Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2

Secondary Structure in Free and Assisted Modeling of Proteins with the Coarse-Grained UNRES Force Field

Secondary structure is a solid scaffold on which the three-dimensional structure of a protein is built. Therefore, care must be taken to reproduce the secondary structure as accurately as possible in the simulations of protein systems. In this chapter, we summarize the physics-based energy terms that govern secondary-structure formation, the auxiliary restraints on secondary structure derived from bioinformatics and from the experimental data, and the role of those in the modeling of protein structures, dynamics, and thermodynamics with the physics-based coarse-grained UNRES force field. Examples illustrating the methodology discussed and further directions of development are presented.

Protein-Variant-Phenotype Study of NBAS Using AlphaFold in the Aspect of SOPH Syndrome

NBAS gene variants cause phenotypically distinct and nonoverlapping conditions, SOPH syndrome and ILFS2. NBAS is a so-called "moonlighting" protein responsible for retrograde membrane trafficking and nonsense-mediated decay. However, its three-dimensional model and the nature of its possible interactions with other proteins have remained elusive. Here, we used AlphaFold to predict protein-protein interaction (PPI) sites and mapped them to NBAS pathogenic variants. We repeated in silico milestone studies of the NBAS protein to explain the multisystem phenotype of its variants, with particular emphasis on the SOPH variant (p.R1914H). We revealed the putative binding sites for the main interaction partners of NBAS and assessed the implications of these binding sites for the subdomain architecture of the NBAS protein. Using AlphaFold, we disclosed the far-reaching impact of NBAS variants on the development of each phenotypic trait in patients with NBAS-related pathologies.

An AI-informed NMR structure reveals an extraordinary LETM1 F-EF-hand domain that functions as a two-way regulator of mitochondrial calcium

AlphaFold can accurately predict static protein structures but does not account for solvent conditions. Human leucine zipper EF-hand transmembrane protein-1 (LETM1) has one sequence-identifiable EF-hand but how calcium (Ca2+) affects structure and function remains enigmatic. Here, we used highly confident AlphaFold Cα predictions to guide nuclear Overhauser effect (NOE) assignments and structure calculation of the LETM1 EF-hand in the presence of Ca2+. The resultant NMR structure exposes pairing between a partial loop-helix and full helix-loop-helix, forming an unprecedented F-EF-hand with non-canonical Ca2+ coordination but enhanced hydrophobicity for protein interactions compared to calmodulin. The structure also reveals the basis for pH sensing at the link between canonical and partial EF-hands. Functionally, mutations that augmented or weakened Ca2+ binding increased or decreased matrix Ca2+, respectively, establishing F-EF as a two-way mitochondrial Ca2+ regulator. Thus, we show how to synergize AI prediction with NMR data, elucidating a solution-specific and extraordinary LETM1 F-EF-hand.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Predicting multiple conformations via sequence clustering and AlphaFold2

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function.

Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

Representing structures of the multiple conformational states of proteins

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Alanine Scanning of the Unstructured Region of Ara h 2 and of a Related Mimotope Reveals Critical Amino Acids for IgE Binding

SCOPE

The unstructured region of Ara h 2, referred to as epitope 3, contains a repeated motif, DYPSh (h = hydroxyproline) that is important for IgE binding.

METHODS AND RESULTS

IgE binding assays to 20mer and shorter peptides of epitope 3, defines a 16mer core sequence containing one copy of the DPYSh motif, DEDSYERDPYShSQDP. This study performs alanine scanning of this and a related 12mer mimotope, LLDPYAhRAWTK. IgE binding, using a pool of 10 sera and with individual sera, is greatly reduced when alanine is substituted for aspartate at position 8 (D8; p < 0.01), tyrosine at position 10 (Y10; p < 0.01), and hydroxyproline at position 12 (h12; p < 0.001). IgE binding to alanine-substituted peptides of a mimotope containing the DPY_h motif confirm the critical importance of Y (p < 0.01) and h (p < 0.01), but not D. Molecular modeling of the core and mimotope suggests an h-dependent conformational basis for the recognition of these sequences by polyclonal IgE.

CONCLUSIONS

IgE from pooled sera and individual sera differentially bound amino acids throughout the sequences of Epitope 3 and its mimotope, with Y10 and h12 being most important for all sera. These results are highly significant for designing hypoallergenic forms of Ara h 2.

Artificial intelligence and the analysis of cryo-EM data provide structural insight into the molecular mechanisms underlying LN-lamininopathies

Laminins (Lm) are major components of basement membranes (BM), which polymerize to form a planar lattice on cell surface. Genetic alternations of Lm affect their oligomerization patterns and lead to failures in BM assembly manifesting in a group of human disorders collectively defined as Lm N-terminal domain lamininopathies (LN-lamininopathies). We have employed a recently determined cryo-EM structure of the Lm polymer node, the basic repeating unit of the Lm lattice, along with structure prediction and modeling to systematically analyze structures of twenty-three pathogenic Lm polymer nodes implicated in human disease. Our analysis provides the detailed mechanistic explanation how Lm mutations lead to failures in Lm polymerization underlining LN-lamininopathies. We propose the new categorization scheme of LN-lamininopathies based on the insight gained from the structural analysis. Our results can help to facilitate rational drug design aiming in the treatment of Lm deficiencies.

Experimental NOE, Chemical Shift, and Proline Isomerization Data Provide Detailed Insights into Amelotin Oligomerization

Amelotin is an intrinsically disordered protein (IDP) rich in Pro residues and is involved in hydroxyapatite mineralization. It rapidly oligomerizes under physiological conditions of pH and pressure but reverts to its monomeric IDP state at elevated pressure. We identified a 105-residue segment of the protein that becomes ordered upon oligomerization, and we used pressure-jump NMR spectroscopy to measure long-range NOE contacts that exist exclusively in the oligomeric NMR-invisible state. The kinetics of oligomerization and dissociation were probed at the residue-specific level, revealing that the oligomerization process is initiated in the C-terminal half of the segment. Using pressure-jump NMR, the degree of order in the oligomer at the sites of Pro residues was probed by monitoring changes in cis/trans equilibria relative to the IDP state after long-term equilibration under oligomerizing conditions. Whereas most Pro residues revert to trans in the oligomeric state, Pro-49 favors a cis configuration and three Pro residues retain an unchanged cis fraction, pointing to their local lack of order in the oligomeric state. NOE contacts and secondary 13C chemical shifts in the oligomeric state indicate the presence of an 11-residue α-helix, preceded by a small intramolecular antiparallel β-sheet, with slower formation of long-range intermolecular interactions to N-terminal residues. Although none of the models generated by AlphaFold2 for the amelotin monomer was consistent with experimental data, subunits of a hexamer generated by AlphaFold-Multimer satisfied intramolecular NOE and chemical shift data and may provide a starting point for developing atomic models for the oligomeric state.

Improved methodology for protein NMR structure calculation using hydrogen bond restraints and ANSURR validation: The SH2 domain of SH2B1

Protein structures calculated using NMR data are less accurate and less well-defined than they could be. Here we use the program ANSURR to show that this deficiency is at least in part due to a lack of hydrogen bond restraints. We describe a protocol to introduce hydrogen bond restraints into the structure calculation of the SH2 domain from SH2B1 in a systematic and transparent way and show that the structures generated are more accurate and better defined as a result. We also show that ANSURR can be used as a guide to know when the structure calculation is good enough to stop.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.

Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein

Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.

Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Highlights

AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

Biophysical analysis of Gaussia Luciferase bioluminescence mechanisms using a non-oxidizable coelenterazine

Gaussia luciferase (GLuc 18.2kDa; 168 residues) is a marine copepod luciferase that emits a bright blue light when oxidizing coelenterazine (CTZ). It is a helical protein where two homologous sequential repeats form two anti-parallel bundles, each made of four helices. We previously identified a hydrophobic cavity as a prime candidate for the catalytic site, but GLuc's fast bioluminescence reaction hampered a detailed analysis. Here, we used azacoelenterazine (Aza-CTZ), a non-oxidizable coelenterazine (CTZ) analog, as a probe to investigate its binding mode to GLuc. While analysing GLuc's activity, we unexpectedly found that salt and monovalent anions are absolutely required for Gluc's bioluminescence, which retrospectively appears reasonable for a sea-dwelling organism. The NMR-based investigation, using chemical shift perturbations monitored by ¹⁵N-¹H HSQC, suggested that Aza-CTZ (and thus unoxidized CTZ) binds to residues in or near the hydrophobic cavity. These NMR data are in line with a recent structural prediction of GLuc, hypothesizing that large structural changes occur in regions remote from the hydrophobic cavity upon the addition of CTZ. Interestingly, these results point toward a unique mode of catalysis to achieve CTZ oxidative decarboxylation.

Reflecting on mutational and biophysical analysis of Gaussia princeps Luciferase from a structural perspective: a unique bioluminescent enzyme

Gaussia princeps luciferase (GLuc 18.2 kDa; 168 residues) is a marine copepod luciferase that emits a bright blue light when oxidizing coelenterazine (CTZ). GLuc is a small luciferase, attracting much attention as a potential reporter protein. However, compared to firefly and Renilla luciferases, which have been thoroughly characterized and are used in a wide range of applications, structural and biophysical studies of GLuc have been slow to appear. Here, we review the biophysical and mutational studies of GLuc's bioluminescence from a structural viewpoint, particularly in view of its recent NMR solution structure, where two homologous sequential repeats form two anti-parallel bundles, each made of four helices, grabbing a short N-terminal helix. Additionally, a long loop classified as an intrinsically disordered region separates the two bundles forming one side of a hydrophobic pocket that is most likely the binding/catalytic site. We compare the NMR-determined structure with a recent AlphaFold2 prediction. Overall, the AlphaFold2 structure was in line with the solution structure; however, it surprisingly revealed a possible, alternative conformation, where the N-terminal helix is replaced by a newly formed α helix in the C-terminal tail that is unfolded in the NMR structure. In addition, we discuss the results of previous mutational analysis focusing on a putative catalytic core identified by chemical shift perturbation analysis and molecular dynamics simulations performed using both the NMR and the AlphaFold2 structures. In particular, we discuss the role of the possible conformational change and the hydrophobic pocket in GLuc's activity. Overall, the discussion points toward GLuc's unexpected and unusual characteristics that appear to be much more flexible than traditional enzymes, resulting in a unique mode of catalysis to achieve CTZ oxidative decarboxylation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01025-6.

Exploring Structural Diversity among Adhesion Devices Encoded by Lactococcal P335 Phages with AlphaFold2

Bacteriophages, or phages, are the most abundant biological entities on Earth. They possess molecular nanodevices to package and store their genome, as well as to introduce it into the cytoplasm of their bacterial prey. Successful phage infection commences with specific recognition of, and adhesion to, a suitable host cell surface. Adhesion devices of siphophages infecting Gram-positive bacteria are very diverse and remain, for the majority, poorly understood. These assemblies often comprise long, flexible, and multi-domain proteins, which limit their structural analyses by experimental approaches. The protein structure prediction program AlphaFold2 is exquisitely adapted to unveil structural and functional details of such molecular machineries. Here, we present structure predictions of adhesion devices from siphophages belonging to the P335 group infecting Lactococcus spp., one of the most extensively applied lactic acid bacteria in dairy fermentations. The predictions of representative adhesion devices from types I-IV P335 phages illustrate their very diverse topology. Adhesion devices from types III and IV phages share a common topology with that of Skunavirus p2, with a receptor binding protein anchored to the virion by a distal tail protein loop. This suggests that they exhibit an activation mechanism similar to that of phage p2 prior to host binding.

A coarse-grained approach to NMR-data-assisted modeling of protein structures

The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function

The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts the 3D structure of two thirds of the human proteome. Its predictions can be used directly as structural models or indirectly as aids for experimental structure determination using X-ray crystallography, CryoEM or NMR spectroscopy. Nevertheless, AlphaFold 2 can neither afford insight into how proteins fold, nor can it determine protein stability or dynamics. Rare folds or minor alternative conformations are also not predicted by AlphaFold 2 and the program does not forecast the impact of post translational modifications, mutations or ligand binding. The remaining third of human proteome which is poorly predicted largely corresponds to intrinsically disordered regions of proteins. Key to regulation and signaling networks, these disordered regions often form biomolecular condensates or amyloids. Fortunately, the limitations of AlphaFold 2 are largely complemented by NMR spectroscopy. This experimental approach provides information on protein folding and dynamics as well as biomolecular condensates and amyloids and their modulation by experimental conditions, small molecules, post translational modifications, mutations, flanking sequence, interactions with other proteins, RNA and virus. Together, NMR spectroscopy and AlphaFold 2 can collaborate to advance our comprehension of proteins.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, "hot spots", as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ-TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

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.

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.