Biophysical Techniques in Structural Biology

Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.

Accessing Properties of Molecular Compounds Involved in Cellular Metabolic Processes with Electron Paramagnetic Resonance, Raman Spectroscopy, and Differential Scanning Calorimetry

In this work, we review some physical methods of macroscopic experiments, which have been recently argued to be promising for the acquisition of valuable characteristics of biomolecular structures and interactions. The methods we focused on are electron paramagnetic resonance spectroscopy, Raman spectroscopy, and differential scanning calorimetry. They were chosen since it can be shown that they are able to provide a mutually complementary picture of the composition of cellular envelopes (with special attention paid to mycobacteria), transitions between their molecular patterning, and the response to biologically active substances (reactive oxygen species and their antagonists-antioxidants-as considered in our case study).

Microwave treatment on structure and digestibility characteristics of Spirulina platensis protein

As a novel protein resource, the low digestibility of Spirulina platensis protein (SPP) limits its large-scale application. From the perspective of food processing methods, different heating treatments were explored to improve the structure and digestibility of SPP. In this study, SPP was heated by water bath and microwave at the same heating rate and heating temperature. Microwave accelerated protein denaturation and structure unfolded as the heating intensity increases, causing more exposed hydrophobic residues and enhancing surface hydrophobicity. The data of free sulfhydryl group, particle size, and gel electrophoresis, showed that microwave treatment promoted the formation of protein aggregates. The structural changes can potentially improve the accessibility of digestive enzymes, promote the in vitro digestibility rate, and further accelerate the production of small molecular peptides and the release of free amino acids. This study provided an innovative approach to improve the digestibility and therefore the utilization efficiency of SPP.

Effects of Hydrogen/Deuterium Exchange on Protein Stability in Solution and in the Gas Phase

Mass spectrometry (MS)-based techniques are widely used for probing protein structure and dynamics in solution. H/D exchange (HDX)-MS is one of the most common approaches in this context. HDX is often considered to be a "benign" labeling method, in that it does not perturb protein behavior in solution. However, several studies have reported that D₂O pushes unfolding equilibria toward the native state. The origin, and even the existence of this protein stabilization remain controversial. Here we conducted thermal unfolding assays in solution to confirm that deuterated proteins in D₂O are more stable, with 2-4 K higher melting temperatures than unlabeled proteins in H₂O. Previous studies tentatively attributed this phenomenon to strengthened H-bonds after deuteration, an effect that may arise from the lower zero-point vibrational energy of the deuterated species. Specifically, it was proposed that strengthened water-water bonds (W···W) in D₂O lower the solubility of nonpolar side chains. The current work takes a broader view by noting that protein stability in solution also depends on water-protein (W···P) and protein-protein (P···P) H-bonds. To help unravel these contributions, we performed collision-induced unfolding (CIU) experiments on gaseous proteins generated by native electrospray ionization. CIU profiles of deuterated and unlabeled proteins were indistinguishable, implying that P···P contacts are insensitive to deuteration. Thus, protein stabilization in D₂O is attributable to solvent effects, rather than alterations of intraprotein H-bonds. Strengthening of W···W contacts represents one possible explanation, but the stabilizing effect of D₂O can also originate from weakened W···P bonds. Future work will be required to elucidate which of these two scenarios is correct, or if both contribute to protein stabilization in D₂O. In any case, the often-repeated adage that "D-bonds are more stable than H-bonds" does not apply to intramolecular contacts in native proteins.

Using Density Functional Theory for Testing the Robustness of Mobile-Proton Molecular Dynamics Simulations on Electrosprayed Ions: Structural Implications for Gaseous Proteins

Current experiments only provide low-resolution information on gaseous protein ions generated by electrospray ionization (ESI). Molecular dynamics (MD) simulations can yield complementary insights. Unfortunately, conventional MD does not capture the mobile nature of protons in gaseous proteins. Mobile-proton MD (MPMD) overcomes this limitation. Earlier MPMD data at 300 K indicated that protein ions generated by "native" ESI retain solution-like structures with a hydrophobic core and zwitterionic exterior [Bakhtiari, M.; Konermann, L. J. Phys. Chem. B 2019, 123, 1784-1796]. MPMD redistributes protons using electrostatic and proton affinity calculations. The robustness of this approach has never been scrutinized. Here, we close this gap by benchmarking MPMD against density functional theory (DFT) at the B3LYP/6-31G* level, which is well suited for predicting proton affinities. The computational cost of DFT necessitated the use of small peptides. The MPMD energetic ranking of proton configurations was found to be consistent with DFT single-point energies, implying that MPMD can reliably identify favorable protonation sites. Peptide MPMD runs converged to DFT-optimized structures only when applying 300-500 K temperature cycling, which was necessary to prevent trapping in local minima. Temperature cycling MPMD was then applied to gaseous protein ions. Native ubiquitin converted to slightly expanded structures with a zwitterionic core and a nonpolar exterior. Our data suggest that such inside-out protein structures are intrinsically preferred in the gas phase, and that they form in ESI experiments after moderate collisional excitation. This is in contrast to native ESI (with minimal collisional excitation, simulated by MPMD at 300 K), where kinetic trapping promotes the survival of solution-like structures. In summary, this work validates the MPMD approach for simulations on gaseous peptides and proteins.

Structure-driven protein engineering for production of valuable natural products

Proteins are the most frequently used biocatalysts, and their structures determine their functions. Modifying the functions of proteins on the basis of their structures lies at the heart of protein engineering, opening a new horizon for metabolic engineering by efficiently generating stable enzymes. Many attempts at classical metabolic engineering have focused on improving specific metabolic fluxes and producing more valuable natural products by increasing gene expression levels and enzyme concentrations. However, most naturally occurring enzymes show limitations, and such limitations have hindered practical applications. Here we review recent advances in protein engineering in synthetic biology, chemoenzymatic synthesis, and plant metabolic engineering and describe opportunities for designing and constructing novel enzymes or proteins with desirable properties to obtain more active natural products.

Y12F mutation in Pseudomonas plecoglossicida S7 lipase enhances its thermal and pH stability for industrial applications: a combination of in silico and in vitro study

Appropriate amino acid substitutions are critical for protein engineering to redesign catalytic properties of industrially important enzymes like lipases. The present study aimed for improving the environmental stability of lipase from Pseudomonas plecoglossicida S7 through site-directed mutagenesis driven by computational studies. lipA gene was amplified and sequenced. Both wild type (WT) and mutant type (MT) lipase genes were expressed into the pET SUMO system. The expressed proteins were purified and characterized for pH and thermostability. The lipase gene belonged to subfamily I.1 lipase. Molecular dynamics revealed that Y12F-palmitic acid complex had a greater binding affinity (-6.3 Kcal/mol) than WT (-6.0 Kcal/mol) complex. Interestingly, MDS showed that the binding affinity of WT-complex (-130.314 ± 15.11 KJ/mol) was more than mutant complex (-108.405 ± 69.376 KJ/mol) with a marked increase in the electrostatic energy of mutant (-26.969 ± 12.646 KJ/mol) as compared to WT (-15.082 ± 13.802 KJ/mol). Y12F mutant yielded 1.27 folds increase in lipase activity at 55 °C as compared to the purified WT protein. Also, Y12F mutant showed increased activity (~ 1.2 folds each) at both pH 6 and 10. P. plecoglossicida S7. Y12F mutation altered the kinetic parameters of MT (K_m- 1.38 mM, V_max- 22.32 µM/min) as compared to WT (K_m- 1.52 mM, V_max- 29.76 µM/min) thus increasing the binding affinity of mutant lipase. Y12F mutant lipase with better pH and thermal stability can be used in biocatalysis.

The lens epithelium as a major determinant in the development, maintenance, and regeneration of the crystalline lens

The crystalline lens is a transparent and refractive biconvex structure formed by lens epithelial cells (LECs) and lens fibers. Lens opacity, also known as cataracts, is the leading cause of blindness in the world. LECs are the principal cells of lens throughout human life, exhibiting different physiological properties and functions. During the embryonic stage, LECs proliferate and differentiate into lens fibers, which form the crystalline lens. Genetics and environment are vital factors that influence normal lens development. During maturation, LECs help maintain lens homeostasis through material transport, synthesis and metabolism as well as mitosis and proliferation. If disturbed, this will result in loss of lens transparency. After cataract surgery, the repair potential of LECs is activated and the structure and transparency of the regenerative tissue depends on postoperative microenvironment. This review summarizes recent research advances on the role of LECs in lens development, homeostasis, and regeneration, with a particular focus on the role of cholesterol synthesis (eg., lanosterol synthase) in lens development and homeostasis maintenance, and how the regenerative potential of LECs can be harnessed to develop surgical strategies and improve the outcomes of cataract surgery (Fig. 1). These new insights suggest that LECs are a major determinant of the physiological and pathological state of the lens. Further studies on their molecular biology will offer possibility to explore new approaches for cataract prevention and treatment.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Quinolinate Synthase: An Example of the Roles of the Second and Outer Coordination Spheres in Enzyme Catalysis

The activation energy barrier of biochemical reactions is normally lowered by an enzyme catalyst, which directly helps the weakening of the bond(s) to be broken. In many metalloenzymes, this is a first coordination sphere effect. Besides having a direct catalytic action, enzymes can fix their reactive groups and substrates so that they are optimally positioned and also modify the water activity in the system. They can either activate substrates prior to their reaction or bind preactivated substrates, thereby drastically reducing local entropic effects. The latter type is well represented by some bisubstrate reactions, where they have been defined as "entropic traps". These can be described as "second coordination sphere" processes, but enzymes can also control the reactivity beyond this point through local conformational changes belonging to an "outer coordinate sphere" that can be modulated by substrate binding. We have chosen the [4Fe-4S] cluster-dependent enzyme quinolinate synthase to illustrate each one of these processes. In addition, this very old metalloenzyme shows low in vitro substrate binding specificity, atypical reactivity that produces dead-end products, and a unique modulation of its active site volume.

Integration of machine learning with computational structural biology of plants

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.

Computational Study on the Dynamics of Mycobacterium Tuberculosis RNA Polymerase Assembly

Gene regulation is an intricate phenomenon involving precise function of many macromolecular complexes. Molecular basis of this phenomenon is highly complex and cannot be fully understood using a single technique. Computational approaches can play a crucial role in overall understanding of functional and mechanistic features of a protein or an assembly. Large amounts of structural data pertaining to these complexes are publicly available. In this project, we took advantage of the availability of the structural information to unravel functional intricacies of Mycobacterium tuberculosis RNA polymerase upon interaction with RbpA. In this article, we discuss how the knowledge on protein structure and dynamics can be exploited to study function using various computational tools and resources. Overall, this article provides an overview of various computational methods which can be efficiently used to understand the role of any protein. We hope especially the nonexperts in the field could benefit from our article.

The NEOtrap - en route with a new single-molecule technique

This paper provides a perspective on potential applications of a new single-molecule technique, viz., the nanopore electro-osmotic trap (NEOtrap). This solid-state nanopore-based method uses locally induced electro-osmosis to form a hydrodynamic trap for single molecules. Ionic current recordings allow one to study an unlabeled protein or nanoparticle of arbitrary charge that can be held in the nanopore's most sensitive region for very long times. After motivating the need for improved single-molecule technologies, we sketch various possible technical extensions and combinations of the NEOtrap. We lay out diverse applications in biosensing, enzymology, protein folding, protein dynamics, fingerprinting of proteins, detecting post-translational modifications, and all that at the level of single proteins - illustrating the unique versatility and potential of the NEOtrap.

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.

Heterodimer Formation of the Homodimeric ABC Transporter OpuA

Many proteins have a multimeric structure and are composed of two or more identical subunits. While this can be advantageous for the host organism, it can be a challenge when targeting specific residues in biochemical analyses. In vitro splitting and re-dimerization to circumvent this problem is a tedious process that requires stable proteins. We present an in vivo approach to transform homodimeric proteins into apparent heterodimers, which then can be purified using two-step affinity-tag purification. This opens the door to both practical applications such as smFRET to probe the conformational dynamics of homooligomeric proteins and fundamental research into the mechanism of protein multimerization, which is largely unexplored for membrane proteins. We show that expression conditions are key for the formation of heterodimers and that the order of the differential purification and reconstitution of the protein into nanodiscs is important for a functional ABC-transporter complex.

Investigation of protein-ligand complexes by ligand-based NMR methods

Molecular recognition is at the base of all biological events and its knowledge at atomic level is pivotal in the development of new drug design approaches. NMR spectroscopy is one of the most widely used technique to detect and characterize transient ligand-receptor interactions in solution. In particular, ligand-based NMR approaches, including NOE-based NMR techniques, diffusion experiments and relaxation methods, are excellent tools to investigate how ligands interact with their receptors. Here we describe the key structural information that can be achieved on binding processes thanks to the combined used of advanced NMR and computational methods. Saturation Transfer Difference NMR (STD-NMR), WaterLOGSY, diffusion- and relaxation-based experiments, together with tr-NOE techniques allow, indeed, to investigate the ligand behavior when bound to a receptor, determining, among others, the epitope map of the ligand and its bioactive conformation. The combination of these NMR techniques with computational methods, including docking, molecular dynamics and CORCEMA-ST analysis, permits to define and validate an accurate 3D model of protein-ligand complexes.

Easy Synthesis of Complex Biomolecular Assemblies: Wheat Germ Cell-Free Protein Expression in Structural Biology

Cell-free protein synthesis (CFPS) systems are gaining more importance as universal tools for basic research, applied sciences, and product development with new technologies emerging for their application. Huge progress was made in the field of synthetic biology using CFPS to develop new proteins for technical applications and therapy. Out of the available CFPS systems, wheat germ cell-free protein synthesis (WG-CFPS) merges the highest yields with the use of a eukaryotic ribosome, making it an excellent approach for the synthesis of complex eukaryotic proteins including, for example, protein complexes and membrane proteins. Separating the translation reaction from other cellular processes, CFPS offers a flexible means to adapt translation reactions to protein needs. There is a large demand for such potent, easy-to-use, rapid protein expression systems, which are optimally serving protein requirements to drive biochemical and structural biology research. We summarize here a general workflow for a wheat germ system providing examples from the literature, as well as applications used for our own studies in structural biology. With this review, we want to highlight the tremendous potential of the rapidly evolving and highly versatile CFPS systems, making them more widely used as common tools to recombinantly prepare particularly challenging recombinant eukaryotic proteins.

Beyond history and "on a roll": The list of the most well-studied human protein structures and overall trends in the protein data bank

Of the roughly 20,000 canonical human protein sequences, as of January 20, 2021, 7,077 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the protein data bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 years for proteins, obtained from any organism). Growth in human protein structures is slightly faster (at 5.9 years). The information in this paper may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2021 snap-shot for the state of protein structural biology.

Double Mutant Cycles as a Tool to Address Folding, Binding, and Allostery

Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted 'energetic coupling' describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein-ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.

Membrane protein crystallography in the era of modern structural biology

The aim of structural biology has been always the study of biological macromolecules structures and their mechanistic behaviour at molecular level. To achieve its goal, multiple biophysical methods and approaches have become part of the structural biology toolbox. Considered as one of the pillars of structural biology, X-ray crystallography has been the most successful method for solving three-dimensional protein structures at atomic level to date. It is however limited by the success in obtaining well-ordered protein crystals that diffract at high resolution. This is especially true for challenging targets such as membrane proteins (MPs). Understanding structure-function relationships of MPs at the biochemical level is vital for medicine and drug discovery as they play critical roles in many cellular processes. Though difficult, structure determination of MPs by X-ray crystallography has significantly improved in the last two decades, mainly due to many relevant technological and methodological developments. Today, numerous MP crystal structures have been solved, revealing many of their mechanisms of action. Yet the field of structural biology has also been through significant technological breakthroughs in recent years, particularly in the fields of single particle electron microscopy (cryo-EM) and X-ray free electron lasers (XFELs). Here we summarise the most important advancements in the field of MP crystallography and the significance of these developments in the present era of modern structural biology.

Refinement of pairwise potentials via logistic regression to score protein-protein interactions

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the accurate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, and then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared with conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. The scripts used are available at https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR.

Exploring the potential of infrared spectroscopy in qualitative and quantitative monitoring of ovalbumin amyloid fibrillation

Amyloid fibrils are highly ordered self-assembled (poly)peptide aggregates with cross-β structural pattern. Ovalbumin was used as a model for exploring the potential of infrared spectroscopy in detecting structural transitions and quantitative monitoring of amyloid fibrillation. Low pH (pH 2) and high temperature (90 °C) over the course of 24 h were conditions applied for amyloid formation. Fibrillation of ovalbumin was monitored by ThT and ANS fluorescence, and SDS PAGE. A significant increase in ThT fluorescence with a plateau reached after 4 h of incubation, without the lag phase, was detected. Structural transitions leading to amyloid fibrillation were analysed using all three Amide regions in ATR-FTIR spectra. Significant changes were detected in Amide I and Amide III region (decrease of α-helix and increase of β-sheet peaks). To establish a fast, precise and simple method for quantitative monitoring of amyloid fibrillation, the Amide I/Amide II ratios of aggregation specific β-sheets (1625 and 1695 cm^-1, respectively) with 1540 cm^-1 as internal standard were used, resulting in good correlation (R² = 0.93 and 0.95) with the data observed by monitoring ThT fluorescence. On the other hand, assessing aggregation specific β-sheet contents by self-deconvolution showed lower correlation with ThT fluorescence (R² = 0.75 and 0.64). Here we examined structural transitions during ovalbumin fibrillation in a qualitative and quantitative manner by exploiting the full potential of Amide regions simultaneously. Secondary structure distribution was monitored using second derivative spectra in Amide I region. A novel, simple mathematical calculation for quantitative monitoring of fibrils formation was presented employing that the increase in low and high frequency aggregation specific β-sheet in Amide I region compared to the internal standard in Amide II region is suitable for fibril formation monitoring.