Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

Protein Modeling with DEER Spectroscopy

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

Molecular basis of pyruvate transport and inhibition of the human mitochondrial pyruvate carrier

The mitochondrial pyruvate carrier transports pyruvate, produced by glycolysis from sugar molecules, into the mitochondrial matrix, as a crucial transport step in eukaryotic energy metabolism. The carrier is a drug target for the treatment of cancers, diabetes mellitus, neurodegeneration, and metabolic dysfunction-associated steatotic liver disease. We have solved the structure of the human MPC1L/MPC2 heterodimer in the inward- and outward-open states by cryo-electron microscopy, revealing its alternating access rocker-switch mechanism. The carrier has a central binding site for pyruvate, which contains an essential lysine and histidine residue, important for its ΔpH-dependent transport mechanism. We have also determined the binding poses of three chemically distinct inhibitor classes, which exploit the same binding site in the outward-open state by mimicking pyruvate interactions and by using aromatic stacking interactions.

Enabling structural biological electron paramagnetic resonance spectroscopy in membrane proteins through spin labelling

Pulsed dipolar electron paramagnetic resonance spectroscopy (PDS), combined with site-directed spin-labelling, represents a powerful tool for the investigation of biomacromolecules, emerging as a keystone approach in structural biology. Increasingly, PDS is applied to study highly complex integral membrane protein systems, such as mechanosensitive ion channels, transporters, G-protein coupled receptors, ion pumps, and outer membrane proteins elucidating their dynamics and revealing conformational ensembles. Indeed, PDS offers a platform to study intermediate or lowly-populated states that are otherwise invisible to other modern methods, such as X-ray crystallography, cryo-EM, and hydrogen-deuterium exchange-mass spectrometry. Importantly, advances in spin labelling strategies welcome a new era of membrane protein investigation under near-native or in-cell conditions. Here, we review recent integral membrane protein PDS applications, and highlight well-suited, emerging spin labelling strategies that show promise for future studies.

Conformations of influenza A M2 protein in DOPC/DOPS and E. coli native lipids and proteins

We compared the conformations of the transmembrane domain (TMD) of influenza A M2 (IM2) protein reconstituted in 1,2-dioleoyl-sn-glycero-3-phosphocholine/1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPC/DOPS) bilayers to those in isolated Escherichia coli (E. coli) membranes, having preserved its native proteins and lipids. IM2 is a single-pass transmembrane protein known to assemble into a homo-tetrameric proton channel. To represent this channel, we made a construct containing the IM2's TMD region flanked by the juxtamembrane residues. The single cysteine substitution, L43C, of leucine located in the bilayer polar region was paramagnetically tagged with a methanethiosulfonate nitroxide label for the electron spin resonance (ESR) study. For this particular residue, we probed the conformations of the spin-labeled IM2 reconstituted in DOPC/DOPS and isolated E. coli membranes using continuous-wave ESR and double electron-electron resonance (DEER) spectroscopy. The total protein-to-lipid molar ratio spanned the range from 1:230 to 1:10,400. The continuous-wave ESR spectra corresponded to very slow spin-label motion in both environments. In all cases, the DEER data were reconstructed into distance distributions with well-resolved peaks at 1.68 and 2.37 nm in distance and amplitude ratios of 1.41 ± 0.2 and 2:1, respectively. This suggests four nitroxide spin labels located at the corners of a square, indicative of an axially symmetric tetramer. The distance modeling of DEER data with molecular modeling software applied to the NMR molecular structures (PDB: 2L0J) confirmed the symmetry and closed state of the C-terminal exit pore of the IM2 TMD tetramer in agreement with the model. Thus, we can conclude that, under conditions of pH 7.4 used in this study, IM2 TMD has similar conformations in model lipid bilayers and membranes made of native E. coli lipids and proteins of comparable thickness and fluidity, notwithstanding the complexity of the E. coli membranes caused by their lipid diversity and the abundance of integral and peripheral membrane proteins.

Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR 15N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

Deep learning for protein structure prediction and design-progress and applications

Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.

A comparative study of influenza A M2 protein conformations in DOPC/DOPS liposomes and in native E. coli membranes

We compared the conformations of the transmembrane domain (TMD) of influenza A M2 (IAM2) protein reconstituted at pH 7.4 in DOPC/DOPS bilayers to those in isolated E. coli membranes, having preserved its native proteins and lipids. IAM2 is a single-pass transmembrane protein known to assemble into homo-tetrameric proton channel. To represent this channel, we made a construct containing the IAM2's TMD region flanked by the juxtamembrane residues. The single cysteine substitute, L43C, of leucine located in the bilayer polar region was paramagnetically tagged with a methanethiosulfonate nitroxide label for the ESR (electron spin resonance) study. We compared the conformations of the spin-labeled IAM2 residing in DOPC/DOPS and native E. coli membranes using continuous-wave (CW) ESR and double electron-electron resonance (DEER) spectroscopy. The total protein-to-lipid molar ratio spanned the range from 1:230 to 1:10,400⩦ The CW ESR spectra corresponded to a nearly rigid limit spin label dynamics in both environments. In all cases, the DEER data were reconstructed into the distance distributions showing well-resolved peaks at 1.68 nm and 2.37 nm. The peak distance ratio was 1.41±0.2 and the amplitude ratio was 2:1. This is what one expects from four nitroxide spin-labels located at the corners of a square, indicative of an axially symmetric tetramer. Distance modeling of DEER data with molecular modeling software applied to the NMR molecular structures (PDB: 2L0J) confirmed the symmetry and closed state of the C-terminal exit pore of the IAM2 tetramer in agreement with the NMR model. Thus, we can conclude that IAM2 TMD has similar conformations in model and native E. coli membranes of comparable thickness and fluidity, notwithstanding the complexity of the E. coli membranes caused by their lipid diversity and the abundance of integral and peripheral membrane proteins.

Integrating Electron Paramagnetic Resonance Spectroscopy and Computational Modeling to Measure Protein Structure and Dynamics

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large-amplitude conformational transition, a coarse-grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE-MD including all intermediate states.

A DNA unwinding equilibrium serves as a checkpoint for CRISPR-Cas12a target discrimination

CRISPR-associated proteins such as Cas9 and Cas12a are programable RNA-guided nucleases that have emerged as powerful tools for genome manipulation and molecular diagnostics. However, these enzymes are prone to cleaving off-target sequences that contain mismatches between the RNA guide and DNA protospacer. In comparison to Cas9, Cas12a has demonstrated distinct sensitivity to protospacer-adjacent-motif (PAM) distal mismatches, and the molecular basis of Cas12a's enhanced target discrimination is of great interest. In this study, we investigated the mechanism of Cas12a target recognition using a combination of site-directed spin labeling, fluorescent spectroscopy, and enzyme kinetics. With a fully matched RNA guide, the data revealed an inherent equilibrium between a DNA unwound state and a DNA-paired duplex-like state. Experiments with off-target RNA guides and pre-nicked DNA substrates identified the PAM-distal DNA unwinding equilibrium as a mismatch sensing checkpoint prior to the first step of DNA cleavage. The finding sheds light on the distinct targeting mechanism of Cas12a and may better inform CRISPR based biotechnology developments.

Darobactin B Stabilises a Lateral-Closed Conformation of the BAM Complex in E. coli Cells

The β-barrel assembly machinery (BAM complex) is essential for outer membrane protein (OMP) folding in Gram-negative bacteria, and represents a promising antimicrobial target. Several conformational states of BAM have been reported, but all have been obtained under conditions which lack the unique features and complexity of the outer membrane (OM). Here, we use Pulsed Electron-Electron Double Resonance (PELDOR, or DEER) spectroscopy distance measurements to interrogate the conformational ensemble of the BAM complex in E. coli cells. We show that BAM adopts a broad ensemble of conformations in the OM, while in the presence of the antibiotic darobactin B (DAR-B), BAM's conformational equilibrium shifts to a restricted ensemble consistent with the lateral closed state. Our in-cell PELDOR findings are supported by new cryoEM structures of BAM in the presence and absence of DAR-B. This work demonstrates the utility of PELDOR to map conformational changes in BAM within its native cellular environment.

Darobactin B Stabilises a Lateral-Closed Conformation of the BAM Complex in E. coli Cells

The β-barrel assembly machinery (BAM complex) is essential for outer membrane protein (OMP) folding in Gram-negative bacteria, and represents a promising antimicrobial target. Several conformational states of BAM have been reported, but all have been obtained under conditions which lack the unique features and complexity of the outer membrane (OM). Here, we use Pulsed Electron-Electron Double Resonance (PELDOR, or DEER) spectroscopy distance measurements to interrogate the conformational ensemble of the BAM complex in E. coli cells. We show that BAM adopts a broad ensemble of conformations in the OM, while in the presence of the antibiotic darobactin B (DAR-B), BAM's conformational equilibrium shifts to a restricted ensemble consistent with the lateral closed state. Our in-cell PELDOR findings are supported by new cryoEM structures of BAM in the presence and absence of DAR-B. This work demonstrates the utility of PELDOR to map conformational changes in BAM within its native cellular environment.

Two putative glutamate decarboxylases of Streptococcus pneumoniae as possible antigens for the production of anti-GAD65 antibodies leading to type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) has been increasing in prevalence in the last decades and has become a global burden. Autoantibodies against human glutamate decarboxylase (GAD65) are among the first to be detected at the onset of T1DM. Diverse viruses have been proposed to be involved in the triggering of T1DM because of molecular mimicry, i.e., similarity between parts of some viral proteins and one or more epitopes of GAD65. However, the possibility that bacterial proteins might also be responsible for GAD65 mimicry has been seldom investigated. To date, many genomes of Streptococcus pneumoniae (the pneumococcus), a prominent human pathogen particularly prevalent among children and the elderly, have been sequenced. A dataset of more than 9000 pneumococcal genomes was mined and two different (albeit related) genes (gadA and gadB), presumably encoding two glutamate decarboxylases similar to GAD65, were found. The various gadASpn alleles were present only in serotype 3 pneumococci belonging to the global lineage GPSC83, although some homologs have also been discovered in two subspecies of Streptococcus constellatus (pharyngis and viborgensis), an isolate of the group B streptococci, and several strains of Lactobacillus delbrueckii. Besides, gadBSpn alleles are present in > 10% of the isolates in our dataset and represent 16 GPSCs with 123 sequence types and 20 different serotypes. Sequence analyses indicated that gadA- and gadB-like genes have been mobilized among different bacteria either by prophage(s) or by integrative and conjugative element(s), respectively. Substantial similarities appear to exist between the putative pneumococcal glutamate decarboxylases and well-known epitopes of GAD65. In this sense, the use of broader pneumococcal conjugate vaccines such as PCV20 would prevent the majority of serotypes expressing those genes that might potentially contribute to T1DM. These results deserve upcoming studies on the possible involvement of S. pneumoniae in the etiopathogenesis and clinical onset of T1DM.

Modeling conformational states of proteins with AlphaFold

Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.

A novel approach to modeling side chain ensembles of the bifunctional spin label RX

We introduce a novel approach to modeling side chain ensembles of bifunctional spin labels. This approach utilizes rotamer libraries to generate side chain conformational ensembles. Because the bifunctional label is constrained by two attachment sites, the label is split into two monofunctional rotamers which are first attached to their respective sites, then rejoined by a local optimization in dihedral space. We validate this method against a set of previously published experimental data using the bifunctional spin label, RX. This method is relatively fast and can readily be used for both experimental analysis and protein modeling, providing significant advantages over modeling bifunctional labels with molecular dynamics simulations. Use of bifunctional labels for site directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy dramatically reduces label mobility, which can significantly improve resolution of small changes in protein backbone structure and dynamics. Coupling the use of bifunctional labels with side chain modeling methods allows for improved quantitative application of experimental SDSL EPR data to protein modeling.

A DNA Unwinding Equilibrium Serves as a Checkpoint for CRISPR-Cas12a Target Discrimination

CRISPR-associated proteins such as Cas9 and Cas12a are programable RNA-guided nucleases that have emerged as powerful tools for genome manipulation and molecular diagnostics. However, these enzymes are prone to cleaving off-target sequences that contain mismatches between the RNA guide and DNA protospacer. In comparison to Cas9, Cas12a has demonstrated distinct sensitivity to protospacer-adjacent-motif (PAM) distal mismatches, and the molecular basis of Cas12a's enhanced target discrimination is of great interest. In this study, we investigated the mechanism of Cas12a target recognition using a combination of site-directed spin labeling, fluorescent spectroscopy, and enzyme kinetics. With a fully matched RNA guide, the data revealed an inherent equilibrium between a DNA unwound state and a DNA-paired duplex-like state. Experiments with off-target RNA guides and pre-nicked DNA substrates identified the PAM-distal DNA unwinding equilibrium as a mismatch sensing checkpoint prior to the first step of DNA cleavage. The data sheds light on the distinct targeting mechanism of Cas12a and may better inform CRISPR based biotechnology developments.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Targeting in silico GPCR conformations with ultra-large library screening for hit discovery

The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.

chiLife: An open-source Python package for in silico spin labeling and integrative protein modeling

Here we introduce chiLife, a Python package for site-directed spin label (SDSL) modeling for electron paramagnetic resonance (EPR) spectroscopy, in particular double electron-electron resonance (DEER). It is based on in silico attachment of rotamer ensemble representations of spin labels to protein structures. chiLife enables the development of custom protein analysis and modeling pipelines using SDSL EPR experimental data. It allows the user to add custom spin labels, scoring functions and spin label modeling methods. chiLife is designed with integration into third-party software in mind, to take advantage of the diverse and rapidly expanding set of molecular modeling tools available with a Python interface. This article describes the main design principles of chiLife and presents a series of examples.

Characterizing the Specific Recognition of Xanthurenic Acid by GEP1 and GEP1-GCα Interactions in cGMP Signaling Pathway in Gametogenesis of Malaria Parasites

Gametogenesis is an essential step for malaria parasite transmission and is activated in mosquito by signals including temperature drop, pH change, and mosquito-derived xanthurenic acid (XA). Recently, a membrane protein gametogenesis essential protein 1 (GEP1) was found to be responsible for sensing these signals and interacting with a giant guanylate cyclase α (GCα) to activate the cGMP-PKG-Ca²⁺ signaling pathway for malaria parasite gametogenesis. However, the molecular mechanisms for this process remain unclear. In this study, we used AlphaFold2 to predict the structure of GEP1 and found that it consists of a conserved N-terminal helical domain and a transmembrane domain that adopts a structure similar to that of cationic amino acid transporters. Molecular docking results showed that XA binds to GEP1 via a pocket similar to the ligand binding sites of known amino acid transporters. In addition, truncations of this N-terminal sequence significantly enhanced the expression, solubility, and stability of GEP1. In addition, we found that GEP1 interacts with GCα via its C-terminal region, which is interrupted by mutations of a few conserved residues. These findings provide further insights into the molecular mechanism for the XA recognition by GEP1 and the activation of the gametogenesis of malaria parasites through GEP1-GCα interaction.