ATP and synthetic ligands and impacts substructures important for the recognition of interacting proteins. N-glycosylation plays an active role in modulating the energy landscape of GRP94, and we provide support for leveraging the knowledge on distinct glycosylation variants to design molecules targeting GRP94 disease-associated conformational states and assemblies.

Decoupling of catalysis and transition state analog binding from mutations throughout a phosphatase revealed by high-throughput enzymology

Using high-throughput microfluidic enzyme kinetics (HT-MEK), we measured over 9,000 inhibition curves detailing impacts of 1,004 single-site mutations throughout the alkaline phosphatase PafA on binding affinity for two transition state analogs (TSAs), vanadate and tungstate. As predicted by catalytic models invoking transition state complementary, mutations to active site and active-site-contacting residues had highly similar impacts on catalysis and TSA binding. Unexpectedly, most mutations to more distal residues that reduced catalysis had little or no impact on TSA binding and many even increased tungstate affinity. These disparate effects can be accounted for by a model in which distal mutations alter the enzyme's conformational landscape, increasing the occupancy of microstates that are catalytically less effective but better able to accommodate larger transition state analogs. In support of this ensemble model, glycine substitutions (rather than valine) were more likely to increase tungstate affinity (but not more likely to impact catalysis), presumably due to increased conformational flexibility that allows previously disfavored microstates to increase in occupancy. These results indicate that residues throughout an enzyme provide specificity for the transition state and discriminate against analogs that are larger only by tenths of an Ångström. Thus, engineering enzymes that rival the most powerful natural enzymes will likely require consideration of distal residues that shape the enzyme's conformational landscape and fine-tune active-site residues. Biologically, the evolution of extensive communication between the active site and remote residues to aid catalysis may have provided the foundation for allostery to make it a highly evolvable trait.

Single-molecule visualization determines conformational substate ensembles in β-sheet-rich peptide fibrils

An understanding of protein conformational ensembles is essential for revealing the underlying mechanisms of interpeptide recognition and association. However, experimentally resolving multiple simultaneously existing conformational substates remains challenging. Here, we report the use of scanning tunneling microscopy (STM) to analyze the conformational substate ensembles of β sheet peptides with a submolecular resolution (in-plane <2.6 Å). We observed ensembles of more than 10 conformational substates (with free energy fluctuations between several k_BTs) in peptide homoassemblies of keratin (KRT) and amyloidal peptides (-5Aβ42 and TDP-43 341-357). Furthermore, STM reveals a change in the conformational ensemble of peptide mutants, which is correlated with the macroscopic properties of peptide assemblies. Our results demonstrate that the STM-based single-molecule imaging can capture a thorough picture of the conformational substates with which to build an energetic landscape of interconformational interactions and can rapidly screen conformational ensembles, which can complement conventional characterization techniques.

Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q

CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q Basic Protocol 2: Exploring conformational diversity in a protein family Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family Basic Protocol 3: Representing conformational diversity in a phylogenetic context.

Fmoc-diphenylalanine gelating nanoarchitectonics: A simplistic peptide self-assembly to meet complex applications

9-fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF), has been has been extensively explored due to its ultrafast self-assembly kinetics, inherent biocompatibility, tunable physicochemical properties, and especially, the capability of forming self-sustained gels under physiological conditions. Consequently, various methodologies to develop Fmoc-FF gels and their corresponding applications in biomedical and industrial fields have been extensively studied. Herein, we systemically summarize the mechanisms underlying Fmoc-FF self-assembly, discuss the preparation methodologies of Fmoc-FF hydrogels, and then deliberate the properties as well as the diverse applications of Fmoc-FF self-assemblies. Finally, the contemporary shortcomings which limit the development of Fmoc-FF self-assembly are raised and the alternative solutions are proposed, along with future research perspectives.

Screw sense and screw sensibility: communicating information by conformational switching in helical oligomers

Biological systems have evolved a number of different strategies to communicate information on the molecular scale. Among these, the propagation of conformational change is among the most important, being the means by which G-protein coupled receptors (GPCRs) use extracellular signals to modulate intracellular processes, and the way that opsin proteins translate light signals into nerve impulses. The developing field of foldamer chemistry has allowed chemists to employ conformationally well-defined synthetic structures likewise to mediate information transfer, making use of mechanisms that are not found in biological contexts. In this review, we discuss the use of switchable screw-sense preference as a communication mechanism. We discuss the requirements for functional communication devices, and show how dynamic helical foldamers derived from the achiral monomers such as α-aminoisobutyric acid (Aib) and meso-cyclohexane-1,2-diamine fulfil them by communicating information in the form of switchable screw-sense preference. We describe the various stimuli that can be used to switch screw sense, and explore the way that propagation of the resulting conformational preference in a well-defined helical molecule allows screw sense to control chemical events remote from a source of information. We describe the operation of these conformational switches in the membrane phase, and outline the progress that has been made towards using conformational switching to communicate between the exterior and interior of a phospholipid vesicle.

Engineering the Active Site Lid Dynamics to Improve the Catalytic Efficiency of Yeast Cytosine Deaminase

Conformational dynamics is important for enzyme catalysis. However, engineering dynamics to achieve a higher catalytic efficiency is still challenging. In this work, we develop a new strategy to improve the activity of yeast cytosine deaminase (yCD) by engineering its conformational dynamics. Specifically, we increase the dynamics of the yCD C-terminal helix, an active site lid that controls the product release. The C-terminal is extended by a dynamical single α-helix (SAH), which improves the product release rate by up to ~8-fold, and the overall catalytic rate kcat by up to ~2-fold. It is also shown that the kcat increase is due to the favorable activation entropy change. The NMR H/D exchange data indicate that the conformational dynamics of the transition state analog complex increases as the helix is extended, elucidating the origin of the enhanced catalytic entropy. This study highlights a novel dynamics engineering strategy that can accelerate the overall catalysis through the entropy-driven mechanism.

Apparatus for Automated Continuous Hydrogen Deuterium Exchange Mass Spectrometry Measurements from Milliseconds to Hours

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a rapidly growing technique for protein characterization in industry and academia, complementing the "static" picture provided by classical structural biology with information about the dynamic structural changes that accompany biological function. Conventional hydrogen deuterium exchange experiments, carried out on commercially available systems, typically collect 4-5 exchange timepoints on a timescale ranging from tens of seconds to hours using a workflow that can require 24 h or more of continuous data collection for triplicate measurements. A small number of groups have developed setups for millisecond timescale HDX, allowing for the characterization of dynamic shifts in weakly structured or disordered regions of proteins. This capability is particularly important given the central role that weakly ordered protein regions often play in protein function and pathogenesis. In this work, we introduce a new continuous flow injection setup for time-resolved HDX-MS (CFI-TRESI-HDX) that allows automated, continuous or discrete labeling time measurements from milliseconds to hours. The device is composed almost entirely of "off-the-shelf" LC components and can acquire an essentially unlimited number of timepoints with substantially reduced runtimes compared to conventional systems.

Obtaining anomalous and ensemble information from protein crystals from 220 K up to physiological temperatures

X-ray crystallography has been invaluable in delivering structural information about proteins. Previously, an approach has been developed that allows high-quality X-ray diffraction data to be obtained from protein crystals at and above room temperature. Here, this previous work is built on and extended by showing that high-quality anomalous signal can be obtained from single protein crystals using diffraction data collected at 220 K up to physiological temperatures. The anomalous signal can be used to directly determine the structure of a protein, i.e. to phase the data, as is routinely performed under cryoconditions. This ability is demonstrated by obtaining diffraction data from model lysozyme, thaumatin and proteinase K crystals, the anomalous signal from which allowed their structures to be solved experimentally at 7.1 keV X-ray energy and at room temperature with relatively low data redundancy. It is also demonstrated that the anomalous signal from diffraction data obtained at 310 K (37°C) can be used to solve the structure of proteinase K and to identify ordered ions. The method provides useful anomalous signal at temperatures down to 220 K, resulting in an extended crystal lifetime and increased data redundancy. Finally, we show that useful anomalous signal can be obtained at room temperature using X-rays of 12 keV energy as typically used for routine data collection, allowing this type of experiment to be carried out at widely accessible synchrotron beamline energies and enabling the simultaneous extraction of high-resolution data and anomalous signal. With the recent emphasis on obtaining conformational ensemble information for proteins, the high resolution of the data allows such ensembles to be built, while the anomalous signal allows the structure to be experimentally solved, ions to be identified, and water molecules and ions to be differentiated. Because bound metal-, phosphorus- and sulfur-containing ions all have anomalous signal, obtaining anomalous signal across temperatures and up to physiological temperatures will provide a more complete description of protein conformational ensembles, function and energetics.