Protein flexibility predictions using graph theory

Remote loop evolution reveals a complex biological function for chitinase enzymes beyond the active site

Loops are small secondary structural elements that play a crucial role in the emergence of new enzyme functions. However, the evolutionary molecular mechanisms how proteins acquire these loop elements and obtain new function is poorly understood. To address this question, we study glycoside hydrolase family 19 (GH19) chitinase-an essential enzyme family for pathogen degradation in plants. By revealing the evolutionary history and loops appearance of GH19 chitinase, we discover that one loop which is remote from the catalytic site, is necessary to acquire the new antifungal activity. We demonstrate that this remote loop directly accesses the fungal cell wall, and surprisingly, it needs to adopt a defined structure supported by long-range intramolecular interactions to perform its function. Our findings prove that nature applies this strategy at the molecular level to achieve a complex biological function while maintaining the original activity in the catalytic pocket, suggesting an alternative way to design new enzyme function.

Structure, dynamics, and stability of the smallest and most complex 71 protein knot

Proteins can spontaneously tie a variety of intricate topological knots through twisting and threading of the polypeptide chains. Recently developed artificial intelligence algorithms have predicted several new classes of topological knotted proteins, but the predictions remain to be authenticated experimentally. Here, we showed by X-ray crystallography and solution-state NMR spectroscopy that Q9PR55, an 89-residue protein from Ureaplasma urealyticum, possesses a novel 71 knotted topology that is accurately predicted by AlphaFold 2, except for the flexible N terminus. Q9PR55 is monomeric in solution, making it the smallest and most complex knotted protein known to date. In addition to its exceptional chemical stability against urea-induced unfolding, Q9PR55 is remarkably robust to resist the mechanical unfolding-coupled proteolysis by a bacterial proteasome, ClpXP. Our results suggest that the mechanical resistance against pulling-induced unfolding is determined by the complexity of the knotted topology rather than the size of the molecule.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Using graphlet degree vectors to predict atomic displacement parameters in protein structures

In structural biology, atomic displacement parameters, commonly used in the form of B values, describe uncertainties in atomic positions. Their distribution over the structure can provide hints on local structural reliability and mobility. A spatial macromolecular model can be represented by a graph whose nodes are atoms and whose edges correspond to all interatomic contacts within a certain distance. Small connected subgraphs, called graphlets, provide information about the wiring of a particular atom. The multiple linear regression approach based on this information aims to predict a distribution of values of isotropic atomic displacement parameters (B values) within a protein structure, given the atomic coordinates and molecular packing. By modeling the dynamic component of atomic uncertainties, this method allows the B values obtained from experimental crystallographic or cryo-electron microscopy studies to be reproduced relatively well.

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States

The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Improved methodology for protein NMR structure calculation using hydrogen bond restraints and ANSURR validation: The SH2 domain of SH2B1

Protein structures calculated using NMR data are less accurate and less well-defined than they could be. Here we use the program ANSURR to show that this deficiency is at least in part due to a lack of hydrogen bond restraints. We describe a protocol to introduce hydrogen bond restraints into the structure calculation of the SH2 domain from SH2B1 in a systematic and transparent way and show that the structures generated are more accurate and better defined as a result. We also show that ANSURR can be used as a guide to know when the structure calculation is good enough to stop.

Thermal Ring-Based Heat Switches in Hyperthermophilic Class II Bacterial Fructose Aldolase

Both thermophilic and hyperthermophilic enzymes in bacterial and archaeal species are activated above a specific temperature threshold but inactivated at another higher temperature. However, the underlying structural basis for these two heat switches is still unresolved. Here, graph theory was used to test if the temperature-dependent noncovalent interactions and metal bridges as identified in a series of crystal structures of the class II bacterial fructose 1,6-bisphosphate aldolase homodimer or homotetramer with or without natural substrates and products bound could form systematic fluidic grid-like mesh networks with topological grids as thermal rings to regulate their structural thermostability and functional thermoactivity. The results indicated that the second biggest grid in the Thermus aquaticus fructose 1,6-diphosphate aldolase dimer may control the specific temperature threshold to release the swapping flexible active sites at the dimeric interface for heat-evoked activation. Meanwhile, the third biggest grid may serve as a necessary structural motif against heat inactivation. Finally, the smallest grid may act as a stiff thermostable anchor. Its dissociation at the maximal melting temperature threshold may stop the catalytic activity. Taken as a whole, this computational study may render the structural motifs for the optimal growth temperature and the extreme heat stability of hyperthermophiles.

Crystal structure of a variable region segment of Leptospira host-interacting outer surface protein, LigA, reveals the orientation of Ig-like domains

Leptospiral immunoglobulin-like (Lig) protein family is a surface-exposed protein from the pathogenic Leptospira. The Lig protein family has been identified as an essential virulence factor of L. interrogan. One of the family members, LigA, contains 13 homologous tandem repeats of bacterial Ig-like (Big) domains in its extracellular portion. It is crucial in binding with the host's Extracellular matrices (ECM) and complement factors. However, its vital role in the invasion and evasion of pathogenic Leptospira, structural details, and domain organization of the extracellular portion of this protein are not explored thoroughly. Here, we described the first high-resolution crystal structure of a variable region segment (LigA8-9) of LigA at 1.87 Å resolution. The structure showed some remarkably distinctive aspects compared with the most closely related Immunoglobulin superfamily (IgSF) members. The structure illustrated the relative orientation of two domains and highlighted the role of the linker region in the domain orientation. We also observed an apparent electron density of Ca²⁺ ions coordinated with a proper interacting geometry within the protein. Molecular dynamic simulations demonstrated the involvement of a linker salt bridge in providing rigidity between the two domains. Our study proposes an overall arrangement of Ig-like domains in the LigA protein. The structural understanding of the extracellular portion of LigA and its interaction with the ECM provides insight into developing new therapeutics directed toward leptospirosis.

Model of ligand-triggered information transmission in G-protein coupled receptor complexes

We present a model for the effects of ligands on information transmission in G-Protein Coupled Receptor (GPCR) complexes. The model is built ab initio entirely on principles of statistical mechanics and tenets of information transmission theory and was validated in part using agonist-induced effector activity and signaling bias for the angiotensin- and adrenergic-mediated signaling pathways, with in vitro observations of phosphorylation sites on the C tail of the GPCR complex, and single-cell information-transmission experiments. The model extends traditional kinetic models that form the basis for many existing models of GPCR signaling. It is based on maximizing the rates of entropy production and information transmission through the GPCR complex. The model predicts that (1) phosphatase-catalyzed reactions, as opposed to kinase-catalyzed reactions, on the C-tail and internal loops of the GPCR are responsible for controlling the signaling activity, (2) signaling favors the statistical balance of the number of switches in the ON state and the number in the OFF state, and (3) biased-signaling response depends discontinuously on ligand concentration.

Mapping the conformational landscape of the stimulatory heterotrimeric G protein

Heterotrimeric G proteins serve as membrane-associated signaling hubs, in concert with their cognate G-protein-coupled receptors. Fluorine nuclear magnetic resonance spectroscopy was employed to monitor the conformational equilibria of the human stimulatory G-protein α subunit (G_sα) alone, in the intact G_sαβ₁γ₂ heterotrimer or in complex with membrane-embedded human adenosine A_2A receptor (A_2AR). The results reveal a concerted equilibrium that is strongly affected by nucleotide and interactions with the βγ subunit, the lipid bilayer and A_2AR. The α1 helix of G_sα exhibits significant intermediate timescale dynamics. The α4β6 loop and α5 helix undergo membrane/receptor interactions and order-disorder transitions respectively, associated with G-protein activation. The αN helix adopts a key functional state that serves as an allosteric conduit between the βγ subunit and receptor, while a significant fraction of the ensemble remains tethered to the membrane and receptor upon activation.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Enzyme adaptation to habitat thermal legacy shapes the thermal plasticity of marine microbiomes

Microbial communities respond to temperature with physiological adaptation and compositional turnover. Whether thermal selection of enzymes explains marine microbiome plasticity in response to temperature remains unresolved. By quantifying the thermal behaviour of seven functionally-independent enzyme classes (esterase, extradiol dioxygenase, phosphatase, beta-galactosidase, nuclease, transaminase, and aldo-keto reductase) in native proteomes of marine sediment microbiomes from the Irish Sea to the southern Red Sea, we record a significant effect of the mean annual temperature (MAT) on enzyme response in all cases. Activity and stability profiles of 228 esterases and 5 extradiol dioxygenases from sediment and seawater across 70 locations worldwide validate this thermal pattern. Modelling the esterase phase transition temperature as a measure of structural flexibility confirms the observed relationship with MAT. Furthermore, when considering temperature variability in sites with non-significantly different MATs, the broadest range of enzyme thermal behaviour and the highest growth plasticity of the enriched heterotrophic bacteria occur in samples with the widest annual thermal variability. These results indicate that temperature-driven enzyme selection shapes microbiome thermal plasticity and that thermal variability finely tunes such processes and should be considered alongside MAT in forecasting microbial community thermal response.

Probing Conformational Dynamics of Antibodies with Geometric Simulations

This chapter describes the application of constrained geometric simulations for prediction of antibody structural dynamics. We utilize constrained geometric simulations method FRODAN, which is a low computational complexity alternative to molecular dynamics (MD) simulations that can rapidly explore flexible motions in protein structures. FRODAN is highly suited for conformational dynamics analysis of large proteins, complexes, intrinsically disordered proteins, and dynamics that occurs on longer biologically relevant time scales that are normally inaccessible to classical MD simulations. This approach predicts protein dynamics at an all-atom scale while retaining realistic covalent bonding, maintaining dihedral angles in energetically good conformations while avoiding steric clashes in addition to performing other geometric and stereochemical criteria checks. In this chapter, we apply FRODAN to showcase its applicability for probing functionally relevant dynamics of IgG2a, including large-amplitude domain-domain motions and motions of complementarity determining region (CDR) loops. As was suggested in previous experimental studies, our simulations show that antibodies can explore a large range of conformational space.

Mechanistic insights into the nickel-dependent allosteric response of the Helicobacter pylori NikR transcription factor

In Helicobacter pylori, the nickel-responsive NikR transcription factor plays a key role in regulating intracellular nickel concentrations, which is an essential process for survival of this pathogen in the acidic human stomach. Nickel binding to H. pylori NikR (HpNikR) allosterically activates DNA binding to target promoters encoding genes involved in nickel homeostasis and acid adaptation, to either activate or repress their transcription. We previously showed that HpNikR adopts an equilibrium between an open conformation and DNA-binding competent cis and trans states. Nickel binding slows down conformational exchange between these states and shifts the equilibrium toward the binding-competent states. The protein then becomes stabilized in a cis conformation upon binding the ureA promoter. Here, we investigate how nickel binding creates this response and how it is transmitted to the DNA-binding domains. Through mutagenesis, DNA-binding studies, and computational methods, the allosteric response to nickel was found to be propagated from the nickel-binding sites to the DNA-binding domains via the β-sheets of the metal-binding domain and a network of residues at the inter-domain interface. Our computational results suggest that nickel binding increases protein rigidity to slow down the conformational exchange. A thymine base in the ureA promoter sequence, known to be critical for high affinity DNA binding by HpNikR, was also found to be important for the allosteric response, while a modified version of this promoter further highlighted the importance of the DNA sequence in modulating the response. Collectively, our results provide insights into regulation of a key protein for H. pylori survival.

Conformational deformation of a multi-jointed elastic loop

A new class of deformation is presented for a planar loop structure made up of slender elastic bodies and joints. In demonstrating the circumferential shortening of the multi-jointed elastic loop, diverse three-dimensional (3D) deformations emerge through piecewise deflections and discrete rotations. These 3D morphologies correspond to conformations of molecular ring systems. Through image processing, the 3D reconstructions of the deformed structures are characterized by number, geometry, and initial imperfections of the body segments. We elucidate from measurements that the conformational deformation without self-stress results from a cyclical assembly of compressive bending of elastic bodies with high shear rigidity. The mechanical insights gained may apply in controlling the polymorphism exhibited by the cyclical structures across scales.

Controlling the configuration space topology of mechanical structures

Linkages are mechanical devices constructed from rigid bars and freely rotating joints studied both for their utility in engineering and as mathematical idealizations in a number of physical systems. Recently, there has been a resurgence of interest in designing linkages in the physics community due to the concurrent developments of mechanical metamaterials, topological mechanics, and the discovery of anomalous rigidity in fiber networks and vertex models. These developments raise a natural question: to what extent can the motion of a linkage or mechanical structure be designed? Here, we describe a method to design the topology of the configuration space of a linkage by first identifying the manifold of critical points, then perturbing around such critical configurations. Unlike other methods, our methods are tractable and provide a simple visual toolkit for mechanism design. We demonstrate our procedure by designing a mechanism to gate the propagation of a soliton in a Kane-Lubensky chain of interconnected rotors.

Biomolecular Topology: Modelling and Analysis

With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.

Met/Val129 polymorphism of the full-length human prion protein dictates distinct pathways of amyloid formation

Methionine/valine polymorphism at position 129 of the human prion protein, huPrP, is tightly associated with the pathogenic phenotype, disease progress, and age of onset of neurodegenerative diseases such as Creutzfeldt–Jakob disease or Fatal Familial Insomnia. This raises the question of whether and how the amino acid type at position 129 influences the structural properties of huPrP, affecting its folding, stability, and amyloid formation behavior. Here, our detailed biophysical characterization of the 129M and 129V variants of recombinant full-length huPrP(23–230) by amyloid formation kinetics, CD spectroscopy, molecular dynamics simulations, and sedimentation velocity analysis reveals differences in their aggregation propensity and oligomer content, leading to deviating pathways for the conversion into amyloid at acidic pH. We determined that the 129M variant exhibits less secondary structure content before amyloid formation and higher resistance to thermal denaturation compared to the 129V variant, whereas the amyloid conformation of both variants shows similar thermal stability. Additionally, our molecular dynamics simulations and rigidity analyses at the atomistic level identify intramolecular interactions responsible for the enhanced monomer stability of the 129M variant, involving more frequent minimum distances between E196 and R156, forming a salt bridge. Removal of the N-terminal half of the 129M full-length variant diminishes its differences compared to the 129V full-length variant and highlights the relevance of the flexible N terminus in huPrP. Taken together, our findings provide insight into structural properties of huPrP and the effects of the amino acid identity at position 129 on amyloid formation behavior.

Uncoupling of Voltage- and Ligand-Induced Activation in HCN2 Channels by Glycine Inserts

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are tetramers that generate electrical rhythmicity in special brain neurons and cardiomyocytes. The channels are activated by membrane hyperpolarization. The binding of cAMP to the four available cyclic nucleotide-binding domains (CNBD) enhances channel activation. We analyzed in the present study the mechanism of how the effect of cAMP binding is transmitted to the pore domain. Our strategy was to uncouple the C-linker (CL) from the channel core by inserting one to five glycine residues between the S6 gate and the A′-helix (constructs 1G to 5G). We quantified in full-length HCN2 channels the resulting functional effects of the inserted glycines by current activation as well as the structural dynamics and statics using molecular dynamics simulations and Constraint Network Analysis. We show functionally that already in 1G the cAMP effect on activation is lost and that with the exception of 3G and 5G the concentration-activation relationships are shifted to depolarized voltages with respect to HCN2. The strongest effect was found for 4G. Accordingly, the activation kinetics were accelerated by all constructs, again with the strongest effect in 4G. The simulations reveal that the average residue mobility of the CL and CNBD domains is increased in all constructs and that the junction between the S6 and A′-helix is turned into a flexible hinge, resulting in a destabilized gate in all constructs. Moreover, for 3G and 4G, there is a stronger downward displacement of the CL-CNBD than in HCN2 and the other constructs, resulting in an increased kink angle between S6 and A′-helix, which in turn loosens contacts between the S4-helix and the CL. This is suggested to promote a downward movement of the S4-helix, similar to the effect of hyperpolarization. In addition, exclusively in 4G, the selectivity filter in the upper pore region and parts of the S4-helix are destabilized. The results provide new insights into the intricate activation of HCN2 channels.

Low pH structure of heliorhodopsin reveals chloride binding site and intramolecular signaling pathway

Within the microbial rhodopsin family, heliorhodopsins (HeRs) form a phylogenetically distinct group of light-harvesting retinal proteins with largely unknown functions. We have determined the 1.97 Å resolution X-ray crystal structure of Thermoplasmatales archaeon SG8-52-1 heliorhodopsin (TaHeR) in the presence of NaCl under acidic conditions (pH 4.5), which complements the known 2.4 Å TaHeR structure acquired at pH 8.0. The low pH structure revealed that the hydrophilic Schiff base cavity (SBC) accommodates a chloride anion to stabilize the protonated retinal Schiff base when its primary counterion (Glu-108) is neutralized. Comparison of the two structures at different pH revealed conformational changes connecting the SBC and the extracellular loop linking helices A–B. We corroborated this intramolecular signaling transduction pathway with computational studies, which revealed allosteric network changes propagating from the perturbed SBC to the intracellular and extracellular space, suggesting TaHeR may function as a sensory rhodopsin. This intramolecular signaling mechanism may be conserved among HeRs, as similar changes were observed for HeR 48C12 between its pH 8.8 and pH 4.3 structures. We additionally performed DEER experiments, which suggests that TaHeR forms possible dimer-of-dimer associations which may be integral to its putative functionality as a light sensor in binding a transducer protein.

Modular representation and control of floppy networks

Geometric graph models of systems as diverse as proteins, DNA assemblies, architected materials and robot swarms are useful abstract representations of these objects that also unify ways to study their properties and control them in space and time. While much work has been done in the context of characterizing the behaviour of these networks close to critical points associated with bond and rigidity percolation, isostaticity, etc., much less is known about floppy, underconstrained networks that are far more common in nature and technology. Here, we combine geometric rigidity and algebraic sparsity to provide a framework for identifying the zero energy floppy modes via a representation that illuminates the underlying hierarchy and modularity of the network and thence the control of its nestedness and locality. Our framework allows us to demonstrate a range of applications of this approach that include robotic reaching tasks with motion primitives, and predicting the linear and nonlinear response of elastic networks based solely on infinitesimal rigidity and sparsity, which we test using physical experiments. Our approach is thus likely to be of use broadly in dissecting the geometrical properties of floppy networks using algebraic sparsity to optimize their function and performance.

Analyzing structural features of proteins from deep-sea organisms

Protein adaptations to extreme environmental conditions are drivers in biotechnological process optimization and essential to unravel the molecular limits of life. Most proteins with such desirable adaptations are found in extremophilic organisms inhabiting extreme environments. The deep sea is such an environment and a promising resource that poses multiple extremes on its inhabitants. Conditions like high hydrostatic pressure and high or low temperature are prevalent and many deep-sea organisms tolerate multiple of these extremes. While molecular adaptations to high temperature are comparatively good described, adaptations to other extremes like high pressure are not well-understood yet. To fully unravel the molecular mechanisms of individual adaptations it is probably necessary to disentangle multifactorial adaptations. In this study, we evaluate differences of protein structures from deep-sea organisms and their respective related proteins from nondeep-sea organisms. We created a data collection of 1281 experimental protein structures from 25 deep-sea organisms and paired them with orthologous proteins. We exhaustively evaluate differences between the protein pairs with machine learning and Shapley values to determine characteristic differences in sequence and structure. The results show a reasonable discrimination of deep-sea and nondeep-sea proteins from which we distinguish correlations previously attributed to thermal stability from other signals potentially describing adaptions to high pressure. While some distinct correlations can be observed the overall picture appears intricate.

Structure and Function of Redox-Sensitive Superfolder Green Fluorescent Protein Variant

Aims: Genetically encoded green fluorescent protein (GFP)-based redox biosensors are widely used to monitor specific and dynamic redox processes in living cells. Over the last few years, various biosensors for a variety of applications were engineered and enhanced to match the organism and cellular environments, which should be investigated. In this context, the unicellular intraerythrocytic parasite Plasmodium, the causative agent of malaria, represents a challenge, as the small size of the organism results in weak fluorescence signals that complicate precise measurements, especially for cell compartment-specific observations. To address this, we have functionally and structurally characterized an enhanced redox biosensor superfolder roGFP2 (sfroGFP2). Results: SfroGFP2 retains roGFP2-like behavior, yet with improved fluorescence intensity (FI) in cellulo. SfroGFP2-based redox biosensors are pH insensitive in a physiological pH range and show midpoint potentials comparable with roGFP2-based redox biosensors. Using crystallography and rigidity theory, we identified the superfolding mutations as being responsible for improved structural stability of the biosensor in a redox-sensitive environment, thus explaining the improved FI in cellulo. Innovation: This work provides insight into the structure and function of GFP-based redox biosensors. It describes an improved redox biosensor (sfroGFP2) suitable for measuring oxidizing effects within small cells where applicability of other redox sensor variants is limited. Conclusion: Improved structural stability of sfroGFP2 gives rise to increased FI in cellulo. Fusion to hGrx1 (human glutaredoxin-1) provides the hitherto most suitable biosensor for measuring oxidizing effects in Plasmodium. This sensor is of major interest for studying glutathione redox changes in small cells, as well as subcellular compartments in general. Antioxid. Redox Signal. 37, 1-18.

PrISM: Precision for Integrative Structural Models

MOTIVATION

A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information.

RESULTS

We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models.

AVAILABILITY

PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Probing structural basis for enhanced binding of SARS-CoV-2 P.1 variant spike protein with the human ACE2 receptor

The initial step of infection by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) involves the binding of receptor binding domain (RBD) of the spike protein to the angiotensin converting enzyme 2 (ACE2) receptor. Each successive wave of SARS‐CoV‐2 reports emergence of many new variants, which is associated with mutations in the RBD as well as other parts of the spike protein. These mutations are reported to have enhanced affinity towards the ACE2 receptor as well as are also crucial for the virus transmission. Many computational and experimental studies have demonstrated the effect of individual mutation on the RBD‐ACE2 binding. However, the cumulative effect of mutations on the RBD and away from the RBD was not investigated in detail. We report here a comparative analysis on the structural communication and dynamics of the RBD and truncated S1 domain of spike protein in complex with the ACE2 receptor from SARS‐CoV‐2 wild type and its P.1 variant. Our integrative network and dynamics approaches highlighted a subtle conformational changes in the RBD as well as truncated S1 domain of spike protein at the protein contact level, responsible for the increased affinity with the ACE2 receptor. Moreover, our study also identified the commonalities and differences in the dynamics of the interactions between spike protein of SARS‐CoV‐2 wild type and its P.1 variant with the ACE2 receptor. Further, our investigation yielded an understanding towards identification of the unique RBD residues crucial for the interaction with the ACE2 host receptor. Overall, the study provides an insight for designing better therapeutics against the circulating P.1 variants as well as other future variants.

Light- and pH-dependent structural changes in cyanobacteriochrome AnPixJg2

Cyanobacteriochromes (CBCRs) are phytochrome-related photosensory proteins that play an essential role in regulating phototaxis, chromatic acclimation, and cell aggregation in cyanobacteria. Here, we apply solid-state NMR spectroscopy to the red/green GAF2 domain of the CBCR AnPixJ assembled in vitro with a uniformly ¹³C- and ¹⁵N-labeled bilin chromophore, tracking changes in electronic structure, geometry, and structural heterogeneity of the chromophore as well as intimate contacts between the chromophore and protein residues in the photocycle. Our data confirm that the bilin ring D is strongly twisted with respect to the B-C plane in both dark and photoproduct states. We also identify a greater structural heterogeneity of the bilin chromophore in the photoproduct than in the dark state. In addition, the binding pocket is more hydrated in the photoproduct. Observation of interfacial ¹H contacts of the photoproduct chromophore, together with quantum mechanics/molecular mechanics (QM/MM)-based structural models for this photoproduct, clearly suggests the presence of a biprotonated (cationic) imidazolium side-chain for a conserved histidine residue (322) at a distance of ~2.7 Å, generalizing the recent theoretical findings that explicitly link the structural heterogeneity of the dark-state chromophore to the protonation of this specific residue. Moreover, we examine pH effects on this in vitro assembled holoprotein, showing a substantially altered electronic structure and protonation of the photoproduct chromophore even with a small pH drop from 7.8 to 7.2. Our studies provide further information regarding the light- and pH-induced changes of the chromophore and the rearrangements of the hydrogen-bonding and electrostatic interaction network around it. Possible correlations between structural heterogeneity of the chromophore, protonation of the histidine residue nearby, and hydration of the pocket in both photostates are discussed.

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity.

Respiratory and C4-photosynthetic NAD-malic enzyme coexist in bundle sheath cell mitochondria and evolved via association of differentially adapted subunits

In plant mitochondria, nicotinamide adenine dinucleotide-malic enzyme (NAD-ME) has a housekeeping function in malate respiration. In different plant lineages, NAD-ME was independently co-opted in C4 photosynthesis. In the C4 Cleome species, Gynandropsis gynandra and Cleome angustifolia, all NAD-ME genes (NAD-MEα, NAD-MEβ1, and NAD-MEβ2) were affected by C4 evolution and are expressed at higher levels than their orthologs in the C3 species Tarenaya hassleriana. In T. hassleriana, the NAD-ME housekeeping function is performed by two heteromers, NAD-MEα/β1 and NAD-MEα/β2, with similar biochemical properties. In both C4 species, this role is restricted to NAD-MEα/β2. In the C4 species, NAD-MEα/β1 is exclusively present in the leaves, where it accounts for most of the enzymatic activity. Gynandropsis gynandra NAD-MEα/β1 (GgNAD-MEα/β1) exhibits high catalytic efficiency and is differentially activated by the C4 intermediate aspartate, confirming its role as the C4-decarboxylase. During C4 evolution, NAD-MEβ1 lost its catalytic activity; its contribution to the enzymatic activity results from a stabilizing effect on the associated α-subunit and the acquisition of regulatory properties. We conclude that in bundle sheath cell mitochondria of C4 species, the functions of NAD-ME as C4 photosynthetic decarboxylase and as a housekeeping enzyme coexist and are performed by isoforms that combine the same α-subunit with differentially adapted β-subunits.

Allosteric modulation of the adenosine A2A receptor by cholesterol

Cholesterol is a major component of the cell membrane and commonly regulates membrane protein function. Here, we investigate how cholesterol modulates the conformational equilibria and signaling of the adenosine A2A receptor (A2AR) in reconstituted phospholipid nanodiscs. This model system conveniently excludes possible effects arising from cholesterol-induced phase separation or receptor oligomerization and focuses on the question of allostery. GTP hydrolysis assays show that cholesterol weakly enhances the basal signaling of A2AR while decreasing the agonist EC50. Fluorine nuclear magnetic resonance (19F NMR) spectroscopy shows that this enhancement arises from an increase in the receptor's active state population and a G-protein-bound precoupled state. 19F NMR of fluorinated cholesterol analogs reveals transient interactions with A2AR, indicating a lack of high-affinity binding or direct allosteric modulation. The combined results suggest that the observed allosteric effects are largely indirect and originate from cholesterol-mediated changes in membrane properties, as shown by membrane fluidity measurements and high-pressure NMR.

LYRUS: a machine learning model for predicting the pathogenicity of missense variants

SUMMARY

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.

AVAILABILITY AND IMPLEMENTATION

LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Co-evolutionary distance predictions contain flexibility information

MOTIVATION

Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure.

RESULTS

We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rigidity through a Projective Lens

In this paper, we offer an overview of a number of results on the static rigidity and infinitesimal rigidity of discrete structures which are embedded in projective geometric reasoning, representations, and transformations. Part I considers the fundamental case of a bar–joint framework in projective d-space and places particular emphasis on the projective invariance of infinitesimal rigidity, coning between dimensions, transfer to the spherical metric, slide joints and pure conditions for singular configurations. Part II extends the results, tools and concepts from Part I to additional types of rigid structures including body-bar, body–hinge and rod-bar frameworks, all drawing on projective representations, transformations and insights. Part III widens the lens to include the closely related cofactor matroids arising from multivariate splines, which also exhibit the projective invariance. These are another fundamental example of abstract rigidity matroids with deep analogies to rigidity. We conclude in Part IV with commentary on some nearby areas.

Structure and in silico simulations of a cold-active esterase reveals its prime cold-adaptation mechanism

Here we determined the structure of a cold active family IV esterase (EstN7) cloned from Bacillus cohnii strain N1. EstN7 is a dimer with a classical α/β hydrolase fold. It has an acidic surface that is thought to play a role in cold-adaption by retaining solvation under changed water solvent entropy at lower temperatures. The conformation of the functionally important cap region is significantly different to EstN7's closest relatives, forming a bridge-like structure with reduced helical content providing greater access to the active site through more than one substrate access tunnel. However, dynamics do not appear to play a major role in cold adaption. Molecular dynamics at different temperatures, rigidity analysis, normal mode analysis and geometric simulations of motion confirm the flexibility of the cap region but suggest that the rest of the protein is largely rigid. Rigidity analysis indicates the distribution of hydrophobic tethers is appropriate to colder conditions, where the hydrophobic effect is weaker than in mesophilic conditions due to reduced water entropy. Thus, it is likely that increased substrate accessibility and tolerance to changes in water entropy are important for of EstN7's cold adaptation rather than changes in dynamics.

Molecular Insights into Site-Specific Interferon-α2a Bioconjugates Originated from PEG, LPG, and PEtOx

Conjugation of biologics with polymers modulates their pharmacokinetics, with polyethylene glycol (PEG) as the gold standard. We compared alternative polymers and two types of cyclooctyne linkers (BCN/DBCO) for bioconjugation of interferon-α2a (IFN-α2a) using 10 kDa polymers including linear mPEG, poly(2-ethyl-2-oxazoline) (PEtOx), and linear polyglycerol (LPG). IFN-α2a was azide functionalized via amber codon expansion and bioorthogonally conjugated to all cyclooctyne linked polymers. Polymer conjugation did not impact IFN-α2a's secondary structure and only marginally reduced IFN-α2a's bioactivity. In comparison to PEtOx, the LPG polymer attached via the less rigid cyclooctyne linker BCN was found to stabilize IFN-α2a against thermal stress. These findings were further detailed by molecular modeling studies which showed a modulation of protein flexibility upon PEtOx conjugation and a reduced amount of protein native contacts as compared to PEG and LPG originated bioconjugates. Polymer interactions with IFN-α2a were further assessed via a limited proteolysis (LIP) assay, which resulted in comparable proteolytic cleavage patterns suggesting weak interactions with the protein's surface. In conclusion, both PEtOx and LPG bioconjugates resulted in a similar biological outcome and may become promising PEG alternatives for bioconjugation.

Determination of kinetic properties in unimolecular dissociation of complex systems from graph theory based analysis of an ensemble of reactive trajectories

In this paper, we report how graph theory can be used to analyze an ensemble of independent molecular trajectories, which can react during the simulation time-length, and obtain structural and kinetic information. This method is totally general and here is applied to the prototypical case of gas phase fragmentation of protonated cyclo-di-glycine. This methodology allows us to analyze the whole set of trajectories in an automatic computer-based way without the need of visual inspection but by getting all the needed information. In particular, we not only determine the appearance of different products and intermediates but also characterize the corresponding kinetics. The use of colored graph and canonical labeling allows for the correct characterization of the chemical species involved. In the present case, the simulations consist of an ensemble of unimolecular fragmentation trajectories at constant energy such that from the rate constants at different energies, the threshold energy can also be obtained for both global and specific pathways. This approach allows for the characterization of ion-molecule complexes, likely through a roaming mechanism, by properly taking into account the elusive nature of such species. Finally, it is possible to directly obtain the theoretical mass spectrum of the fragmenting species if the reacting system is an ion as in the specific example.

Parameterizing elastic network models to capture the dynamics of proteins

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility

Structural flexibility plays an essential role in many biological processes. B-factor is an important indicator to measure the flexibility of protein or RNA structures. Many methods were developed to predict protein B-factors, but few studies have been done for RNA B-factor prediction. In this paper, we proposed a new method RNAbval to predict RNA B-factors using random forest. The method was developed using a comprehensive set of features, including the sequence profile and predicted solvent accessibility. RNAbval achieved an improvement of 9.2-20.5 percent over the state-of-the-art method on two benchmark test datasets. The proposed method is available at http://yanglab.nankai.edu.cn/RNAbval/.

F/G Region Rigidity is Inversely Correlated to Substrate Promiscuity of Human CYP Isoforms Involved in Metabolism

Of 57 human cytochrome P450 (CYP) enzymes, 12 metabolize 90% of xenobiotics. To our knowledge, no study has addressed the relation between enzyme dynamics and substrate promiscuity for more than three CYPs. Here, we show by constraint dilution simulations with the Constraint Network Analysis for the 12 isoforms that structural rigidity of the F/G region is significantly inversely correlated to the enzymes' substrate promiscuity. This highlights the functional importance of structural dynamics of the substrate tunnel.

The accuracy of NMR protein structures in the Protein Data Bank

The program ANSURR measures the accuracy of NMR structures by comparing rigidity obtained from experimental backbone chemical shifts and from structures. We report on ANSURR analysis of 7,000 PDB NMR ensembles within the Protein Data Bank, which can be found at ansurr.com. The accuracy of NMR structures progressively improved up until 2005, but since then, it has plateaued. Most structures have accurate secondary structure, but are generally too floppy, particularly in loops. Thus, there is a need for more experimental restraints in loops. Currently, the best predictors of accuracy are Ramachandran distribution and the number of NOE restraints per residue. The precision of structures within the ensemble correlates well with accuracy, as does the number of hydrogen bond restraints per residue. Structure accuracy is improved when other components (such as additional polypeptide chains or ligands) are included.

Aqueous ionic liquids redistribute local enzyme stability via long-range perturbation pathways

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for biocatalysis due to their unique properties. On the other hand, the incubation of enzymes in IL or aIL often reduces enzyme activity. Recent studies proposed various aIL-induced effects to explain the reduction, classified as direct effects, e.g., local dehydration or competitive inhibition, and indirect effects, e.g., structural perturbations or disturbed catalytic site integrity. However, the molecular origin of indirect effects has largely remained elusive. Here we show by multi-μs long molecular dynamics simulations, free energy computations, and rigidity analyses that aIL favorably interact with specific residues of Bacillus subtilis Lipase A (BsLipA) and modify the local structural stability of this model enzyme by inducing long-range perturbations of noncovalent interactions. The perturbations percolate over neighboring residues and eventually affect the catalytic site and the buried protein core. Validation against a complete experimental site saturation mutagenesis library of BsLipA (3620 variants) reveals that the residues of the perturbation pathways are distinguished sequence positions where substitutions highly likely yield significantly improved residual activity. Our results demonstrate that identifying these perturbation pathways and specific IL ion-residue interactions there effectively predicts focused variant libraries with improved aIL tolerance.

Comparative protein structure network analysis on 3CLpro from SARS-CoV-1 and SARS-CoV-2

The main protease M^pro, 3CL^pro is an important target from coronaviruses. In spite of having 96% sequence identity among M^pros from SARS‐CoV‐1 and SARS‐CoV‐2; the inhibitors used to block the activity of SARS‐CoV‐1 M^pro so far, were found to have differential inhibitory effect on M^pro of SARS‐CoV‐2. The possible reason could be due to the difference of few amino acids among the peptidases. Since, overall 3‐D crystallographic structure of M^pro from SARS‐CoV‐1 and SARS‐CoV‐2 is quite similar and mapping a subtle structural variation is seemingly impossible. Hence, we have attempted to study a structural comparison of SARS‐CoV‐1 and SARS‐CoV‐2 M^pro in apo and inhibitor bound states using protein structure network (PSN) based approach at contacts level. The comparative PSNs analysis of apo M^pros from SARS‐CoV‐1 and SARS‐CoV‐2 uncovers small but significant local changes occurring near the active site region and distributed throughout the structure. Additionally, we have shown how inhibitor binding perturbs the PSG and the communication pathways in M^pros. Moreover, we have also investigated the network connectivity on the quaternary structure of M^pro and identified critical residue pairs for complex formation using three centrality measurement parameters along with the modularity analysis. Taken together, these results on the comparative PSN provide an insight into conformational changes that may be used as an additional guidance towards specific drug development.

Promiscuous Esterases Counterintuitively Are Less Flexible than Specific Ones

Understanding mechanisms of promiscuity is increasingly important from a fundamental and application point of view. As to enzyme structural dynamics, more promiscuous enzymes generally have been recognized to also be more flexible. However, examples for the opposite received much less attention. Here, we exploit comprehensive experimental information on the substrate promiscuity of 147 esterases tested against 96 esters together with computationally efficient rigidity analyses to understand the molecular origin of the observed promiscuity range. Unexpectedly, our data reveal that promiscuous esterases are significantly less flexible than specific ones, are significantly more thermostable, and have a significantly increased specific activity. These results may be reconciled with a model according to which structural flexibility in the case of specific esterases serves for conformational proofreading. Our results signify that an esterase sequence space can be screened by rigidity analyses for promiscuous esterases as starting points for further exploration in biotechnology and synthetic chemistry.

Delineating the conformational landscape of the adenosine A2A receptor during G protein coupling

G-protein-coupled receptors (GPCRs) represent a ubiquitous membrane protein family and are important drug targets. Their diverse signaling pathways are driven by complex pharmacology arising from a conformational ensemble rarely captured by structural methods. Here, fluorine nuclear magnetic resonance spectroscopy (¹⁹F NMR) is used to delineate key functional states of the adenosine A_2A receptor (A_2AR) complexed with heterotrimeric G protein (Gα_sβ₁γ₂) in a phospholipid membrane milieu. Analysis of A_2AR spectra as a function of ligand, G protein, and nucleotide identifies an ensemble represented by inactive states, a G-protein-bound activation intermediate, and distinct nucleotide-free states associated with either partial- or full-agonist-driven activation. The Gβγ subunit is found to be critical in facilitating ligand-dependent allosteric transmission, as shown by ¹⁹F NMR, biochemical, and computational studies. The results provide a mechanistic basis for understanding basal signaling, efficacy, precoupling, and allostery in GPCRs.

Monoclonal antibody stability can be usefully monitored using the excitation-energy-dependent fluorescence edge-shift

Among the major challenges in the development of biopharmaceuticals are structural heterogeneity and aggregation. The development of a successful therapeutic monoclonal antibody (mAb) requires both a highly active and also stable molecule. Whilst a range of experimental (biophysical) approaches exist to track changes in stability of proteins, routine prediction of stability remains challenging. The fluorescence red edge excitation shift (REES) phenomenon is sensitive to a range of changes in protein structure. Based on recent work, we have found that quantifying the REES effect is extremely sensitive to changes in protein conformational state and dynamics. Given the extreme sensitivity, potentially this tool could provide a ‘fingerprint’ of the structure and stability of a protein. Such a tool would be useful in the discovery and development of biopharamceuticals and so we have explored our hypothesis with a panel of therapeutic mAbs. We demonstrate that the quantified REES data show remarkable sensitivity, being able to discern between structurally identical antibodies and showing sensitivity to unfolding and aggregation. The approach works across a broad concentration range (µg–mg/ml) and is highly consistent. We show that the approach can be applied alongside traditional characterisation testing within the context of a forced degradation study (FDS). Most importantly, we demonstrate the approach is able to predict the stability of mAbs both in the short (hours), medium (days) and long-term (months). The quantified REES data will find immediate use in the biopharmaceutical industry in quality assurance, formulation and development. The approach benefits from low technical complexity, is rapid and uses instrumentation which exists in most biochemistry laboratories without modification.

Allosteric signaling in C-linker and cyclic nucleotide-binding domain of HCN2 channels

Opening of hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels is controlled by membrane hyperpolarization and binding of cyclic nucleotides to the tetrameric cyclic nucleotide-binding domain (CNBD), attached to the C-linker (CL) disk. Confocal patch-clamp fluorometry revealed pronounced cooperativity of ligand binding among protomers. However, by which pathways allosteric signal transmission occurs remained elusive. Here, we investigate how changes in the structural dynamics of the CL-CNBD of mouse HCN2 upon cAMP binding relate to inter- and intrasubunit signal transmission. Applying a rigidity-theory-based approach, we identify two intersubunit and one intrasubunit pathways that differ in allosteric coupling strength between cAMP-binding sites or toward the CL. These predictions agree with results from electrophysiological and patch-clamp fluorometry experiments. Our results map out distinct routes within the CL-CNBD that modulate different cAMP-binding responses in HCN2 channels. They signify that functionally relevant submodules may exist within and across structurally discernable subunits in HCN channels.

Probing Allosteric Mechanism with Long-Range Rigidity Transmission Across Protein Networks

Allosteric transmission refers to regulation of protein function at a distance. "Allostery" involves regulation and/or signal transduction induced by a perturbation event. Allostery, which has been coined the "second secret of life," is a fundamental property of most dynamics proteins. Most of critical questions surrounding allostery are largely unresolved. One of the key puzzles is to describe the physical mechanism of distant coupled conformational change. Another hot research area surrounding allostery is detection of allosteric pathways or regions (residues) in the protein that are the most critical for transmission of allosteric information. Using techniques inspired by mathematical rigidity theory and mechanical linkages, we have previously proposed a mechanistic model and description of allosteric transmission and an accompanying computational method, the Rigidity Transmission Allostery (RTA) algorithm. The RTA algorithm and method are designed to predict if mechanical perturbation of rigidity, for example, due to ligand binding, at one site of the protein can transmit and propagate across a protein structure and in turn cause a change in available conformational degrees of freedom and a change in conformation at a second distant site, equivalently resulting in allosteric transmission. The RTA algorithm is computationally very fast and can rapidly scan many unknown sites for allosteric transmission, identifying potential novel allosteric sites and quantify their allosteric effect. In this chapter we will discuss the rigidity-based mechanistic model of allosteric communication. As a case illustrative study, we will demonstrate RTA analysis on a G protein coupled receptor (GPCR) human adenosine A_2A receptor. Our method gives important implications and a novel prospective for general mechanistic description of allosteric communication.