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1.
Nature ; 619(7969): 403-409, 2023 Jul.
Article in English | MEDLINE | ID: covidwho-20242865

ABSTRACT

The entry of SARS-CoV-2 into host cells depends on the refolding of the virus-encoded spike protein from a prefusion conformation, which is metastable after cleavage, to a lower-energy stable postfusion conformation1,2. This transition overcomes kinetic barriers for fusion of viral and target cell membranes3,4. Here we report a cryogenic electron microscopy (cryo-EM) structure of the intact postfusion spike in a lipid bilayer that represents the single-membrane product of the fusion reaction. The structure provides structural definition of the functionally critical membrane-interacting segments, including the fusion peptide and transmembrane anchor. The internal fusion peptide forms a hairpin-like wedge that spans almost the entire lipid bilayer and the transmembrane segment wraps around the fusion peptide at the last stage of membrane fusion. These results advance our understanding of the spike protein in a membrane environment and may guide development of intervention strategies.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Cryoelectron Microscopy , Lipid Bilayers , Virus Internalization , Membrane Fusion , Protein Conformation
2.
Nat Chem ; 15(7): 998-1005, 2023 07.
Article in English | MEDLINE | ID: covidwho-2324972

ABSTRACT

γ-Amino acids can play important roles in the biological activities of natural products; however, the ribosomal incorporation of γ-amino acids into peptides is challenging. Here we report how a selection campaign employing a non-canonical peptide library containing cyclic γ2,4-amino acids resulted in the discovery of very potent inhibitors of the SARS-CoV-2 main protease (Mpro). Two kinds of cyclic γ2,4-amino acids, cis-3-aminocyclobutane carboxylic acid (γ1) and (1R,3S)-3-aminocyclopentane carboxylic acid (γ2), were ribosomally introduced into a library of thioether-macrocyclic peptides. One resultant potent Mpro inhibitor (half-maximal inhibitory concentration = 50 nM), GM4, comprising 13 residues with γ1 at the fourth position, manifests a 5.2 nM dissociation constant. An Mpro:GM4 complex crystal structure reveals the intact inhibitor spans the substrate binding cleft. The γ1 interacts with the S1' catalytic subsite and contributes to a 12-fold increase in proteolytic stability compared to its alanine-substituted variant. Knowledge of interactions between GM4 and Mpro enabled production of a variant with a 5-fold increase in potency.


Subject(s)
Amino Acids , COVID-19 , Amino Acids/chemistry , Antiviral Agents/chemistry , Carboxylic Acids , Peptides/chemistry , Protease Inhibitors/chemistry , Protease Inhibitors/pharmacology , Protein Conformation , SARS-CoV-2/metabolism
3.
J Chem Theory Comput ; 19(10): 2973-2984, 2023 May 23.
Article in English | MEDLINE | ID: covidwho-2314675

ABSTRACT

All atom simulations can be used to quantify conformational properties of Intrinsically Disordered Proteins (IDP). However, simulations must satisfy convergence checks to ensure observables computed from simulation are reliable and reproducible. While absolute convergence is purely a theoretical concept requiring infinitely long simulation, a more practical, yet rigorous, approach is to impose Self Consistency Checks (SCCs) to gain confidence in the simulated data. Currently there is no study of SCCs in IDPs, unlike their folded counterparts. In this paper, we introduce different criteria for self-consistency checks for IDPs. Next, we impose these SCCs to critically assess the performance of different simulation protocols using the N terminal domain of HIV Integrase and the linker region of SARS-CoV-2 Nucleoprotein as two model IDPs. All simulation protocols begin with all-atom implicit solvent Monte Carlo (MC) simulation and subsequent clustering of MC generated conformations to create the representative structures of the IDPs. These representative structures serve as the initial structure for subsequent molecular dynamics (MD) runs with explicit solvent. We conclude that generating multiple short (∼3 µs) MD simulation trajectories─all starting from the most representative MC generated conformation─and merging them is the protocol of choice due to (i) its ability to satisfy multiple SCCs, (ii) consistently reproducing experimental data, and (iii) the efficiency of running independent trajectories in parallel by harnessing multiple cores available in modern GPU clusters. Running one long trajectory (greater than 20 µs) can also satisfy the first two criteria but is less desirable due to prohibitive computation time. These findings help resolve the challenge of identifying a usable starting configuration, provide an objective measure of SCC, and establish rigorous criteria to determine the minimum length (for one long simulation) or number of trajectories needed in all-atom simulation of IDPs.


Subject(s)
COVID-19 , Intrinsically Disordered Proteins , Humans , Intrinsically Disordered Proteins/chemistry , Molecular Dynamics Simulation , Protein Conformation , SARS-CoV-2 , Solvents/chemistry
4.
J Cell Biochem ; 124(6): 861-876, 2023 06.
Article in English | MEDLINE | ID: covidwho-2294095

ABSTRACT

The spread of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants underscores the need for insights into the structural properties of its structural and non-structural proteins. The highly conserved homo-dimeric chymotrypsin-like protease (3CL MPRO ), belonging to the class of cysteine hydrolases, plays an indispensable role in processing viral polyproteins that are involved in viral replication and transcription. Studies have successfully demonstrated the role of MPRO as an attractive drug target for designing antiviral treatments because of its importance in the viral life cycle. Herein, we report the structural dynamics of six experimentally solved structures of MPRO (i.e., 6LU7, 6M03, 6WQF, 6Y2E, 6Y84, and 7BUY including both ligand-free and ligand-bound states) at different resolutions. We have employed a structure-based balanced forcefield, CHARMM36m through state-of-the-art all-atoms molecular dynamics simulations at µ-seconds scale at room temperature (303K) and pH 7.0 to explore their structure-function relationship. The helical domain-III responsible for dimerization mostly contributes to the altered conformational states and destabilization of MPRO . A keen observation of the high degree of flexibility in the P5 binding pocket adjoining domain II-III highlights the reason for observation of conformational heterogeneity among the structural ensembles of MPRO . We also observe a differential dynamics of the catalytic pocket residues His41, Cys145, and Asp187, which may lead to catalytic impairment of the monomeric proteases. Among the highly populated conformational states of the six systems, 6LU7 and 7M03 forms the most stable and compact MPRO conformation with intact catalytic site and structural integrity. Altogether, our findings from this extensive study provides a benchmark to identify physiologically relevant structures of such promising drug targets for structure-based drug design and discovery of potent drug-like compounds having clinical potential.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Protein Conformation , Cysteine Endopeptidases/metabolism , Molecular Dynamics Simulation , Protease Inhibitors/chemistry , Molecular Docking Simulation , Antiviral Agents/chemistry
5.
Trends Biochem Sci ; 48(7): 590-596, 2023 Jul.
Article in English | MEDLINE | ID: covidwho-2293793

ABSTRACT

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Proteins/chemistry , Molecular Structure , Databases, Protein , Protein Conformation
6.
Nat Methods ; 20(2): 170-173, 2023 02.
Article in English | MEDLINE | ID: covidwho-2267918
8.
Comput Biol Med ; 158: 106814, 2023 05.
Article in English | MEDLINE | ID: covidwho-2273828

ABSTRACT

This paper presents a novel framework, called PSAC-PDB, for analyzing and classifying protein structures from the Protein Data Bank (PDB). PSAC-PDB first finds, analyze and identifies protein structures in PDB that are similar to a protein structure of interest using a protein structure comparison tool. Second, the amino acids (AA) sequences of identified protein structures (obtained from PDB), their aligned amino acids (AAA) and aligned secondary structure elements (ASSE) (obtained by structural alignment), and frequent AA (FAA) patterns (discovered by sequential pattern mining), are used for the reliable detection/classification of protein structures. Eleven classifiers are used and their performance is compared using six evaluation metrics. Results show that three classifiers perform well on overall, and that FAA patterns can be used to efficiently classify protein structures in place of providing the whole AA sequences, AAA or ASSE. Furthermore, better classification results are obtained using AAA of protein structures rather than AA sequences. PSAC-PDB also performed better than state-of-the-art approaches for SARS-CoV-2 genome sequences classification.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Protein Structure, Secondary , Amino Acids , Databases, Protein , Protein Conformation
9.
Int J Mol Sci ; 24(4)2023 Feb 14.
Article in English | MEDLINE | ID: covidwho-2242566

ABSTRACT

Since November 2021, Omicron has been the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant that causes the coronavirus disease 2019 (COVID-19) and has continuously impacted human health. Omicron sublineages are still increasing and cause increased transmission and infection rates. The additional 15 mutations on the receptor binding domain (RBD) of Omicron spike proteins change the protein conformation, enabling the Omicron variant to evade neutralizing antibodies. For this reason, many efforts have been made to design new antigenic variants to induce effective antibodies in SARS-CoV-2 vaccine development. However, understanding the different states of Omicron spike proteins with and without external molecules has not yet been addressed. In this review, we analyze the structures of the spike protein in the presence and absence of angiotensin-converting enzyme 2 (ACE2) and antibodies. Compared to previously determined structures for the wildtype spike protein and other variants such as alpha, beta, delta, and gamma, the Omicron spike protein adopts a partially open form. The open-form spike protein with one RBD up is dominant, followed by the open-form spike protein with two RBD up, and the closed-form spike protein with the RBD down. It is suggested that the competition between antibodies and ACE2 induces interactions between adjacent RBDs of the spike protein, which lead to a partially open form of the Omicron spike protein. The comprehensive structural information of Omicron spike proteins could be helpful for the efficient design of vaccines against the Omicron variant.


Subject(s)
Angiotensin-Converting Enzyme 2 , COVID-19 , SARS-CoV-2 , Spike Glycoprotein, Coronavirus , Humans , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Antibodies, Neutralizing , COVID-19/virology , COVID-19 Vaccines , Mutation , Protein Binding , Protein Conformation , SARS-CoV-2/chemistry , SARS-CoV-2/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
10.
Int J Mol Sci ; 24(1)2022 Dec 31.
Article in English | MEDLINE | ID: covidwho-2242222

ABSTRACT

During coronavirus infection, three non-structural proteins, nsp3, nsp4, and nsp6, are of great importance as they induce the formation of double-membrane vesicles where the replication and transcription of viral gRNA takes place, and the interaction of nsp3 and nsp4 lumenal regions triggers membrane pairing. However, their structural states are not well-understood. We investigated the interactions between nsp3 and nsp4 by predicting the structures of their lumenal regions individually and in complex using AlphaFold2 as implemented in ColabFold. The ColabFold prediction accuracy of the nsp3-nsp4 complex was increased compared to nsp3 alone and nsp4 alone. All cysteine residues in both lumenal regions were modelled to be involved in intramolecular disulphide bonds. A linker region in the nsp4 lumenal region emerged as crucial for the interaction, transitioning to a structured state when predicted in complex. The key interactions modelled between nsp3 and nsp4 appeared stable when the transmembrane regions of nsp3 and nsp4 were added to the modelling either alone or together. While molecular dynamics simulations (MD) demonstrated that the proposed model of the nsp3 lumenal region on its own is not stable, key interactions between nsp and nsp4 in the proposed complex model appeared stable after MD. Together, these observations suggest that the interaction is robust to different modelling conditions. Understanding the functional importance of the nsp4 linker region may have implications for the targeting of double membrane vesicle formation in controlling coronavirus infection.


Subject(s)
SARS-CoV-2 , Viral Nonstructural Proteins , SARS-CoV-2/metabolism , Viral Nonstructural Proteins/genetics , Viral Nonstructural Proteins/metabolism , Protein Conformation
11.
Biomolecules ; 13(1)2023 01 09.
Article in English | MEDLINE | ID: covidwho-2241005

ABSTRACT

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.


Subject(s)
Deep Learning , Molecular Docking Simulation , Cryoelectron Microscopy/methods , Ligands , Proteins/chemistry , Protein Conformation
12.
Nat Methods ; 19(11): 1376-1382, 2022 11.
Article in English | MEDLINE | ID: covidwho-2151063

ABSTRACT

Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.


Subject(s)
Algorithms , Proteins , Models, Molecular , Cryoelectron Microscopy/methods , Proteins/chemistry , Machine Learning , Protein Conformation
13.
Structure ; 28(8): 874-878, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-2132441

ABSTRACT

During global pandemics, the spread of information needs to be faster than the spread of the virus in order to ensure the health and safety of human populations worldwide. In our current crisis, the demand for SARS-CoV-2 drugs and vaccines highlights the importance of biological targets and their three-dimensional shape. In particular, structural biology as a field was poised to quickly respond to crises due to previous experience and expertise and because of its early adoption of open access practices.


Subject(s)
Betacoronavirus/chemistry , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Viral Proteins/chemistry , COVID-19 , Coronavirus 3C Proteases , Coronavirus RNA-Dependent RNA Polymerase , Cysteine Endopeptidases/chemistry , Databases, Protein , Humans , Models, Molecular , Molecular Biology , Protein Conformation , RNA-Dependent RNA Polymerase/chemistry , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/chemistry , Viral Nonstructural Proteins/chemistry
14.
J Phys Chem B ; 126(46): 9465-9475, 2022 Nov 24.
Article in English | MEDLINE | ID: covidwho-2106303

ABSTRACT

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Markov Chains , Protein Conformation , Algorithms , Molecular Dynamics Simulation
15.
Adv Protein Chem Struct Biol ; 132: 221-242, 2022.
Article in English | MEDLINE | ID: covidwho-2003777

ABSTRACT

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.


Subject(s)
Intrinsically Disordered Proteins , Intrinsically Disordered Proteins/metabolism , Protein Conformation , Proteome/genetics , Viral Proteins
16.
Science ; 377(6608): 819-820, 2022 08 19.
Article in English | MEDLINE | ID: covidwho-2001760

ABSTRACT

Molecular structures provide a road map for understanding and controlling B cell receptor activation.


Subject(s)
CD79 Antigens , Immunoglobulin M , CD79 Antigens/chemistry , Cryoelectron Microscopy , Humans , Immunoglobulin M/chemistry , Protein Conformation
17.
Faraday Discuss ; 240(0): 184-195, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-1984449

ABSTRACT

AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.


Subject(s)
Computational Biology , Proteins , Models, Molecular , Amino Acid Sequence , Proteins/chemistry , Machine Learning , Protein Conformation
18.
PLoS Pathog ; 18(7): e1010583, 2022 07.
Article in English | MEDLINE | ID: covidwho-1974332

ABSTRACT

The spike (S) protein of SARS-CoV-2 has been observed in three distinct pre-fusion conformations: locked, closed and open. Of these, the function of the locked conformation remains poorly understood. Here we engineered a SARS-CoV-2 S protein construct "S-R/x3" to arrest SARS-CoV-2 spikes in the locked conformation by a disulfide bond. Using this construct we determined high-resolution structures confirming that the x3 disulfide bond has the ability to stabilize the otherwise transient locked conformations. Structural analyses reveal that wild-type SARS-CoV-2 spike can adopt two distinct locked-1 and locked-2 conformations. For the D614G spike, based on which all variants of concern were evolved, only the locked-2 conformation was observed. Analysis of the structures suggests that rigidified domain D in the locked conformations interacts with the hinge to domain C and thereby restrains RBD movement. Structural change in domain D correlates with spike conformational change. We propose that the locked-1 and locked-2 conformations of S are present in the acidic high-lipid cellular compartments during virus assembly and egress. In this model, release of the virion into the neutral pH extracellular space would favour transition to the closed or open conformations. The dynamics of this transition can be altered by mutations that modulate domain D structure, as is the case for the D614G mutation, leading to changes in viral fitness. The S-R/x3 construct provides a tool for the further structural and functional characterization of the locked conformations of S, as well as how sequence changes might alter S assembly and regulation of receptor binding domain dynamics.


Subject(s)
COVID-19 , SARS-CoV-2 , Disulfides , Humans , Protein Binding , Protein Conformation , Spike Glycoprotein, Coronavirus/metabolism
19.
Faraday Discuss ; 240(0): 196-209, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-1972674

ABSTRACT

Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Cryoelectron Microscopy/methods , Models, Molecular , Protein Conformation , Software
20.
Phys Chem Chem Phys ; 24(29): 17723-17743, 2022 Jul 27.
Article in English | MEDLINE | ID: covidwho-1947641

ABSTRACT

Dissecting the regulatory principles underlying function and activity of the SARS-CoV-2 spike protein at the atomic level is of paramount importance for understanding the mechanisms of virus transmissibility and immune escape. In this work, we introduce a hierarchical computational approach for atomistic modeling of allosteric mechanisms in the SARS-CoV-2 Omicron spike proteins and present evidence of a frustration-based allostery as an important energetic driver of the conformational changes and spike activation. By examining conformational landscapes and the residue interaction networks in the SARS-CoV-2 Omicron spike protein structures, we have shown that the Omicron mutational sites are dynamically coupled and form a central engine of the allosterically regulated spike machinery that regulates the balance and tradeoffs between conformational plasticity, protein stability, and functional adaptability. We have found that the Omicron mutational sites at the inter-protomer regions form regulatory hotspot clusters that control functional transitions between the closed and open states. Through perturbation-based modeling of allosteric interaction networks and diffusion analysis of communications in the closed and open spike states, we have quantified the allosterically regulated activation mechanism and uncover specific regulatory roles of the Omicron mutations. Atomistic reconstruction of allosteric communication pathways and kinetic modeling using Markov transient analysis reveal that the Omicron mutations form the inter-protomer electrostatic bridges that operate as a network of coupled regulatory switches that could control global conformational changes and signal transmission in the spike protein. The results of this study have revealed distinct and yet complementary roles of the Omicron mutation sites as a network of hotspots that enable allosteric modulation of structural stability and conformational changes which are central for spike activation and virus transmissibility.


Subject(s)
COVID-19 , Spike Glycoprotein, Coronavirus , Allosteric Regulation , Humans , Molecular Dynamics Simulation , Mutation , Protein Conformation , Protein Stability , Protein Subunits , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/metabolism
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