Evidence for a Partially Stalled γ Rotor in F1-ATPase from Hydrogen-Deuterium Exchange Experiments and Molecular Dynamics Simulations

SARS-CoV-2 external structures interacting with nanospheres using docking and molecular dynamics

Coronavirus is caused by the SARS-CoV-2 virus has shown rapid proliferation and scarcity of treatments with proven effectiveness. In this way, we simulated the hospitalization of carbon nanospheres, with external active sites of the SARS-CoV-2 virus (M-Pro, S-Gly and E-Pro), which can be adsorbed or inactivated when interacting with the nanospheres. The computational procedures performed in this work were developed with the SwissDock server for molecular docking and the GROMACS software for molecular dynamics, making it possible to extract relevant data on affinity energy, distance between molecules, free Gibbs energy and mean square deviation of atomic positions, surface area accessible to solvents. Molecular docking indicates that all ligands have an affinity for the receptor's active sites. The nanospheres interact favorably with all proteins, showing promising results, especially C60, which presented the best affinity energy and RMSD values ​​for all protein macromolecules investigated. The C60 with E-Pro exhibited the highest affinity energy of -9.361 kcal/mol, demonstrating stability in both molecular docking and molecular dynamics simulations. Our RMSD calculations indicated that the nanospheres remained predominantly stable, fluctuating within a range of 2 to 3 Å. Additionally, the analysis of other structures yielded promising results that hold potential for application in other proteases.Communicated by Ramaswamy H. Sarma.

Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction

Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Advances in Hydrogen/Deuterium Exchange Mass Spectrometry and the Pursuit of Challenging Biological Systems

Solution-phase hydrogen/deuterium exchange (HDX) coupled to mass spectrometry (MS) is a widespread tool for structural analysis across academia and the biopharmaceutical industry. By monitoring the exchangeability of backbone amide protons, HDX-MS can reveal information about higher-order structure and dynamics throughout a protein, can track protein folding pathways, map interaction sites, and assess conformational states of protein samples. The combination of the versatility of the hydrogen/deuterium exchange reaction with the sensitivity of mass spectrometry has enabled the study of extremely challenging protein systems, some of which cannot be suitably studied using other techniques. Improvements over the past three decades have continually increased throughput, robustness, and expanded the limits of what is feasible for HDX-MS investigations. To provide an overview for researchers seeking to utilize and derive the most from HDX-MS for protein structural analysis, we summarize the fundamental principles, basic methodology, strengths and weaknesses, and the established applications of HDX-MS while highlighting new developments and applications.

Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction

Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.

Integrating hydrogen-deuterium exchange mass spectrometry with molecular dynamics simulations to probe lipid-modulated conformational changes in membrane proteins

Biological membranes define the boundaries of cells and are composed primarily of phospholipids and membrane proteins. It has become increasingly evident that direct interactions of membrane proteins with their surrounding lipids play key roles in regulating both protein conformations and function. However, the exact nature and structural consequences of these interactions remain difficult to track at the molecular level. Here, we present a protocol that specifically addresses this challenge. First, hydrogen-deuterium exchange mass spectrometry (HDX-MS) of membrane proteins incorporated into nanodiscs of controlled lipid composition is used to obtain information on the lipid species that are involved in modulating the conformational changes in the membrane protein. Then molecular dynamics (MD) simulations in lipid bilayers are used to pinpoint likely lipid-protein interactions, which can be tested experimentally using HDX-MS. By bringing together the MD predictions with the conformational readouts from HDX-MS, we have uncovered key lipid-protein interactions implicated in stabilizing important functional conformations. This protocol can be applied to virtually any integral membrane protein amenable to classic biophysical studies and for which a near-atomic-resolution structure or homology model is available. This protocol takes ~4 d to complete, excluding the time for data analysis and MD simulations, which depends on the size of the protein under investigation.

HDX-MS reveals nucleotide-dependent, anti-correlated opening and closure of SecA and SecY channels of the bacterial translocon

The bacterial Sec translocon is a multi-protein complex responsible for translocating diverse proteins across the plasma membrane. For post-translational protein translocation, the Sec-channel – SecYEG – associates with the motor protein SecA to mediate the ATP-dependent transport of pre-proteins across the membrane. Previously, a diffusional-based Brownian ratchet mechanism for protein secretion has been proposed; the structural dynamics required to facilitate this mechanism remain unknown. Here, we employ hydrogen-deuterium exchange mass spectrometry (HDX-MS) to reveal striking nucleotide-dependent conformational changes in the Sec protein-channel from Escherichia coli. In addition to the ATP-dependent opening of SecY, reported previously, we observe a counteracting, and ATP-dependent, constriction of SecA around the pre-protein. ATP binding causes SecY to open and SecA to close; while, ADP produced by hydrolysis, has the opposite effect. This alternating behaviour could help impose the directionality of the Brownian ratchet for protein transport through the Sec machinery.

Weighing-up protein dynamics: the combination of native mass spectrometry and molecular dynamics simulations

Structural dynamics underpin biological function at the molecular level, yet many biophysical and structural biology approaches give only a static or averaged view of proteins. Native mass spectrometry yields spectra of the many states and interactions in the structural ensemble, but its spatial resolution is limited. Conversely, molecular dynamics simulations are innately high-resolution, but have a limited capacity for exploring all structural possibilities. The two techniques hence differ fundamentally in the information they provide, returning data that reflect different length scales and time scales, making them natural bedfellows. Here we discuss how the combination of native mass spectrometry with molecular dynamics simulations is enabling unprecedented insights into a range of biological questions by interrogating the motions of proteins, their assemblies, and interactions.