Protein Solvent Accessibility Research Articles

Structural and functional analysis of engineered antibodies for cancer immunotherapy: insights into protein compactness and solvent accessibility

Antibodies are crucial tools in various biomedical applications, including immunotherapy. In this study, we focused on designing and engineering antibodies to enhance their structural dynamics and functional properties. By employing advanced computational techniques and experimental validation, we gained crucial insights into the impact of specific mutations on the engineered antibodies. This study investigates the design and engineering of antibodies to improve their structural dynamics and functional properties. Structural attributes, such as protein compactness and solvent accessibility, were assessed, revealing interesting trends in anti-CD3 and anti-HER2 antibodies. Mutations in CD3 antibodies resulted in a more stable conformation, while mutant HER2 antibodies exhibited altered interaction with the target. Analysis of secondary structure assignments demonstrated significant changes in the folding and stability of the mutant antibodies compared to the wild-type counterparts. The conformational landscape of the engineered antibodies was explored, providing insights into folding pathways and binding mechanisms. Overall, the current study highlights the significance of antibody design and engineering in modulating structural dynamics and functional properties. The findings contribute to developing improved immunotherapeutic strategies by optimising antibody-based therapeutics for targeted diseases with enhanced efficacy and precision. Communicated by Ramaswamy H. Sarma

Toward a MALDI in-source decay (ISD) method for top-down analysis of protein footprinting.

Irreversible protein footprinting is a mass spectrometry-based approach in which solvent-accessible sites of a protein are modified to assess high-order protein structure. Structural insights can be gained by determining the position and extents of modification. The usual approach to obtain the "footprint" is to analyze the protein through bottom-up LC-MS/MS. In this approach, the proteins are digested to yield a mixture of peptides that are then separated by LC before locating the modification sites by MS/MS. This process consumes substantial amounts of time and is difficult to accelerate for applications that require quick and high-throughput analysis. Here, we describe employing matrix-assisted laser desorption/ionization (MALDI) in-source decay (ISD) to analyze a footprinted small test protein (ubiquitin) via a top-down approach. Matrix-assisted laser desorption/ionization is easily adapted for high-throughput analysis, and top-down strategies can avoid lengthy proteolysis and LC separation. We optimized the method with model peptides and then demonstrated its feasibility on ubiquitin submitted to two types of footprinting. We found that MALDI ISD can produce a comprehensive set of fragment ions for small proteins, affording footprinting information in a fast manner and giving results that agree with the established methods, and serve as a rough measure of protein solvent accessibility. To assist in the implementation of the MALDI approach, we developed a method of processing top-down ISD data.

IGPRED-MultiTask: A Deep Learning Model to Predict Protein Secondary Structure, Torsion Angles and Solvent Accessibility.

Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.

ProtRAP: Predicting Lipid Accessibility Together with Solvent Accessibility of Proteins in One Run

Solvent accessibility has been extensively used to characterize and predict the chemical properties of the surface residues of soluble proteins. However, there is not yet a widely accepted quantity of the same dimension for the study of lipid-accessible residues of membrane proteins. In this study, we propose that lipid accessibility, defined in a similar way to solvent accessibility, can be used to characterize the lipid-accessible residues of membrane proteins. Moreover, we developed a deep learning-based method, ProtRAP (Protein Relative Accessibility Predictor), to predict the relative lipid accessibility and relative solvent accessibility of residues from a given protein sequence, which can infer which residues are likely accessible to lipids, accessible to solvent, or buried in the protein interior in one run.

Comprehensive in-silico analysis of deleterious SNPs in APOC2 and APOA5 and their differential expression in cancer and cardiovascular diseases conditions

Genetic variations in APOC2 and APOA5 genes involve activating lipoprotein lipase (LPL), responsible for the hydrolysis of triglycerides (TG) in blood and whose impaired functions affect the TG metabolism and are associated with metabolic diseases. In this study, we investigate the biological significance of genetic variations at the DNA sequence and structural level using various computational tools. Subsequently, 8 (APOC2) and 17 (APOA5) non-synonymous SNPs (nsSNPs) were identified as high-confidence deleterious SNPs based on the effects of the mutations on protein conservation, stability, and solvent accessibility. Furthermore, based on our docking results, the interaction of native and mutant forms of the corresponding proteins with LPL depicts differences in root mean square deviation (RMSD), and binding affinities suggest that these mutations may affect their function. Furthermore, in vivo, and in vitro studies have shown that differential expression of these genes in disease conditions due to the influence of nsSNPs abundance may be associated with promoting the development of cancer and cardiovascular diseases. Preliminary screening using computational methods can be a helpful start in understanding the effects of mutations in APOC2 and APOA5 on lipid metabolism; however, further wet-lab experiments would further strengthen the conclusions drawn from the computational study.

MALDI-TOF-MS and in-depth dynamic simulations on the molecular forces determining the stability of the 4-hydroxybenzoic acid – β-Casein complex following UHT-like treatment

Previous work has suggested the ability of 4-hydroxybenzoic acid (4HBA) to bind to β -casein following ultra high temperature-like processing (UHT) in model aqueous systems. The present work confirmed directly, using MALDI-TOF-MS, the presence of covalently bound 4HBA following UHT-like treatment. In subsequent molecular dynamics simulations, the 3D structure of the β -casein molecule was modified so that the meta-C of 4HBA ring and the side chain amino group of lys32 were linked covalently. Such simulations further indicated that the covalent addition of the phenolic compound had impacted the protein density and solvent accessibility. Hydrogen bond analysis between lys32 and the remainder of the protein structure revealed that the covalent complexation supported the formation of additional hydrogen bonds. These increased from potentially 9, in the single protein molecule, to 51 in the complex with 4HBA. However, the persistence of hydrogen bonds was reduced, leading overall to decreased stability and increased protein flexibility.

Panoramic Perspective on Human Phosphosites.

Protein phosphorylation is the most common reversible post-translational modification of proteins and is key in the regulation of many cellular processes. Due to this importance, phosphorylation is extensively studied, resulting in the availability of a large amount of mass spectrometry-based phospho-proteomics data. Here, we leverage the information in these large-scale phospho-proteomics data sets, as contained in Scop3P, to analyze and characterize proteome-wide protein phosphorylation sites (P-sites). First, we set out to differentiate correctly observed P-sites from false-positive sites using five complementary site properties. We then describe the context of these P-sites in terms of the protein structure, solvent accessibility, structural transitions and disorder, and biophysical properties. We also investigate the relative prevalence of disease-linked mutations on and around P-sites. Moreover, we assess the structural dynamics of P-sites in their phosphorylated and unphosphorylated states. As a result, we show how large-scale reprocessing of available proteomics experiments can enable a more reliable view on proteome-wide P-sites. Furthermore, adding the structural context of proteins around P-sites helps uncover possible conformational switches upon phosphorylation. Moreover, by placing sites in different biophysical contexts, we show the differential preference in protein dynamics at phosphorylated sites when compared to the nonphosphorylated counterparts.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments. Communicated by Ramaswamy H. Sarma.

Carbocation Footprinting of Soluble and Transmembrane Proteins

Here, we introduce carbocations (R3C+) as laser-initiated footprinting reagents for proteins. We screened seven candidates and selected trifluomethoxy benzyl bromide (TFBB) as an effective precursor for the electrophilic trifluomethoxy benzyl carbocation (TFB+) under laser (248 nm) irradiation on the fast photochemical oxidation of proteins (FPOP) platform. Initial results demonstrate that this electrophilic cation reagent affords residue coverage of nucleophilic amino acids including H, W, M, and S. Further, the addition of TFB+ increases the hydrophobicity of the peptides so that separation of isomeric peptide products by reversed-phase LC is improved, suggesting opportunities for subresidue footprinting. Comparison of apo- and holo-myoglobin footprints shows that the TFB+ footprinting is sensitive to protein conformational change and solvent accessibility. Interestingly, because the TFB+ is amphiphilic, the reagent can potentially footprint membrane proteins as demonstrated for vitamin K epoxide reductase (VKOR) stabilized in a micelle. Not only does footprinting of the extra-membrane domain occur, but also some footprinting of the hydrophobic transmembrane domain is achieved owing to the interaction of TFB+ with the micelle. Carbocation precursors are stable and amenable for tailoring their properties and those of the incipient carbocation, enabling targeting their soluble or membrane-associated or embedded regions and distinguishing between the extra- and trans-membrane domains of membrane proteins.

OPUS-X: an open-source toolkit for protein torsion angles, secondary structure, solvent accessibility, contact map predictions and 3D folding.

MotivationThe development of an open-source platform to predict protein 1D features and 3D structure is an important task. In this paper, we report an open-source toolkit for protein 3D structure modeling, named OPUS-X. It contains three modules: OPUS-TASS2, which predicts protein torsion angles, secondary structure and solvent accessibility; OPUS-Contact, which measures the distance and orientation information between different residue pairs; and OPUS-Fold2, which uses the constraints derived from the first two modules to guide folding. ResultsOPUS-TASS2 is an upgraded version of our previous method OPUS-TASS. OPUS-TASS2 integrates protein global structure information and significantly outperforms OPUS-TASS. OPUS-Contact combines multiple raw co-evolutionary features with protein 1D features predicted by OPUS-TASS2, and delivers better results than the open-source state-of-the-art method trRosetta. OPUS-Fold2 is a complementary version of our previous method OPUS-Fold. OPUS-Fold2 is a gradient-based protein folding framework based on the differentiable energy terms in opposed to OPUS-Fold that is a sampling-based method used to deal with the non-differentiable terms. OPUS-Fold2 exhibits comparable performance to the Rosetta folding protocol in trRosetta when using identical inputs. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to any source-code-level modification.Availabilityand implementationThe code and pre-trained models of OPUS-X can be downloaded from https://github.com/OPUS-MaLab/opus_x.Supplementary information Supplementary data are available at Bioinformatics online.

SPOT-1D-Single: improving the single-sequence-based prediction of protein secondary structure, backbone angles, solvent accessibility and half-sphere exposures using a large training set and ensembled deep learning.

Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here, we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39 120 proteins deposited prior to 2016 and an ensemble of hybrid long-short-term-memory bidirectional neural network and convolutional neural network. We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12 and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12% to 74.28% by SPOT-1D-Single, compared to 69.1-72.6% by SPIDER3-Single and 70.6-73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff = 1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning. Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub. Supplementary data are available at Bioinformatics online.

Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning.

Accurate predictions of protein structure properties, for example, secondary structure and solvent accessibility, are essential in analyzing the structure and function of a protein. Position-specific scoring matrix (PSSM) features are widely used in the structure property prediction. However, some proteins may have low-quality PSSM features due to insufficient homologous sequences, leading to limited prediction accuracy. To address this limitation, we propose an enhancing scheme for PSSM features. We introduce the "Bagging MSA" (multiple sequence alignment) method to calculate PSSM features used to train our model, adopt a convolutional network to capture local context features and bidirectional long short-term memory for long-term dependencies, and integrate them under an unsupervised framework. Structure property prediction models are then built upon such enhanced PSSM features for more accurate predictions. Moreover, we develop two frameworks to evaluate the effectiveness of the enhanced PSSM features, which also bring proposed method into real-world scenarios. Empirical evaluation of CB513, CASP11, and CASP12 data sets indicates that our unsupervised enhancing scheme indeed generates more informative PSSM features for structure property prediction.

Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network.

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition. Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs. Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. Supplementary data are available at Bioinformatics online.

Structural profile matrices for predicting structural properties of proteins.

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment methods in LOMETS server, gap affine alignment method, ScanProsite, PfamScan, and HHblits. The contribution of each template is weighted by its similarity to target, which is assessed by several sequence alignment scores. For comparison, the SPMs are also computed using Homolpro, which uses BLAST for target template alignments and does not assign weights to templates. Incorporating the SPMs into DSPRED classifier, the prediction accuracy improves significantly as demonstrated by cross-validation experiments on two difficult benchmarks. The most accurate predictions are obtained using the SPMs derived by threading methods in LOMETS server. On the other hand, the computational cost of computing these SPMs was the highest.

Evaluation of NHS-Acetate and DEPC labelling for determination of solvent accessible amino acid residues in protein complexes

The activity of most proteins and protein complexes relies on the formation of defined three-dimensional structures. The analysis of these arrangements is therefore key for understanding their function and regulation in the cell. Besides the traditional structural techniques, structural mass spectrometry delivers insights into the various aspects of protein structure, including stoichiometry, protein-ligand interactions and solvent accessibility. The latter is usually obtained from labelling experiments. In this study, we evaluate two chemical labelling strategies using N-hydroxysuccinimidyl acetate and diethylpyrocarbonate as labelling reagents. We characterised the mass spectra of modified peptides and assessed labelling reactivity of individual amino acid residues in intact proteins. Importantly, we uncovered neutral losses from diethylpyrocarbonate modified amino acids improving the assignments of the peptide fragment spectra. We further established a quantitative labelling workflow to determine labelling percentage and unambiguously distinguish solvent accessible amino acid residues from stochastically labelled residues. Finally, we used ion mobility MS to explore whether labelled proteins maintain their structures and remain stable. We conclude that labelling using N-hydroxysuccinimidyl acetate and diethylpyrocarbonate delivers comparable results, however, N-hydroxysuccinimidyl acetate labelling is compatible with standard proteomic workflows while diethylpyrocarbonate labelling requires specialised experimental conditions and data analysis. SignificanceCovalent labelling is widely used to identify solvent accessible amino acid residues of proteins or protein complexes. However, with increasing sensitivity of available MS instrumentation, a high number of modified residues is usually observed making an unambiguous assignment of solvent accessible residues necessary. In this study, we establish a quantitative labelling workflow for two different labelling strategies to identify accessible amino acid residues. In addition, we characterise observed mass spectra of modified peptides and identified neutral loss of DEPC modified amino acid residues during HCD fragmentation improving their assignments.

Characterizing Cellular Proteins with In-cell Fast Photochemical Oxidation of Proteins.

Fast photochemical oxidation of proteins (FPOP) is a hydroxyl radical protein footprinting method used to characterize protein structure and interactions. FPOP uses a 248 nm excimer laser to photolyze hydrogen peroxide producing hydroxyl radicals. These radicals oxidatively modify solvent exposed side chains of 19 of the 20 amino acids. Recently, this method has been used in live cells (IC-FPOP) to study protein interactions in their native environment. The study of proteins in cells accounts for intermolecular crowding and various protein interactions that are disrupted for in vitro studies. A custom single cell flow system was designed to reduce cell aggregation and clogging during IC-FPOP. This flow system focuses the cells past the excimer laser individually, thus ensuring consistent irradiation. By comparing the extent of oxidation produced from FPOP to the protein's solvent accessibility calculated from a crystal structure, IC-FPOP can accurately probe the solvent accessible side chains of proteins.

Site-Directed Alkylation Detected by In-Gel Fluorescence (SDAF) to Determine the Topology Map and Probe the Solvent Accessibility of Membrane Proteins

The topology of helix-bundle membrane proteins provides low-resolution structural information with regard to the number and orientation of membrane-spanning helices, as well as the sidedness of intra/extra-cellular domains. In the past decades, several strategies have been developed to experimentally determine the topology of membrane proteins. However, generally, these methods are labour-intensive, time-consuming and difficult to implement for quantitative analysis. Here, we report a novel approach, site-directed alkylation detected by in-gel fluorescence (SDAF), which monitors the fluorescent band shift caused by alkylation of the EGFP-fused target membrane protein bearing one single introduced cysteine. In-gel fluorescence provides a unique readout of target membrane proteins with EGFP fusion from non-purified samples, revealing a distinct 5 kDa shift on SDS-PAGE gel due to conjugation with mPEG-MAL-5K. Using the structurally characterised bile acid transporter ASBTNM as an example, we demonstrate that SDAF generates a topology map consistent with the crystal structure. The efficiency of mPEG-MAL-5K modification at each introduced cysteine can easily be quantified and analysed, providing a useful tool for probing the solvent accessibility at a specific position of the target membrane protein.

Developing structural profile matrices for protein secondary structure and solvent accessibility prediction.

Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests. The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred. Supplementary data are available at Bioinformatics online.

Using X-ray Footprinting and Mass Spectrometry to Study the Structure and Function of Membrane Proteins.

Membrane proteins are crucial for cellular sensory cascades and metabolite transport, and hence are key pharmacological targets. Structural studies by traditional highresolution techniques are limited by the requirements for high purity and stability when handled in high concentration and nonnative buffers. Hence, there is a growing requirement for the use of alternate methods in a complementary but orthogonal approach to study the dynamic and functional aspects of membrane proteins in physiologically relevant conditions. In recent years, significant progress has been made in the field of X-ray radiolytic labeling in combination with mass spectroscopy, commonly known as X-ray Footprinting and Mass Spectrometry (XFMS), which provide residue-specific information on the solvent accessibility of proteins. In combination with both lowresolution biophysical methods and high-resolution structural data, XFMS is capable of providing valuable insights into structure and dynamics of membrane proteins, which have been difficult to obtain by standalone high-resolution structural techniques. The XFMS method has also demonstrated a unique capability for identification of structural waters and their dynamics in protein cavities at both a high degree of spatial and temporal resolution, and thus capable of identifying conformational hot-spots in transmembrane proteins. We provide a perspective on the place of XFMS amongst other structural biology methods and showcase some of the latest developments in its usage for studying conformational changes in membrane proteins.