AlphaFold2 predicts the inward-facing conformation of the multidrug transporter LmrP

DEERefiner-assisted structural refinement using pulsed dipolar spectroscopy: a study on multidrug transporter LmrP

Pulsed dipolar spectroscopy, such as double electron-electron resonance (DEER), has been underutilized in protein structure determination, despite its ability to provide valuable spatial information. In this study, we present DEERefiner, a user-friendly MATLAB-based GUI program that enables the modeling of protein structures by combining an initial structure and DEER distance restraints. We illustrate the effectiveness of DEERefiner by successfully modeling the ligand-dependent conformational changes of the proton-drug antiporter LmrP to an extracellular-open-like conformation with an impressive precision of 0.76 Å. Additionally, DEERefiner was able to uncover a previously hypothesized but experimentally unresolved proton-dependent conformation of LmrP, characterized as an extracellular-closed/partially intracellular-open conformation, with a precision of 1.16 Å. Our work not only highlights the ability of DEER spectroscopy to model protein structures but also reveals the potential of DEERefiner to advance the field by providing an accessible and applicable tool for precise protein structure modeling, thereby paving the way for deeper insights into protein function.

Elucidation of the mechanism of nuclear localization of Mexican tetra Neu4 via bipartite nuclear localization signal and less conserved regions

Nuclear sialoglycans are minor components in the nucleus, and their biological significance was not well understood. Recently, Nile tilapia Neu4 sialidase (OnNeu4) was identified and reported as the first nuclear sialidase in vertebrates. Although OnNeu4 possesses the nuclear localization signal (NLS) required for nuclear localization, other fish Neu4 sialidases, such as zebrafish and Japanese medaka, also possess NLS, but their subcellular localizations are not nucleus. To understand the nuclear localization mechanism of fish Neu4, we focused on Mexican tetra Neu4 (AmNeu4), which, unlike Neu4 in other fishes, has a bipartite NLS. AmNeu4 exhibited a wide range of optimal pH and substrate specificity, and its gene expression was specifically detected in the liver, spleen, and gut in adult fish. AmNeu4, like OnNeu4, exhibited nuclear localization, which was attenuated by importin inhibitor, and deletion of the bipartite NLS completely reduced the nuclear localization. In addition, the conjugation of the bipartite NLS of AmNeu4 made GFP show nuclear localization. To understand the mechanism of nuclear localization of AmNeu4 and OnNeu4, we compared fish Neu4 amino acid sequences and focused on the less conserved region of Neu4 sialidase (LCR). LCR-deletion mutants of AmNeu4 and OnNeu4 showed significantly reduced the nuclear localization. The LCR region in AmNeu4 and OnNeu4 possessed consecutive Ser/Thr. The Neu4 mutants in which consecutive Ser/Thr in LCR were changed to Ala or deleted significantly suppressed the nuclear localization. These results suggest that the nuclear localization of Neu4 in Nile tilapia and Mexican tetra may be regulated by NLS and LCR.

Cryo-EM structure of antibacterial efflux transporter QacA from Staphylococcus aureus reveals a novel extracellular loop with allosteric role

Efflux of antibacterial compounds is a major mechanism for developing antimicrobial resistance. In the Gram-positive pathogen Staphylococcus aureus, QacA, a 14 transmembrane helix containing major facilitator superfamily antiporter, mediates proton-coupled efflux of mono and divalent cationic antibacterial compounds. In this study, we report the cryo-EM structure of QacA, with a single mutation D411N that improves homogeneity and retains efflux activity against divalent cationic compounds like dequalinium and chlorhexidine. The structure of substrate-free QacA, complexed to two single-domain camelid antibodies, was elucidated to a resolution of 3.6 Å. The structure displays an outward-open conformation with an extracellular helical hairpin loop (EL7) between transmembrane helices 13 and 14, which is conserved in a subset of DHA2 transporters. Removal of the EL7 hairpin loop or disrupting the interface formed between EL7 and EL1 compromises efflux activity. Chimeric constructs of QacA with a helical hairpin and EL1 grafted from other DHA2 members, LfrA and SmvA, restore activity in the EL7 deleted QacA revealing the allosteric and vital role of EL7 hairpin in antibacterial efflux in QacA and related members.

Template-free prediction of a new monotopic membrane protein fold and assembly by AlphaFold2

AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel β-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and the interfaces between them. It also assembles between seven and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel β-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher-order complexes, thus positing a new direction for the continued development of deep-learning protein structure prediction approaches.

Novel machine learning approaches revolutionize protein knowledge

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.

Distinguishing features of fold-switching proteins

Though many folded proteins assume one stable structure that performs one function, a small-but-increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold-switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus-2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold-switching proteins from their single-fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late-breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue-residue coevolution. The review closes by discussing high-throughput and medium-throughput experimental approaches that might be used to identify new fold-switching proteins.

Substrate Recognition Properties from an Intermediate Structural State of the UreA Transporter

Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide in the Aspergillus nidulans urea transporter, UreA. To deepen the structural characterization of UreA, we employed the artificial intelligence (AI) based AlphaFold2 (AF2) program. In this analysis, the resulting AF2 models lacked inward- and outward-facing cavities, suggesting a structural intermediate state of UreA. Moreover, the orientation of the W82, W84, N279, and T282 side chains showed a large variability, which in the case of W82 and W84, may operate as a gating mechanism in the ligand pathway. To test this hypothesis non-conservative and conservative substitutions of these amino acids were introduced, and binding and transport assessed for urea and its toxic analogue 2-thiourea, as well as binding of the structural analogue acetamide. As a result, residues W82, W84, N279, and T282 were implicated in substrate identification, selection, and translocation. Using molecular docking with Autodock Vina with flexible side chains, we corroborated the AF2 theoretical intermediate model, showing a remarkable correlation between docking scores and experimental affinities determined in wild-type and UreA mutants. The combination of AI-based modeling with classical docking, validated by comprehensive mutational analysis at the binding region, would suggest an unforeseen option to determine structural level details on a challenging family of proteins.

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

The Amino Acid-Polyamine-Organocation (APC) transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular γ-aminobutyric acid (GABA). Its structure, determined in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold with a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin transporter SERT and the glucose-sodium symporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follow the blueprint of these or other LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced modification of AlphaFold2 . Our experimental results reveal acid-induced conformational changes that dislodge the Cterminus from the permeation pathway coupled with rearrangement of helices that enables isomerization between inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminates the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different families in the LeuT-fold.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

Isolation, Purification, and Characterization of a Laccase-Degrading Aflatoxin B1 from Bacillus amyloliquefaciens B10

Aflatoxins, widely found in feed and foodstuffs, are potentially harmful to human and animal health because of their high toxicity. In this study, a strain of Bacillus amyloliquefaciens B10 with a strong ability to degrade aflatoxin B1 (AFB1) was screened; it could degrade 2.5 μg/mL of AFB1 within 96 h. The active substances of Bacillus amyloliquefaciens B10 for the degradation of AFB1 mainly existed in the culture supernatant. A new laccase with AFB1-degrading activity was separated by ammonium sulfate precipitation, diethylaminoethyl (DEAE) and gel filtration chromatography. The results of molecular docking showed that B10 laccase and aflatoxin had a high docking score. The coding sequence of the laccase was successfully amplified from cDNA by PCR and cloned into E. coli. The purified laccase could degrade 79.3% of AFB1 within 36 h. The optimum temperature for AFB1 degradation was 40 °C, and the optimum pH was 6.0–8.0. Notably, Mg²⁺ and dimethyl sulfoxide (DMSO) could enhance the AFB1-degrading activity of B10 laccase. Mutation of the three key metal combined sites of B10 laccase resulted in the loss of AFB1-degrading activity, indicating that these three metal combined sites of B10 laccase play an essential role in the catalytic degradation of AFB1.

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

OUP accepted manuscript

Summary

Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state.

Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding.

Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/

Supplementary information

Supplementary data are available at Bioinformatics online.

Co-evolutionary distance predictions contain flexibility information

MOTIVATION

Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure.

RESULTS

We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Critical assessment of methods of protein structure prediction (CASP)-Round XIV

Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein-folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.

Target highlights in CASP14: Analysis of models by structure providers

The biological and functional significance of selected Critical Assessment of Techniques for Protein Structure Prediction 14 (CASP14) targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modeled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins.

The AlphaFold Database of Protein Structures: A Biologist's Guide

AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the AlphaFold models from the perspective of a biologist, who may not be an expert in structural biology.

Applying and improving AlphaFold at CASP14

We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end‐to‐end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large‐scale structure prediction.

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.