PDBStat: a universal restraint converter and restraint analysis software package for protein NMR

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

We introduce AlphaFold-NMR, a novel approach to NMR structure determination that reveals previously undetected protein conformational states. Unlike conventional NMR methods that rely on NOE-derived spatial restraints, AlphaFold-NMR combines AI-driven conformational sampling with Bayesian scoring of realistic protein models against NOESY and chemical shift data. This method uncovers alternative conformational states of the enzyme Gaussia luciferase, involving large-scale changes in the lid, binding pockets, and other surface cavities. It also identifies similar yet distinct conformational states of the human tumor suppressor Cyclin-Dependent Kinase 2-Associated Protein 1. These studies demonstrate the potential of AI-based modeling with enhanced sampling to generate diverse structural models followed by conformer selection and validation with experimental data as an alternative to traditional restraint-satisfaction protocols for protein NMR structure determination. The AlphaFold-NMR framework enables discovery of conformational heterogeneity and cryptic pockets that conventional NMR analysis methods do not distinguish, providing new insights into protein structure-function relationships.

NMR Spectroscopy for the Validation of AlphaFold2 Structures

The introduction of AlphaFold has fundamentally changed our ability to predict the structure of proteins from their primary sequence of amino acids. As machine learning (ML) and artificial intelligence (AI) based protein prediction continues to advance, we examine the potential of hybrid techniques that combine experiment and computation that may yield more accurate structures than AI alone with significantly reduced experimental burden. We have developed heuristics comparing N-edited NOESY spectra and AlphaFold predicted structures that seek to determine whether the predicted structure reasonably describes the structure of the protein which generated the NOESY. We present a large collection of data connecting entries across the BMRB, PDB and AlphaFold Database that includes experimentally derived structures and corresponding spectra, establishing it as a means to develop and test hybrid methods utilizing AlphaFold and NMR spectra to perform structure determination. These data test the new heuristics' ability to identify inaccurate AlphaFold structures. A support vector machine was developed to test the consistency of NMR data with predicted structure and we show its application to the structure of an unsolved engineered protein, LoTOP.

A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy

Diabetes mellitus is a metabolic disorder that persists as a global threat to the world. A G-protein coupled receptor (GPCR), free fatty acid receptor 4 (FFAR4), has emerged as a potential target for type 2 diabetes mellitus (T2DM) and obesity-related disorders. The current study has investigated the FFAR4, deploying 3-dimensional structure modeling, molecular docking, machine learning, and high-throughput virtual screening methods to unravel the receptor's crucial and non-crucial binding site residues. We screened four lakh compounds and shortlisted them based on binding energy, stereochemical considerations, non-bonded interactions, and pharmacokinetic profiling. Out of the screened compounds, four compounds were selected for ligand-bound simulations. The molecular dynamic simulations were carried out for 1µs for native FFAR4 and 500 ns each for complexes of FFAR4 with compound 1, compound 2, compound 3, and compound 4. Our findings showed that in addition to reported binding site residues ARG99, ARG183, and VAL98 in known agonists like TUG-891, the amino acids ARG22, ARG24, THR23, TRP305, and GLU43 were also critical binding site residues. These amino acids impart stability to the FFAR4 complexes and contribute to the stronger binding affinity of the compounds. The study also indicated that aromatic residues like PHE211 are crucial for recognizing the active site's pi-pi and C-C double bonds. Since FFAR4 is a membrane protein, the simulation studies give an insight into the mechanisms of the crucial protein-lipid and lipid-water interactions. The analysis of the molecular dynamics trajectories showed all four compounds as potential hit molecules that can be developed further into potential agonists for T2DM therapy. Amongst the four compounds, compound 4 showed relatively better binding affinity, stronger non-bonded interactions, and a stable complex.Communicated by Ramaswamy H. Sarma.

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

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.

Structural transitions enable interleukin-18 maturation and signaling

Several interleukin-1 (IL-1) family members, including IL-1β and IL-18, require processing by inflammasome-associated caspases to unleash their activities. Here, we unveil, by cryoelectron microscopy (cryo-EM), two major conformations of the complex between caspase-1 and pro-IL-18. One conformation is similar to the complex of caspase-4 and pro-IL-18, with interactions at both the active site and an exosite (closed conformation), and the other only contains interactions at the active site (open conformation). Thus, pro-IL-18 recruitment and processing by caspase-1 is less dependent on the exosite than the active site, unlike caspase-4. Structure determination by nuclear magnetic resonance uncovers a compact fold of apo pro-IL-18, which is similar to caspase-1-bound pro-IL-18 but distinct from cleaved IL-18. Binding sites for IL-18 receptor and IL-18 binding protein are only formed upon conformational changes after pro-IL-18 cleavage. These studies show how pro-IL-18 is selected as a caspase-1 substrate, and why cleavage is necessary for its inflammatory activity.

Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

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.

Structural, Functional, and Mutational Studies of a Potent Subtilisin Inhibitor from Budgett's Frog, Lepidobatrachus laevis

Subtilases play a significant role in microbial pathogen infections by degrading the host proteins. Subtilisin inhibitors are crucial in fighting against these harmful microorganisms. LL-TIL, from skin secretions of Lepidobatrachus laevis, is a cysteine-rich peptide belonging to the I8 family of inhibitors. Protease inhibitory assays demonstrated that LL-TIL acts as a slow-tight binding inhibitor of subtilisin Carlsberg and proteinase K with inhibition constants of 91 pM and 2.4 nM, respectively. The solution structures of LL-TIL and a mutant peptide reveal that they adopt a typical TIL-type fold with a canonical conformation of a reactive site loop (RSL). The structure of the LL-TIL-subtilisin complex and molecular dynamics (MD) simulations provided an in-depth view of the structural basis of inhibition. NMR relaxation data and molecular dynamics simulations indicated a rigid conformation of RSL, which does not alter significantly upon subtilisin binding. The energy calculation for subtilisin inhibition predicted Ile31 as the highest contributor to the binding energy, which was confirmed experimentally by site-directed mutagenesis. A chimeric mutant of LL-TIL broadened the inhibitory profile and attenuated subtilisin inhibition by 2 orders of magnitude. These results provide a template to engineer more specific and potent TIL-type subtilisin inhibitors.

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.

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.

Structural and mutational analysis of MazE6-operator DNA complex provide insights into autoregulation of toxin-antitoxin systems

Of the 10 paralogs of MazEF Toxin-Antitoxin system in Mycobacterium tuberculosis, MazEF6 plays an important role in multidrug tolerance, virulence, stress adaptation and Non Replicative Persistant (NRP) state establishment. The solution structures of the DNA binding domain of MazE6 and of its complex with the cognate operator DNA show that transcriptional regulation occurs by binding of MazE6 to an 18 bp operator sequence bearing the TANNNT motif (-10 region). Kinetics and thermodynamics of association, as determined by NMR and ITC, indicate that the nMazE6-DNA complex is of high affinity. Residues in N-terminal region of MazE6 that are key for its homodimerization, DNA binding specificity, and the base pairs in the operator DNA essential for the protein-DNA interaction, have been identified. It provides a basis for design of chemotherapeutic agents that will act via disruption of TA autoregulation, leading to cell death.

The dimeric MazE6 antitoxin binds to a specific sequence in its cognate operator DNA for autoregulation, and the key residues for dimerization and DNA binding are identified.

Accurate de novo design of membrane-traversing macrocycles

We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1 × 10-6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.

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.

Theta-Defensins to Counter COVID-19 as Furin Inhibitors: In Silico Efficiency Prediction and Novel Compound Design

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was characterized as a pandemic by the World Health Organization (WHO) in Dec. 2019. SARS-CoV-2 binds to the cell membrane through spike proteins on its surface and infects the cell. Furin, a host-cell enzyme, possesses a binding site for the spike protein. Thus, molecules that block furin could potentially be a therapeutic solution. Defensins are antimicrobial peptides that can hypothetically inhibit furin because of their arginine-rich structure. Theta-defensins, a subclass of defensins, have attracted attention as drug candidates due to their small size, unique structure, and involvement in several defense mechanisms. Theta-defensins could be a potential treatment for COVID-19 through furin inhibition and an anti-inflammatory mechanism. Note that inflammatory events are a significant and deadly condition that could happen at the later stages of COVID-19 infection. Here, the potential of theta-defensins against SARS-CoV-2 infection was investigated through in silico approaches. Based on docking analysis results, theta-defensins can function as furin inhibitors. Additionally, a novel candidate peptide against COVID-19 with optimal properties regarding antigenicity, stability, electrostatic potential, and binding strength was proposed. Further in vitro/in vivo investigations could verify the efficiency of the designed novel peptide.

Mechanism for the activation of the anaplastic lymphoma kinase receptor

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase (RTK) that regulates important functions in the central nervous system^1,2. The ALK gene is a hotspot for chromosomal translocation events that result in several fusion proteins that cause a variety of human malignancies³. Somatic and germline gain-of-function mutations in ALK were identified in paediatric neuroblastoma^4-7. ALK is composed of an extracellular region (ECR), a single transmembrane helix and an intracellular tyrosine kinase domain^8,9. ALK is activated by the binding of ALKAL1 and ALKAL2 ligands^10-14 to its ECR, but the lack of structural information for the ALK-ECR or for ALKAL ligands has limited our understanding of ALK activation. Here we used cryo-electron microscopy, nuclear magnetic resonance and X-ray crystallography to determine the atomic details of human ALK dimerization and activation by ALKAL1 and ALKAL2. Our data reveal a mechanism of RTK activation that allows dimerization by either dimeric (ALKAL2) or monomeric (ALKAL1) ligands. This mechanism is underpinned by an unusual architecture of the receptor-ligand complex. The ALK-ECR undergoes a pronounced ligand-induced rearrangement and adopts an orientation parallel to the membrane surface. This orientation is further stabilized by an interaction between the ligand and the membrane. Our findings highlight the diversity in RTK oligomerization and activation mechanisms.

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

NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR‐derived contacts for an integral membrane protein (T1088). For the three targets with NMR‐based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of “inverse structure determination,” in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238‐residue integral membrane porin, was also used to assess several NMR‐assisted prediction methods. Most groups involved in this exercise generated similar beta‐barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.

Role of backbone strain in de novo design of complex α/β protein structures

We previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.

NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview

BACKGROUND

NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems.

OBJECTIVE

This review provides a toolkit to investigate peptide conformational properties by NMR.

METHODS

Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well.

RESULTS

The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well.

CONCLUSION

NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins

The extraterminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR structures of complexes formed between the BRD3 ET domain and either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN_329-408) or its 22-residue IN tail peptide (IN_386-407) alone reveal similar intermolecular three-stranded β-sheet formations. ¹⁵N relaxation studies reveal a 10-residue linker region (IN_379-388) tethering the SH3 domain (IN_329-378) to the ET-binding motif (IN_389-405):ET complex. This linker has restricted flexibility, affecting its potential range of orientations in the IN:nucleosome complex. The complex of the ET-binding peptide of the host NSD3 protein (NSD3_148-184) and the BRD3 ET domain includes a similar three-stranded β-sheet interaction, but the orientation of the β hairpin is flipped compared with the two IN:ET complexes. These studies expand our understanding of molecular recognition polymorphism in complexes of ET-binding motifs with viral and host proteins.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

Residual Dipolar Couplings have the potential to improve the accuracy and reduce the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, provide assignment of resonances, and characterize the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. One key factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can complement existing methods to provide faster and more accurate studies of larger and more complex protein structures by NMR spectroscopy in solution state.

From integrative structural biology to cell biology

Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.

Aromatic Interactions Drive the Coupled Folding and Binding of the Intrinsically Disordered Sesbania mosaic Virus VPg Protein

The plant Sesbania mosaic virus [a (+)-ssRNA sobemovirus] VPg protein is intrinsically disordered in solution. For the virus life cycle, the VPg protein is essential for replication and for polyprotein processing that is carried out by a virus-encoded protease. The nuclear magnetic resonance (NMR)-derived tertiary structure of the protease-bound VPg shows it to have a novel tertiary structure with an α-β-β-β topology. The quaternary structure of the high-affinity protease-VPg complex (≈27 kDa) has been determined using HADDOCK protocols with NMR (residual dipolar coupling, dihedral angle, and nuclear Overhauser enhancement) restraints and mutagenesis data as inputs. The geometry of the complex is in excellent agreement with long-range orientational restraints such as residual dipolar couplings and ring-current shifts. A "vein" of aromatic residues on the protease surface is pivotal for the folding of VPg via intermolecular edge-to-face π···π stacking between Trp²⁷¹ and Trp³⁶⁸ of the protease and VPg, respectively, and for the CH···π interactions between Leu³⁶¹ of VPg and Trp²⁷¹ of the protease. The structure of the protease-VPg complex provides a molecular framework for predicting sites of important posttranslational modifications such as RNA linkage and phosphorylation and a better understanding of the coupled folding upon binding of intrinsically disordered proteins. The structural data presented here augment the limited structural data available on viral proteins, given their propensity for structural disorder.

PRE-driven protein NMR structures: an alternative approach in highly paramagnetic systems

Metalloproteins play key roles across biology, and knowledge of their structure is essential to understand their physiological role. For those metalloproteins containing paramagnetic states, the enhanced relaxation caused by the unpaired electrons often makes signal detection unfeasible near the metal center, precluding adequate structural characterization right where it is more biochemically relevant. Here, we report a protein structure determination by NMR where two different sets of restraints, one containing Nuclear Overhauser Enhancements (NOEs) and another containing Paramagnetic Relaxation Enhancements (PREs), are used separately and eventually together. The protein PioC from Rhodopseudomonas palustris TIE-1 is a High Potential Iron-Sulfur Protein (HiPIP) where the [4Fe-4S] cluster is paramagnetic in both oxidation states at room temperature providing the source of PREs used as alternative distance restraints. Comparison of the family of structures obtained using NOEs only, PREs only, and the combination of both reveals that the pairwise root-mean-square deviation (RMSD) between them is similar and comparable with the precision within each family. This demonstrates that, under favorable conditions in terms of protein size and paramagnetic effects, PREs can efficiently complement and eventually replace NOEs for the structural characterization of small paramagnetic metalloproteins and de novo-designed metalloproteins by NMR. DATABASES: The 20 conformers with the lowest target function constituting the final family obtained using the full set of NMR restraints were deposited to the Protein Data Bank (PDB ID: 6XYV). The 20 conformers with the lowest target function obtained using NOEs only (PDB ID: 7A58) and PREs only (PDB ID: 7A4L) were also deposited to the Protein Data Bank. The chemical shift assignments were deposited to the BMRB (code 34487).

A double-stranded RNA platform is required for the interaction between a host restriction factor and the NS1 protein of influenza A virus

Influenza A viruses cause widespread human respiratory disease. The viral multifunctional NS1 protein inhibits host antiviral responses. This inhibition results from the binding of specific cellular antiviral proteins at various positions on the NS1 protein. Remarkably, binding of several proteins also requires the two amino-acid residues in the NS1 N-terminal RNA-binding domain (RBD) that are required for binding double-stranded RNA (dsRNA). Here we focus on the host restriction factor DHX30 helicase that is countered by the NS1 protein, and establish why the dsRNA-binding activity of NS1 is required for its binding to DHX30. We show that the N-terminal 152 amino-acid residue segment of DHX30, denoted DHX30N, possesses all the antiviral activity of DHX30 and contains a dsRNA-binding domain, and that the NS1-DHX30 interaction in vivo requires the dsRNA-binding activity of both DHX30N and the NS1 RBD. We demonstrate why this is the case using bacteria-expressed proteins: the DHX30N-NS1 RBD interaction in vitro requires the presence of a dsRNA platform that binds both NS1 RBD and DHX30N. We propose that a similar dsRNA platform functions in interactions of the NS1 protein with other proteins that requires these same two amino-acid residues required for NS1 RBD dsRNA-binding activity.

BioMagResBank (BMRB) as a Resource for Structural Biology

The Biological Magnetic Resonance Data Bank (BioMagResBank or BMRB), founded in 1988, serves as the archive for data generated by nuclear magnetic resonance (NMR) spectroscopy of biological systems. NMR spectroscopy is unique among biophysical approaches in its ability to provide a broad range of atomic and higher-level information relevant to the structural, dynamic, and chemical properties of biological macromolecules, as well as report on metabolite and natural product concentrations in complex mixtures and their chemical structures. BMRB became a core member of the Worldwide Protein Data Bank (wwPDB) in 2007, and the BMRB archive is now a core archive of the wwPDB. Currently, about 10% of the structures deposited into the PDB archive are based on NMR spectroscopy. BMRB stores experimental and derived data from biomolecular NMR studies. Newer BMRB biopolymer depositions are divided about evenly between those associated with structure determinations (atomic coordinates and supporting information archived in the PDB) and those reporting experimental information on molecular dynamics, conformational transitions, ligand binding, assigned chemical shifts, or other results from NMR spectroscopy. BMRB also provides resources for NMR studies of metabolites and other small molecules that are often macromolecular ligands and/or nonstandard residues. This chapter is directed to the structural biology community rather than the metabolomics and natural products community. Our goal is to describe various BMRB services offered to structural biology researchers and how they can be accessed and utilized. These services can be classified into four main groups: (1) data deposition, (2) data retrieval, (3) data analysis, and (4) services for NMR spectroscopists and software developers. The chapter also describes the NMR-STAR data format used by BMRB and the tools provided to facilitate its use. For programmers, BMRB offers an application programming interface (API) and libraries in the Python and R languages that enable users to develop their own BMRB-based tools for data analysis, visualization, and manipulation of NMR-STAR formatted files. BMRB also provides users with direct access tools through the NMRbox platform.

Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵ N-¹ H residual dipolar coupling data, typical of that obtained for ¹⁵ N,¹³ C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

De novo protein design by citizen scientists

Online citizen science projects such as GalaxyZoo¹, Eyewire² and Phylo³ have been very successful for data collection, annotation, and processing, but for the most part have harnessed human pattern recognition skills rather than human creativity. An exception is the game EteRNA⁴, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein folding game Foldit⁵. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure with an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top scoring solutions, and subsequent game improvement, Foldit players can now, starting from an extended polypeptide chain, generate a diversity of protein structures and sequences which encode them in silico. 146 Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed in E. coli with good solubility and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge contributing to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges, such as the protein design problem.

The C-terminal domain of HpDprA is a DNA-binding winged helix domain that does not bind double-stranded DNA

DNA-processing protein A, a ubiquitous multidomain DNA-binding protein, plays a crucial role during natural transformation in bacteria. Here, we carried out the structural analysis of DprA from the human pathogen Helicobacter pylori by combining data issued from the 1.8-Å resolution X-ray structure of the Pfam02481 domain dimer (RF), the NMR structure of the carboxy terminal domain (CTD), and the low-resolution structure of the full-length DprA dimer obtained in solution by SAXS. In particular, we sought a molecular function for the CTD, a domain that we show here is essential for transformation in H. pylori. Albeit its structural homology to winged helix DNA-binding motifs, we confirmed that the isolated CTD does not interact with ssDNA nor with dsDNA. The key R52 and K137 residues of RF are crucial for these two interactions. Search for sequences harboring homology to either HpDprA or Rhodopseudomonas palustris DprA CTDs led to the identification of conserved patches in the two CTD. Our structural study revealed the similarity of the structures adopted by these residues in RpDprA CTD and HpDprA CTD. This argues for a conserved, but yet to be defined, CTD function, distinct from DNA binding.

NMR-STAR: comprehensive ontology for representing, archiving and exchanging data from nuclear magnetic resonance spectroscopic experiments

The growth of the biological nuclear magnetic resonance (NMR) field and the development of new experimental technology have mandated the revision and enlargement of the NMR-STAR ontology used to represent experiments, spectral and derived data, and supporting metadata. We present here a brief description of the NMR-STAR ontology and software tools for manipulating NMR-STAR data files, editing the files, extracting selected data, and creating data visualizations. Detailed information on these is accessible from the links provided.

Combining Evolutionary Covariance and NMR Data for Protein Structure Determination

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

The lineage-specific, intrinsically disordered N-terminal extension of monothiol glutaredoxin 1 from trypanosomes contains a regulatory region

Glutaredoxins (Grx) are small proteins conserved throughout all the kingdoms of life that are engaged in a wide variety of biological processes and share a common thioredoxin-fold. Among them, class II Grx are redox-inactive proteins involved in iron-sulfur (FeS) metabolism. They contain a single thiol group in their active site and use low molecular mass thiols such as glutathione as ligand for binding FeS-clusters. In this study, we investigated molecular aspects of 1CGrx1 from the pathogenic parasite Trypanosoma brucei brucei, a mitochondrial class II Grx that fulfills an indispensable role in vivo. Mitochondrial 1CGrx1 from trypanosomes differs from orthologues in several features including the presence of a parasite-specific N-terminal extension (NTE) whose role has yet to be elucidated. Previously we have solved the structure of a truncated form of 1CGrx1 containing only the conserved glutaredoxin domain but lacking the NTE. Our aim here is to investigate the effect of the NTE on the conformation of the protein. We therefore solved the NMR structure of the full-length protein, which reveals subtle but significant differences with the structure of the NTE-less form. By means of different experimental approaches, the NTE proved to be intrinsically disordered and not involved in the non-redox dependent protein dimerization, as previously suggested. Interestingly, the portion comprising residues 65–76 of the NTE modulates the conformational dynamics of the glutathione-binding pocket, which may play a role in iron-sulfur cluster assembly and delivery. Furthermore, we disclosed that the class II-strictly conserved loop that precedes the active site is critical for stabilizing the protein structure. So far, this represents the first communication of a Grx containing an intrinsically disordered region that defines a new protein subgroup within class II Grx.

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Principles for designing proteins with cavities formed by curved β sheets

Active sites and ligand-binding cavities in native proteins are often formed by curved β sheets, and the ability to control β-sheet curvature would allow design of binding proteins with cavities customized to specific ligands. Toward this end, we investigated the mechanisms controlling β-sheet curvature by studying the geometry of β sheets in naturally occurring protein structures and folding simulations. The principles emerging from this analysis were used to design, de novo, a series of proteins with curved β sheets topped with α helices. Nuclear magnetic resonance and crystal structures of the designs closely match the computational models, showing that β-sheet curvature can be controlled with atomic-level accuracy. Our approach enables the design of proteins with cavities and provides a route to custom design ligand-binding and catalytic sites.

15N and 13C- SOFAST-HMQC editing enhances 3D-NOESY sensitivity in highly deuterated, selectively [1H,13C]-labeled proteins

The ongoing NMR method development effort strives for high quality multidimensional data with reduced collection time. Here, we apply 'SOFAST-HMQC' to frequency editing in 3D NOESY experiments and demonstrate the sensitivity benefits using highly deuterated and ¹⁵N, methyl labeled samples in H₂O. The experiments benefit from a combination of selective T ₁ relaxation (or L-optimized effect), from Ernst angle optimization and, in certain types of experiments, from using the mixing time for both NOE buildup and magnetization recovery. This effect enhances sensitivity by up to 2.4× at fast pulsing versus reference HMQC sequences of same overall length and water suppression characteristics. Representative experiments designed to address interesting protein NMR challenges are detailed. Editing capabilities are exploited with heteronuclear ¹⁵N,¹³C-edited, or with diagonal-free ¹³C aromatic/methyl-resolved 3D-SOFAST-HMQC-NOESY-HMQC. The latter experiment is used here to elucidate the methyl-aromatic NOE network in the hydrophobic core of the 19 kDa FliT-FliJ flagellar protein complex. Incorporation of fast pulsing to reference experiments such as 3D-NOESY-HMQC boosts digital resolution, simplifies the process of NOE assignment and helps to automate protein structure determination.

Structural/Functional Properties of Human NFU1, an Intermediate [4Fe-4S] Carrier in Human Mitochondrial Iron-Sulfur Cluster Biogenesis

Human mitochondrial NFU1 functions in the maturation of iron-sulfur proteins, and NFU1 deficiency is associated with a fatal mitochondrial disease. We determined three-dimensional structures of the N- and C-terminal domains of human NFU1 by nuclear magnetic resonance spectroscopy and used these structures along with small-angle X-ray scattering (SAXS) data to derive structural models for full-length monomeric apo-NFU1, dimeric apo-NFU1 (an artifact of intermolecular disulfide bond formation), and holo-NFUI (the [4Fe-4S] cluster-containing form of the protein). Apo-NFU1 contains two cysteine residues in its C-terminal domain, and two apo-NFU1 subunits coordinate one [4Fe-4S] cluster to form a cluster-linked dimer. Holo-NFU1 consists of a complex of three of these dimers as shown by molecular weight estimates from SAXS and size-exclusion chromatography. The SAXS-derived structural model indicates that one N-terminal region from each of the three dimers forms a tripartite interface. The activity of the holo-NFU1 preparation was verified by demonstrating its ability to activate apo-aconitase.

Structural basis for the antifolding activity of a molecular chaperone

Molecular chaperones act on non-native proteins in the cell to prevent their aggregation, premature folding or misfolding. Different chaperones often exert distinct effects, such as acceleration or delay of folding, on client proteins via mechanisms that are poorly understood. Here we report the solution structure of SecB, a chaperone that exhibits strong antifolding activity, in complex with alkaline phosphatase and maltose-binding protein captured in their unfolded states. SecB uses long hydrophobic grooves that run around its disk-like shape to recognize and bind to multiple hydrophobic segments across the length of non-native proteins. The multivalent binding mode results in proteins wrapping around SecB. This unique complex architecture alters the kinetics of protein binding to SecB and confers strong antifolding activity on the chaperone. The data show how the different architectures of chaperones result in distinct binding modes with non-native proteins that ultimately define the activity of the chaperone.

Introduction of a polar core into the de novo designed protein Top7

Design of polar interactions is a current challenge for protein design. The de novo designed protein Top7, like almost all designed proteins, has an entirely nonpolar core. Here we describe the replacing of a sizable fraction (5 residues) of this core with a designed polar hydrogen bond network. The polar core design is expressed at high levels in E. coli, has a folding free energy of 10 kcal/mol, and retains the multiphasic folding kinetics of the original Top7. The NMR structure of the design shows that conformations of three of the five residues, and the designed hydrogen bonds between them, are very close to those in the design model. The remaining two residues, which are more solvent exposed, sample a wide range of conformations in the NMR ensemble. These results show that hydrogen bond networks can be designed in protein cores, but also highlight challenges that need to be overcome when there is competition with solvent.

Nuclear Magnetic Resonance Structure of a Major Lens Protein, Human γC-Crystallin: Role of the Dipole Moment in Protein Solubility

A hallmark of the crystallin proteins is their exceptionally high solubility, which is vital for maintaining the high refractive index of the eye lens. Human γC-crystallin is a major γ-crystallin whose mutant forms are associated with congenital cataracts but whose three-dimensional structure is not known. An earlier study of a homology model concluded that human γC-crystallin has low intrinsic solubility, mainly because of the atypical magnitude and fluctuations of its dipole moment. On the contrary, the high-resolution tertiary structure of human γC-crystallin determined here shows unequivocally that it is a highly soluble, monomeric molecule in solution. Notable differences between the orientations and interactions of several side chains are observed upon comparison to those in the model. No evidence of the pivotal role ascribed to the effect of dipole moment on protein solubility was found. The nuclear magnetic resonance structure should facilitate a comprehensive understanding of the deleterious effects of cataract-associated mutations in human γC-crystallin.

Structural characterization of NRAS isoform 5

It was recently discovered that the NRAS isoform 5 (20 amino acids) is expressed in melanoma and results in a more aggressive cell phenotype. This novel isoform is responsible for increased phosphorylation of downstream targets such as AKT, MEK, and ERK as well as increased cellular proliferation. This structure report describes the NMR solution structure of NRAS isoform 5 to be used as a starting point to understand its biophysical interactions. The isoform is highly flexible in aqueous solution, but forms a helix-turn-coil structure in the presence of trifluoroethanol as determined by NMR and CD spectroscopy.

Selective membrane disruption by the cyclotide kalata B7: complex ions and essential functional groups in the phosphatidylethanolamine binding pocket

The cyclic cystine knot plant peptides called cyclotides are active against a wide variety of organisms. This is primarily achieved through membrane binding and disruption, in part deriving from a high affinity for phosphatidylethanolamine (PE) lipids. Some cyclotides, such as kalata B7 (kB7), form complexes with divalent cations in a pocket associated with the tyrosine residue at position 15 (Tyr15). In the current work we explore the effect of cations on membrane leakage caused by cyclotides kB1, kB2 and kB7, and we identify a functional group that is essential for PE selectivity. The presence of PE-lipids in liposomes increased the membrane permeabilizing potency of the cyclotides, with the potency of kB7 increasing by as much as 740-fold. The divalent cations Mn(2+), Mg(2+) and Ca(2+) had no apparent effect on PE selectivity. However, amino acid substitutions in kB7 proved that Tyr15 is crucial for PE-selective membrane permeabilization on various liposome systems. Although the tertiary structure of kB7 was maintained, as reflected by the NMR solution structure, mutating Tyr into Ser at position 15 resulted in substantially reduced PE selectivity. Ala substitution at the same position produced a similar reduction in PE selectivity, while substitution with Phe maintained high selectivity. We conclude that the phenyl ring in Tyr15 is critical for the high PE selectivity of kB7. Our results suggest that PE-binding and divalent cation coordination occur in the same pocket without adverse effects of competitive binding for the phospholipid.

Protein structure determination by conformational space annealing using NMR geometric restraints

We have carried out numerical experiments to investigate the applicability of the global optimization method of conformational space annealing (CSA) to the enhanced NMR protein structure determination over existing PDB structures. The NMR protein structure determination is driven by the optimization of collective multiple restraints arising from experimental data and the basic stereochemical properties of a protein-like molecule. By rigorous and straightforward application of CSA to the identical NMR experimental data used to generate existing PDB structures, we redetermined 56 recent PDB protein structures starting from fully randomized structures. The quality of CSA-generated structures and existing PDB structures were assessed by multiobjective functions in terms of their consistencies with experimental data and the requirements of protein-like stereochemistry. In 54 out of 56 cases, CSA-generated structures were better than existing PDB structures in the Pareto-dominant manner, while in the remaining two cases, it was a tie with mixed results. As a whole, all structural features tested improved in a statistically meaningful manner. The most improved feature was the Ramachandran favored portion of backbone torsion angles with about 8.6% improvement from 88.9% to 97.5% (P-value <10(-17)). We show that by straightforward application of CSA to the efficient global optimization of an energy function, NMR structures will be of better quality than existing PDB structures.

A community resource of experimental data for NMR / X-ray crystal structure pairs

We have developed an online NMR / X-ray Structure Pair Data Repository. The NIGMS Protein Structure Initiative (PSI) has provided many valuable reagents, 3D structures, and technologies for structural biology. The Northeast Structural Genomics Consortium was one of several PSI centers. NESG used both X-ray crystallography and NMR spectroscopy for protein structure determination. A key goal of the PSI was to provide experimental structures for at least one representative of each of hundreds of targeted protein domain families. In some cases, structures for identical (or nearly identical) constructs were determined by both NMR and X-ray crystallography. NMR spectroscopy and X-ray diffraction data for 41 of these "NMR / X-ray" structure pairs determined using conventional triple-resonance NMR methods with extensive sidechain resonance assignments have been organized in an online NMR / X-ray Structure Pair Data Repository. In addition, several NMR data sets for perdeuterated, methyl-protonated protein samples are included in this repository. As an example of the utility of this repository, these data were used to revisit questions about the precision and accuracy of protein NMR structures first outlined by Levy and coworkers several years ago (Andrec et al., Proteins 2007;69:449-465). These results demonstrate that the agreement between NMR and X-ray crystal structures is improved using modern methods of protein NMR spectroscopy. The NMR / X-ray Structure Pair Data Repository will provide a valuable resource for new computational NMR methods development.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

Protein structure determination by combining sparse NMR data with evolutionary couplings

Accurate protein structure determination by NMR is challenging for larger proteins, for which experimental data is often incomplete and ambiguous. Fortunately, the upsurge in evolutionary sequence information and advances in maximum entropy statistical methods now provide a rich complementary source of structural constraints. We have developed a hybrid approach (EC-NMR) combining sparse NMR data with evolutionary residue-residue couplings, and demonstrate accurate structure determination for several 6 to 41 kDa proteins.