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.

Development of Polyamine-Substituted Triphenylamine Ligands with High Affinity and Selectivity for G-Quadruplex DNA.

Currently, significant efforts are devoted to designing small molecules able to bind selectively to guanine quadruplexes (G4s). These noncanonical DNA structures are implicated in various important biological processes and have been identified as potential targets for drug development. Previously, a series of triphenylamine (TPA)-based compounds, including macrocyclic polyamines, that displayed high affinity towards G4 DNA were reported. Following this initial work, herein a series of second-generation compounds, in which the central TPA has been functionalised with flexible and adaptive linear polyamines, are presented with the aim of maximising the selectivity towards G4 DNA. The acid-base properties of the new derivatives have been studied by means of potentiometric titrations, UV/Vis and fluorescence emission spectroscopy. The interaction with G4s and duplex DNA has been explored by using FRET melting assays, fluorescence spectroscopy and circular dichroism. Compared with previous TPA derivatives with macrocyclic substituents, the new ligands reported herein retain the G4 affinity, but display two orders of magnitude higher selectivity for G4 versus duplex DNA; this is most likely due to the ability of the linear substituents to embrace the G4 structure.

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.

Synthesis, Characterization, and Cu(2+) Coordination Studies of a 3-Hydroxy-4-pyridinone Aza Scorpiand Derivative.

The synthesis, acid-base behavior, and Cu(2+) coordination chemistry of a new ligand (L1) consisting of an azamacrocyclic core appended with a lateral chain containing a 3-hydroxy-2-methyl-4(1H)-pyridinone group have been studied by potentiometry, cyclic voltammetry, and NMR and UV-vis spectroscopy. UV-vis and NMR studies showed that phenolate group was protonated at the highest pH values [log K = 9.72(1)]. Potentiometric studies point out the formation of Cu(2+) complexes of 1:2, 2:2, 4:3, 1:1, and 2:1 Cu(2+)/L1 stoichiometries. UV-vis analysis and electrochemical studies evidence the implication of the pyridinone moieties in the metal coordination of the 1:2 Cu(2+)/L1 complexes. L1 shows a stronger chelating ability than the reference chelating ligand deferiprone. While L1 shows no cytotoxicity in HeLa and ARPE-19 human cell lines (3.1-25.0 µg/mL), it has significant antioxidant activity, as denoted by TEAC assays at physiological pH. The addition of Cu(2+) diminishes the antioxidant activity because of its coordination to the pyridinone moiety phenolic group.

Analysis of the structural quality of the CASD-NMR 2013 entries.

We performed a comprehensive structure validation of both automated and manually generated structures of the 10 targets of the CASD-NMR-2013 effort. We established that automated structure determination protocols are capable of reliably producing structures of comparable accuracy and quality to those generated by a skilled researcher, at least for small, single domain proteins such as the ten targets tested. The most robust results appear to be obtained when NOESY peak lists are used either as the primary input data or to augment chemical shift data without the need to manually filter such lists. A detailed analysis of the long-range NOE restraints generated by the different programs from the same data showed a surprisingly low degree of overlap. Additionally, we found that there was no significant correlation between the extent of the NOE restraint overlap and the accuracy of the structure. This result was surprising given the importance of NOE data in producing good quality structures. We suggest that this could be explained by the information redundancy present in NOEs between atoms contained within a fixed covalent network.

The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013.

The second round of the community-wide initiative Critical Assessment of automated Structure Determination of Proteins by NMR (CASD-NMR-2013) comprised ten blind target datasets, consisting of unprocessed spectral data, assigned chemical shift lists and unassigned NOESY peak and RDC lists, that were made available in both curated (i.e. manually refined) or un-curated (i.e. automatically generated) form. Ten structure calculation programs, using fully automated protocols only, generated a total of 164 three-dimensional structures (entries) for the ten targets, sometimes using both curated and un-curated lists to generate multiple entries for a single target. The accuracy of the entries could be established by comparing them to the corresponding manually solved structure of each target, which was not available at the time the data were provided. Across the entire data set, 71 % of all entries submitted achieved an accuracy relative to the reference NMR structure better than 1.5 Å. Methods based on NOESY peak lists achieved even better results with up to 100% of the entries within the 1.5 Å threshold for some programs. However, some methods did not converge for some targets using un-curated NOESY peak lists. Over 90% of the entries achieved an accuracy better than the more relaxed threshold of 2.5 Å that was used in the previous CASD-NMR-2010 round. Comparisons between entries generated with un-curated versus curated peaks show only marginal improvements for the latter in those cases where both calculations converged.

Equilibrium, kinetic, and computational studies on the formation of Cu2+ and Zn2+ complexes with an indazole-containing azamacrocyclic scorpiand: evidence for metal-induced tautomerism.

Cu(2+) and Zn(2+) coordination chemistry of a new member of the family of scorpiand-like macrocyclic ligands derived from tris(2-aminoethyl)amine (tren) is reported. The new ligand (L1) contains in its pendant arm not only the amine group derived from tren but also a 6-indazole ring. Potentiometric studies allow the determination of four protonation constants. UV-vis and fluorescence data support that the last protonation step occurs on the indazole group. Equilibrium measurements in the presence of Cu(2+) and Zn(2+) reveal the formation of stable [ML1](2+), [MHL1](3+), and [ML1(OH)](+) complexes. Kinetic studies on the acid-promoted decomposition of the metal complexes were carried out using both absorbance and fluorescence detection. For Zn(2+), both types of detection led to the same results. The experiments suggest that [ZnL1](2+) protonates upon addition of an acid excess to form [ZnHL1](3+) within the mixing time of the stopped-flow instrument, which then decomposes with a first-order dependence on the acid concentration. The kinetic behavior is more complex in the case of Cu(2+). Both [CuL1](2+) and [CuHL1](3+) show similar absorption spectra and convert within the mixing time to a new intermediate species with a band at 750 nm, the process being reverted by addition of base. The intermediate then decomposes with a second-order dependence on the acid concentration. However, kinetic experiments with fluorescence detection showed the existence of an additional faster step. With the help of DFT calculations, an interpretation is proposed in which protonation of [CuL1](2+) to form [CuHL1](3+) would involve dissociation of the tren-based NH group in the pendant arm and coordination of a 2H-indazole group. Further protonation would lead to dissociation of coordinated indazole, which then will convert to the more stable 1H tautomer in a process signaled by fluorescence changes that would not be affecting to the d-d spectrum of the complex.

The expanded FindCore method for identification of a core atom set for assessment of protein structure prediction.

Maximizing the scientific impact of NMR-based structure determination requires robust and statistically sound methods for assessing the precision of NMR-derived structures. In particular, a method to define a core atom set for calculating superimpositions and validating structure predictions is critical to the use of NMR-derived structures as targets in the CASP competition. FindCore (Snyder and Montelione, Proteins 2005;59:673-686) is a superimposition independent method for identifying a core atom set and partitioning that set into domains. However, as FindCore optimizes superimposition by sensitively excluding not-well-defined atoms, the FindCore core may not comprise all atoms suitable for use in certain applications of NMR structures, including the CASP assessment process. Adapting the FindCore approach to assess predicted models against experimental NMR structures in CASP10 required modification of the FindCore method. This paper describes conventions and a standard protocol to calculate an "Expanded FindCore" atom set suitable for validation and application in biological and biophysical contexts. A key application of the Expanded FindCore method is to identify a core set of atoms in the experimental NMR structure for which it makes sense to validate predicted protein structure models. We demonstrate the application of this Expanded FindCore method in characterizing well-defined regions of 18 NMR-derived CASP10 target structures. The Expanded FindCore protocol defines "expanded core atom sets" that match an expert's intuition of which parts of the structure are sufficiently well defined to use in assessing CASP model predictions. We also illustrate the impact of this analysis on the CASP GDT assessment scores.

Protein NMR structures refined with Rosetta have higher accuracy relative to corresponding X-ray crystal structures.

We have found that refinement of protein NMR structures using Rosetta with experimental NMR restraints yields more accurate protein NMR structures than those that have been deposited in the PDB using standard refinement protocols. Using 40 pairs of NMR and X-ray crystal structures determined by the Northeast Structural Genomics Consortium, for proteins ranging in size from 5-22 kDa, restrained Rosetta refined structures fit better to the raw experimental data, are in better agreement with their X-ray counterparts, and have better phasing power compared to conventionally determined NMR structures. For 37 proteins for which NMR ensembles were available and which had similar structures in solution and in the crystal, all of the restrained Rosetta refined NMR structures were sufficiently accurate to be used for solving the corresponding X-ray crystal structures by molecular replacement. The protocol for restrained refinement of protein NMR structures was also compared with restrained CS-Rosetta calculations. For proteins smaller than 10 kDa, restrained CS-Rosetta, starting from extended conformations, provides slightly more accurate structures, while for proteins in the size range of 10-25 kDa the less CPU intensive restrained Rosetta refinement protocols provided equally or more accurate structures. The restrained Rosetta protocols described here can improve the accuracy of protein NMR structures and should find broad and general for studies of protein structure and function.

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.

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

The heterogeneous array of software tools used in the process of protein NMR structure determination presents organizational challenges in the structure determination and validation processes, and creates a learning curve that limits the broader use of protein NMR in biology. These challenges, including accurate use of data in different data formats required by software carrying out similar tasks, continue to confound the efforts of novices and experts alike. These important issues need to be addressed robustly in order to standardize protein NMR structure determination and validation. PDBStat is a C/C++ computer program originally developed as a universal coordinate and protein NMR restraint converter. Its primary function is to provide a user-friendly tool for interconverting between protein coordinate and protein NMR restraint data formats. It also provides an integrated set of computational methods for protein NMR restraint analysis and structure quality assessment, relabeling of prochiral atoms with correct IUPAC names, as well as multiple methods for analysis of the consistency of atomic positions indicated by their convergence across a protein NMR ensemble. In this paper we provide a detailed description of the PDBStat software, and highlight some of its valuable computational capabilities. As an example, we demonstrate the use of the PDBStat restraint converter for restrained CS-Rosetta structure generation calculations, and compare the resulting protein NMR structure models with those generated from the same NMR restraint data using more traditional structure determination methods. These results demonstrate the value of a universal restraint converter in allowing the use of multiple structure generation methods with the same restraint data for consensus analysis of protein NMR structures and the underlying restraint data.

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.

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.

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.

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.

Tritopic phenanthroline and pyridine tail-tied aza-scorpiands.

The synthesis of two new tritopic double-scorpiand receptors in which two equivalent 5-(2-aminoethyl)-2,5,8-triaza[9]-(2,6)-pyridinophane moieties have been linked with 2,6-dimethylpyridine (L1) or 2,9-dimethylphenanthroline (L2) units is reported for the first time. Their acid-base behaviour and Zn(2+) coordination chemistry have been studied by pH-metric titrations, molecular dynamic calculations, NMR, UV-Vis and steady-state fluorescence techniques. L1 and L2 behave, respectively, as hexaprotic and heptaprotic bases in the experimental conditions used (298.1 +/- 0.1 K, 0.15 mol dm(-3) NaCl, pH range under study 2.0-11.0). These ligands are able to form mono-, bi- and trinuclear Zn(2+) complexes depending on the Zn(2+)-receptor molar ratio. Interaction of L1 and L2 with pyrophosphate (PPi), tripolyphosphate (TPP) and adenosine 5'-triphosphate (ATP) has been followed by pH-metric titrations, (1)H and (31)P NMR techniques and molecular dynamic analysis. Finally, formation of mixed complexes Zn(2+)-L-PPi, Zn(2+)-L-TPP and Zn(2+)-L-ATP has been studied for both receptors by potentiometric titrations.

Construct optimization for protein NMR structure analysis using amide hydrogen/deuterium exchange mass spectrometry.

Disordered or unstructured regions of proteins, while often very important biologically, can pose significant challenges for resonance assignment and three-dimensional structure determination of the ordered regions of proteins by NMR methods. In this article, we demonstrate the application of (1)H/(2)H exchange mass spectrometry (DXMS) for the rapid identification of disordered segments of proteins and design of protein constructs that are more suitable for structural analysis by NMR. In this benchmark study, DXMS is applied to five NMR protein targets chosen from the Northeast Structural Genomics project. These data were then used to design optimized constructs for three partially disordered proteins. Truncated proteins obtained by deletion of disordered N- and C-terminal tails were evaluated using (1)H-(15)N HSQC and (1)H-(15)N heteronuclear NOE NMR experiments to assess their structural integrity. These constructs provide significantly improved NMR spectra, with minimal structural perturbations to the ordered regions of the protein structure. As a representative example, we compare the solution structures of the full length and DXMS-based truncated construct for a 77-residue partially disordered DUF896 family protein YnzC from Bacillus subtilis, where deletion of the disordered residues (ca. 40% of the protein) does not affect the native structure. In addition, we demonstrate that throughput of the DXMS process can be increased by analyzing mixtures of up to four proteins without reducing the sequence coverage for each protein. Our results demonstrate that DXMS can serve as a central component of a process for optimizing protein constructs for NMR structure determination.

Assessing model accuracy using the homology modeling automatically software.

Homology modeling is a powerful technique that greatly increases the value of experimental structure determination by using the structural information of one protein to predict the structures of homologous proteins. We have previously described a method of homology modeling by satisfaction of spatial restraints (Li et al., Protein Sci 1997;6:956-970). The Homology Modeling Automatically (HOMA) web site, , is a new tool, using this method to predict 3D structure of a target protein based on the sequence alignment of the target protein to a template protein and the structure coordinates of the template. The user is presented with the resulting models, together with an extensive structure validation report providing critical assessments of the quality of the resulting homology models. The homology modeling method employed by HOMA was assessed and validated using twenty-four groups of homologous proteins. Using HOMA, homology models were generated for 510 proteins, including 264 proteins modeled with correct folds and 246 modeled with incorrect folds. Accuracies of these models were assessed by superimposition on the corresponding experimentally determined structures. A subset of these results was compared with parallel studies of modeling accuracy using several other automated homology modeling approaches. Overall, HOMA provides prediction accuracies similar to other state-of-the-art homology modeling methods. We also provide an evaluation of several structure quality validation tools in assessing the accuracy of homology models generated with HOMA. This study demonstrates that Verify3D (Luthy et al., Nature 1992;356:83-85) and ProsaII (Sippl, Proteins 1993;17:355-362) are most sensitive in distinguishing between homology models with correct or incorrect folds. For homology models that have the correct fold, the steric conformational energy (including primarily the Van der Waals energy), MolProbity clashscore (Word et al., Protein Sci 2000;9:2251-2259), and the PROCHECK G-factors (Laskowski et al., J Biomol NMR 1996;8:477-486) provide sensitive and consistent methods for assessing accuracy and can distinguish between homology models of higher and lower accuracy. As demonstrated in the accompanying paper (Bhattacharya et al., accompanying paper), combinations of these scores for models generated with HOMA provide a basis for distinguishing low from high accuracy models.

Enhancement of SOD activity in boehmite supported nanoreceptors.

The binuclear Cu2+ complex of a pyridinophane polyamine ligand ranking amongst the fastest SOD mimetics so far reported displays a remarkable SOD activity enhancement when grafted to the surface of boehmite (γ-AlO(OH)) nanoparticles (BNPs).