Showing papers on "Structural biology published in 2023"

Protein complexes in cells by AI‐assisted structural proteomics

Abstract: Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enables a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry, cofractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interface, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Dissecting the structural heterogeneity of proteins by native mass spectrometry

Abstract: A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher‐order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the “bottom‐up” approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher‐order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the “top‐down” approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in‐depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero‐complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near‐native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

Bacteriophage T4 Head: Structure, Assembly, and Genome Packaging

Abstract: Bacteriophage (phage) T4 has served as an extraordinary model to elucidate biological structures and mechanisms. Recent discoveries on the T4 head (capsid) structure, portal vertex, and genome packaging add a significant body of new literature to phage biology. Head structures in unexpanded and expanded conformations show dramatic domain movements, structural remodeling, and a ~70% increase in inner volume while creating high-affinity binding sites for the outer decoration proteins Soc and Hoc. Small changes in intercapsomer interactions modulate angles between capsomer planes, leading to profound alterations in head length. The in situ cryo-EM structure of the symmetry-mismatched portal vertex shows the remarkable structural morphing of local regions of the portal protein, allowing similar interactions with the capsid protein in different structural environments. Conformational changes in these interactions trigger the structural remodeling of capsid protein subunits surrounding the portal vertex, which propagate as a wave of expansion throughout the capsid. A second symmetry mismatch is created when a pentameric packaging motor assembles at the outer “clip” domains of the dodecameric portal vertex. The single-molecule dynamics of the packaging machine suggests a continuous burst mechanism in which the motor subunits adjusted to the shape of the DNA fire ATP hydrolysis, generating speeds as high as 2000 bp/s.

Onto Grid Purification and 3D Reconstruction of Protein Complexes Using Matrix-Landing Native Mass Spectrometry.

Abstract: Addressing mixtures and heterogeneity in structural biology requires approaches that can differentiate and separate structures based on mass and conformation. Mass spectrometry (MS) provides tools for measuring and isolating gas-phase ions. The development of native MS including electrospray ionization allowed for manipulation and analysis of intact noncovalent biomolecules as ions in the gas phase, leading to detailed measurements of structural heterogeneity. Conversely, transmission electron microscopy (TEM) generates detailed images of biomolecular complexes that show an overall structure. Our matrix-landing approach uses native MS to probe and select biomolecular ions of interest for subsequent TEM imaging, thus unifying information on mass, stoichiometry, heterogeneity, etc., available via native MS with TEM images. Here, we prepare TEM grids of protein complexes purified via quadrupolar isolation and matrix-landing and generate 3D reconstructions of the isolated complexes. Our results show that these complexes maintain their structure through gas-phase isolation.

Errors in structural biology are not the exception

Abstract: During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future.

Bringing Structure to Cell Biology with Cryo-Electron Tomography.

Abstract: Recent advances in cryo-electron microscopy have marked only the beginning of the potential of this technique. To bring structure into cell biology, the modality of cryo-electron tomography has fast developed into a bona fide in situ structural biology technique where structures are determined in their native environment, the cell. Nearly every step of the cryo-focused ion beam-assisted electron tomography (cryo-FIB-ET) workflow has been improved upon in the past decade, since the first windows were carved into cells, unveiling macromolecular networks in near-native conditions. By bridging structural and cell biology, cryo-FIB-ET is advancing our understanding of structure-function relationships in their native environment and becoming a tool for discovering new biology.

Analyzing the Geometry and Dynamics of Viral Structures: A Review of Computational Approaches Based on Alpha Shape Theory, Normal Mode Analysis, and Poisson–Boltzmann Theories

Abstract: The current SARS-CoV-2 pandemic highlights our fragility when we are exposed to emergent viruses either directly or through zoonotic diseases. Fortunately, our knowledge of the biology of those viruses is improving. In particular, we have more and more structural information on virions, i.e., the infective form of a virus that includes its genomic material and surrounding protective capsid, and on their gene products. It is important to have methods that enable the analyses of structural information on such large macromolecular systems. We review some of those methods in this paper. We focus on understanding the geometry of virions and viral structural proteins, their dynamics, and their energetics, with the ambition that this understanding can help design antiviral agents. We discuss those methods in light of the specificities of those structures, mainly that they are huge. We focus on three of our own methods based on the alpha shape theory for computing geometry, normal mode analyses to study dynamics, and modified Poisson–Boltzmann theories to study the organization of ions and co-solvent and solvent molecules around biomacromolecules. The corresponding software has computing times that are compatible with the use of regular desktop computers. We show examples of their applications on some outer shells and structural proteins of the West Nile Virus.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Abstract: Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones’ rational design, also with the treatment of rare diseases in mind.

Structural biology: A golden era

Abstract: In the past 2 decades, structural biology has transformed from a single technique used on single proteins to a multimodal integrative approach. Recently, protein structure prediction algorithms have opened new avenues to address challenging biological questions.

Missing Pieces in Structure Puzzles: How Hyperpolarized NMR Spectroscopy Can Complement Structural Biology and Biochemistry

Abstract: Structure determination lies at the heart of many biochemical research programs. However, the “giants”: X‐ray diffraction, electron microscopy, molecular dynamics simulations, and nuclear magnetic resonance, among others, leave quite a few dark spots on the structural pictures drawn of proteins, nucleic acids, membranes, and other biomacromolecules. For example, structural models under physiological conditions or of short‐lived intermediates often remain out of reach of the established experimental methods. This account frames the possibility of including hyperpolarized, that is, dramatically signal‐enhanced NMR in existing workflows to fill these spots with detailed depictions. We highlight how integrating methods based on dissolution dynamic nuclear polarization can provide valuable complementary information about formerly inaccessible conformational spaces for many systems. A particular focus will be on hyperpolarized buffers to facilitate the NMR structure determination of challenging systems.

A special issue of <i>Essays in Biochemistry</i> on structural mass spectrometry

Abstract: Mass spectrometry (MS) is now established as an analytical tool to interrogate the structure and dynamics of proteins and their assemblies. An array of MS-based technologies has been developed, with each providing unique information pertaining to protein structure, and forming the heart of integrative structural biology studies. This special issue includes a collection of review articles that discuss both established and emerging structural MS methodologies, along with examples of how these technologies are being deployed to interrogate protein structure and function. Combined, this collection highlights the immense potential of the structural MS toolkit in the study of molecular mechanisms underpinning cellular homeostasis and disease.

Perspective: Structure determination of protein-ligand complexes at room temperature using X-ray diffraction approaches

Abstract: The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations

Abstract: Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.

Integrative dynamic structural biology unveils conformers essential for the oligomerization of a large GTPase

Abstract: Guanylate binding proteins (GBPs) are soluble dynamin-like proteins. They undergo a conformational transition for GTP-controlled oligomerization and disrupt membranes of intra-cellular parasites to exert their function as part of the innate immune system of mammalian cells. We apply neutron spin echo, X-ray scattering, fluorescence, and EPR spectroscopy as techniques for integrative dynamic structural biology to study the structural basis and mechanism conformational transitions in the human GBP1 (hGBP1). We mapped hGBP1's essential dynamics from nanoseconds to milliseconds by motional spectra of sub-domains. We find a GTP-independent flexibility of the C-terminal effector domain in the μs-regime and resolve structures of two distinct conformers essential for an opening of hGBP1 like a pocketknife and oligomerization. Our results show that an intrinsic flexibility, a GTP-triggered association of the GTPase-domains, and the assembly-dependent GTP-hydrolysis are functional design principles that control hGBP1's reversible oligomerization.

The Function of X-ray Crystallography in Modern Structural Biology

Abstract: In modern life science research, structural information on proteins is indispensable for understanding the mechanisms of their biological functions. Many protein structures have been determined by X-ray crystallography, which has been developed in conjunction with molecular biology experiments and synchrotron radiation development. Since the Nobel Prize in Chemistry in 2017, the rapid progress of cryo-EM hardware and analysis software has led to the use of cryo-EM single-particle analysis for structural analysis. In this article, we compare the features of protein crystallography and cryo-EM single-particle analysis and discuss the best method for protein crystallography.

Turning high-throughput structural biology into predictive inhibitor design

Abstract: A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein–ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein–ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (M Pro ), obtaining parallel measurements of over 200 protein–ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.