A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

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.

The NS1 protein of influenza B virus binds 5'-triphosphorylated dsRNA to suppress RIG-I activation and the host antiviral response

Influenza A and B viruses overcome the host antiviral response to cause a contagious and often severe human respiratory disease. Here, integrative structural biology and biochemistry studies on non-structural protein 1 of influenza B virus (NS1B) reveal a previously unrecognized viral mechanism for innate immune evasion. Conserved basic groups of its C-terminal domain (NS1B-CTD) bind 5'triphosphorylated double-stranded RNA (5'-ppp-dsRNA), the primary pathogen-associated feature that activates the host retinoic acid-inducible gene I protein (RIG-I) to initiate interferon synthesis and the cellular antiviral response. Like RIG-I, NS1B-CTD preferentially binds blunt-end 5'ppp-dsRNA. NS1B-CTD also competes with RIG-I for binding 5'ppp-dsRNA, and thus suppresses activation of RIG-I's ATPase activity. Although the NS1B N-terminal domain also binds dsRNA, it utilizes a different binding mode and lacks 5'ppp-dsRNA end preferences. In cells infected with wild-type influenza B virus, RIG-I activation is inhibited. In contrast, RIG-I activation and the resulting phosphorylation of transcription factor IRF-3 are not inhibited in cells infected with a mutant virus encoding NS1B with a R208A substitution it its CTD that eliminates its 5'ppp-dsRNA binding activity. These results reveal a novel mechanism in which NS1B binds 5'ppp-dsRNA to inhibit the RIG-I antiviral response during influenza B virus infection, and open the door to new avenues for antiviral drug discovery.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.

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.

The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.

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.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

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.

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Protocol for production and purification of SARS-CoV-2 3CLpro

3CL^pro protease from SARS-CoV-2 is a primary target for COVID-19 antiviral drug development. Here, we present a protocol for 3CL^pro production in Escherichia coli. We describe steps to purify 3CL^pro, expressed as a fusion with the Saccharomyces cerevisiae SUMO protein, with yields up to 120 mg L^-1 following cleavage. The protocol also provides isotope-enriched samples suitable for nuclear magnetic resonance (NMR) studies. We also present methods to characterize 3CL^pro by mass spectrometry, X-ray crystallography, heteronuclear NMR, and a Förster-resonance-energy-transfer-based enzyme assay. For complete details on the use and execution of this protocol, please refer to Bafna et al.¹.

Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding-upon-binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict the three-dimensional structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine the structures of peptide-protein complexes. Here, we use the MELD (Modeling Employing Limited Data) technique applied to polypeptide complexes formed with the extraterminal domain (ET) of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can adopt different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only protein receptor backbone chemical shift data, in excellent agreement with experimentally determined structures for moderately tight (K_d ∼100 nM) binders. The hybrid MELD + NMR approach required additional peptide ligand chemical shift data for weaker (K_d ∼250 μM) peptide binding partners. AlphaFold also successfully predicts the structures of some of these peptide-protein complexes. However, whereas AlphaFold can provide qualitative peptide rankings, MELD can directly estimate relative binding affinities. The hybrid MELD + NMR approach offers a powerful new tool for structural analysis of protein-polypeptide complexes involving disorder-to-order transitions upon complex formation, which are not successfully modeled with most other complex prediction methods, providing both the 3D structures of peptide-protein complexes and their relative binding affinities.

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 similarities between SARS-CoV2 3CLpro and other viral proteases suggest potential lead molecules for developing broad spectrum antivirals

Considering the significant impact of the recent COVID-19 outbreak, development of broad-spectrum antivirals is a high priority goal to prevent future global pandemics. Antiviral development processes generally emphasize targeting a specific protein from a particular virus. However, some antiviral agents developed for specific viral protein targets may exhibit broad spectrum antiviral activity, or at least provide useful lead molecules for broad spectrum drug development. There is significant potential for repurposing a wide range of existing viral protease inhibitors to inhibit the SARS-CoV2 3C-like protease (3CLpro). If effective even as relatively weak inhibitors of 3CLpro, these molecules can provide a diverse and novel set of scaffolds for new drug discovery campaigns. In this study, we compared the sequence- and structure-based similarity of SARS-CoV2 3CLpro with proteases from other viruses, and identified 22 proteases with similar active-site structures. This structural similarity, characterized by secondary-structure topology diagrams, is evolutionarily divergent within taxonomically related viruses, but appears to result from evolutionary convergence of protease enzymes between virus families. Inhibitors of these proteases that are structurally similar to the SARS-CoV2 3CLpro protease were identified and assessed as potential inhibitors of SARS-CoV2 3CLpro protease by virtual docking. Several of these molecules have docking scores that are significantly better than known SARS-CoV2 3CLpro inhibitors, suggesting that these molecules are also potential inhibitors of the SARS-CoV2 3CLpro protease. Some have been previously reported to inhibit SARS-CoV2 3CLpro. The results also suggest that established inhibitors of SARS-CoV2 3CLpro may be considered as potential inhibitors of other viral 3C-like proteases.

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.

SpecDB: A relational database for archiving biomolecular NMR spectral data

NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.

Oligomeric interactions maintain active-site structure in a noncooperative enzyme family

The evolutionary benefit accounting for widespread conservation of oligomeric structures in proteins lacking evidence of intersubunit cooperativity remains unclear. Here, crystal and cryo-EM structures, and enzymological data, demonstrate that a conserved tetramer interface maintains the active-site structure in one such class of proteins, the short-chain dehydrogenase/reductase (SDR) superfamily. Phylogenetic comparisons support a significantly longer polypeptide being required to maintain an equivalent active-site structure in the context of a single subunit. Oligomerization therefore enhances evolutionary fitness by reducing the metabolic cost of enzyme biosynthesis. The large surface area of the structure-stabilizing oligomeric interface yields a synergistic gain in fitness by increasing tolerance to activity-enhancing yet destabilizing mutations. We demonstrate that two paralogous SDR superfamily enzymes with different specificities can form mixed heterotetramers that combine their individual enzymological properties. This suggests that oligomerization can also diversify the functions generated by a given metabolic investment, enhancing the fitness advantage provided by this architectural strategy.

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.

Structural evolution of the ancient enzyme, dissimilatory sulfite reductase

Dissimilatory sulfite reductase is an ancient enzyme that has linked the global sulfur and carbon biogeochemical cycles since at least 3.47 Gya. While much has been learned about the phylogenetic distribution and diversity of DsrAB across environmental gradients, far less is known about the structural changes that occurred to maintain DsrAB function as the enzyme accompanied diversification of sulfate/sulfite reducing organisms (SRO) into new environments. Analyses of available crystal structures of DsrAB from Archaeoglobus fulgidus and Desulfovibrio vulgaris, representing early and late evolving lineages, respectively, show that certain features of DsrAB are structurally conserved, including active siro-heme binding motifs. Whether such structural features are conserved among DsrAB recovered from varied environments, including hot spring environments that host representatives of the earliest evolving SRO lineage (e.g., MV2-Eury), is not known. To begin to overcome these gaps in our understanding of the evolution of DsrAB, structural models from MV2.Eury were generated and evolutionary sequence co-variance analyses were conducted on a curated DsrAB database. Phylogenetically diverse DsrAB harbor many conserved functional residues including those that ligate active siro-heme(s). However, evolutionary co-variance analysis of monomeric DsrAB subunits revealed several False Positive Evolutionary Couplings (FPEC) that correspond to residues that have co-evolved despite being too spatially distant in the monomeric structure to allow for direct contact. One set of FPECs corresponds to residues that form a structural path between the two active siro-heme moieties across the interface between heterodimers, suggesting the potential for allostery or electron transfer within the enzyme complex. Other FPECs correspond to structural loops and gaps that may have been selected to stabilize enzyme function in different environments. These structural bioinformatics results suggest that DsrAB has maintained allosteric communication pathways between subunits as SRO diversified into new environments. The observations outlined here provide a framework for future biochemical and structural analyses of DsrAB to examine potential allosteric control of this enzyme.

SETD4-mediated KU70 methylation suppresses apoptosis

The mammalian KU70 is a pleiotropic protein functioning in DNA repair and cytoplasmic suppression of apoptosis. We report a regulatory mechanism by which KU70’s cytoplasmic function is enabled due to a methylation at K570 of KU70 by SET-domain-containing protein 4 (SETD4). While SETD4 silencing reduces the level of methylated KU70, over-expression of SETD4 enhances methylation of KU70. Mutations of Y272 and Y284 of SETD4 abrogate methylation of KU70. Although SETD4 is predominantly a nuclear protein, the methylated KU70 is enriched in the cytoplasm. SETD4 knockdown enhances staurosporine (STS)-induced apoptosis and cell killing. Over-expression of the wild-type (WT) SETD4, but not the SETD4-Y272/Y284F mutant, suppresses STS-induced apoptosis. The KU70-K570R (mouse Ku70-K568R) mutation dampens the anti-apoptosis activity of KU70. Our study identifies KU70 as a non-histone substrate of SETD4, discovers a post-translational modification of KU70, and uncovers a role for SETD4 and KU70-K570 methylation in the suppression of apoptosis.

Wang et al. identify the methylation of mammalian KU70 by SETD4. This post-translational modification is critical for KU70 localization to the cytoplasm and subsequent suppression of apoptosis.

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.

Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.

Hepatitis C virus drugs that inhibit SARS-CoV-2 papain-like protease synergize with remdesivir to suppress viral replication in cell culture

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (M^pro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the M^pro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 M^pro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their M^pro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PL^pro). HCV drugs that inhibit PL^pro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit M^pro do not synergize with remdesivir.

ZapG (YhcB/DUF1043), a novel cell division protein in gamma-proteobacteria linking the Z-ring to septal peptidoglycan synthesis

YhcB, a poorly understood protein conserved across gamma-proteobacteria, contains a domain of unknown function (DUF1043) and an N-terminal transmembrane domain. Here, we used an integrated approach including X-ray crystallography, genetics, and molecular biology to investigate the function and structure of YhcB. The Escherichia coli yhcB KO strain does not grow at 45 °C and is hypersensitive to cell wall–acting antibiotics, even in the stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septum development. The Z-ring is essential for the positioning of the septa and the initiation of cell division. We found that YhcB interacts with proteins of the divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). Seven of these interactions are also conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we mapped the amino acid residues likely involved in the interactions of YhcB with FtsI and RodZ. The 2.8 Å crystal structure of the cytosolic domain of Haemophilus ducreyi YhcB shows a unique tetrameric α-helical coiled-coil structure likely to be involved in linking the Z-ring to the septal peptidoglycan-synthesizing complexes. In summary, YhcB is a conserved and conditionally essential protein that plays a role in cell division and consequently affects envelope biogenesis. Based on these findings, we propose to rename YhcB to ZapG (Z-ring-associated protein G). This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.

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.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 Mpro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 Mpro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (Mpro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of Mpro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the Mpro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the Mpro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 M^pro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 M^pro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (M^pro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of M^pro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the M^pro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the M^pro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

Structural Similarity of SARS-CoV2 Mpro and HCV NS3/4A Proteases Suggests New Approaches for Identifying Existing Drugs Useful as COVID-19 Therapeutics

During the current COVID-19 pandemic more than 160,000 people have died worldwide as of mid-April 2020, and the global economy has been crippled. Effective control of the SARS-CoV2 virus that causes the COVID-19 pandemic requires both vaccines and antivirals. Antivirals are particularly crucial to treat infected people during the period of time that an effective vaccine is being developed and deployed. Because the development of specific antiviral drugs can take a considerable length of time, an important approach is to identify existing drugs already approved for use in humans which could be repurposed as COVID-19 therapeutics. Here we focus on antivirals directed against the SARS-CoV2 M^pro protease, which is required for virus replication. A structural similarity search showed that the Hepatitis C virus (HCV) NS3/4A protease has a striking three-dimensional structural similarity to the SARS-CoV2 M^pro protease, particularly in the arrangement of key active site residues. We used virtual docking predictions to assess the hypothesis that existing drugs already approved for human use or clinical testing that are directed at the HCV NS3/4A protease might fit well into the active-site cleft of the SARS-CoV2 protease (M^pro). AutoDock docking scores for 12 HCV protease inhibitors and 9 HIV-1 protease inhibitors were determined and compared to the docking scores for an α-ketoamide inhibitor of M^pro, which has recently been shown to inhibit SARS-CoV2 virus replication in cell culture. We identified eight HCV protease inhibitors that bound to the M^pro active site with higher docking scores than the α-ketoamide inhibitor, suggesting that these protease inhibitors may effectively bind to the M^pro active site. These results provide the rationale for us to test the identified HCV protease inhibitors as inhibitors of the SARS-CoV2 protease, and as inhibitors of SARS-CoV2 virus replication. Subsequently these repurposed drugs could be evaluated as COVID-19 therapeutics.

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.

High-Throughput PIXE as an Essential Quantitative Assay for Accurate Metalloprotein Structural Analysis: Development and Application

Metalloproteins comprise over one-third of proteins, with approximately half of all enzymes requiring metal to function. Accurate identification of these metal atoms and their environment is a prerequisite to understanding biological mechanism. Using ion beam analysis through particle induced X-ray emission (PIXE), we have quantitatively identified the metal atoms in 30 previously structurally characterized proteins using minimal sample volume and a high-throughput approach. Over half of these metals had been misidentified in the deposited structural models. Some of the PIXE detected metals not seen in the models were explainable as artifacts from promiscuous crystallization reagents. For others, using the correct metal improved the structural models. For multinuclear sites, anomalous diffraction signals enabled the positioning of the correct metals to reveal previously obscured biological information. PIXE is insensitive to the chemical environment, but coupled with experimental diffraction data deposited alongside the structural model it enables validation and potential remediation of metalloprotein models, improving structural and, more importantly, mechanistic knowledge.

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.

Evolutionary coupling saturation mutagenesis: Coevolution-guided identification of distant sites influencing Bacillus naganoensis pullulanase activity

Pullulanases are well-known debranching enzymes hydrolyzing α-1,6-glycosidic linkages. To date, engineering of pullulanase is mainly focused on catalytic pocket or domain tailoring based on structure/sequence information. Saturation mutagenesis-involved directed evolution is, however, limited by the low number of mutational sites compatible with combinatorial libraries of feasible size. Using Bacillus naganoensis pullulanase as a target protein, here we introduce the 'evolutionary coupling saturation mutagenesis' (ECSM) approach: residue pair covariances are calculated to identify residues for saturation mutagenesis, focusing directed evolution on residue pairs playing important roles in natural evolution. Evolutionary coupling (EC) analysis identified seven residue pairs as evolutionary mutational hotspots. Subsequent saturation mutagenesis yielded variants with enhanced catalytic activity. The functional pairs apparently represent distant sites affecting enzyme activity.

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.

Structural Basis by Which the N-Terminal Polypeptide Segment of Rhizopus chinensis Lipase Regulates Its Substrate Binding Affinity

Members of an important group of industrial enzymes, Rhizopus lipases, exhibit valuable hydrolytic features that underlie their biological functions. Particularly important is their N-terminal polypeptide segment (NTPS), which is required for secretion and proper folding but is removed in the process of enzyme maturation. A second common feature of this class of lipases is the α-helical "lid", which regulates the accessibility of the substrate to the enzyme active site. Some Rhizopus lipases also exhibit "interfacial activation" by micelle and/or aggregate surfaces. While it has long been recognized that the NTPS is critical for function, its dynamic features have frustrated efforts to characterize its structure by X-ray crystallography. Here, we combine nuclear magnetic resonance spectroscopy and X-ray crystallography to determine the structure and dynamics of Rhizopus chinensis lipase (RCL) with its 27-residue NTPS prosequence (r27RCL). Both r27RCL and the truncated mature form of RCL (mRCL) exhibit biphasic interfacial activation kinetics with p-nitrophenyl butyrate (pNPB). r27RCL exhibits a substrate binding affinity significantly lower than that of mRCL due to stabilization of the closed lid conformation by the NTPS. In contrast to previous predictions, the NTPS does not enhance lipase activity by increasing surface hydrophobicity but rather inhibits activity by forming conserved interactions with both the closed lid and the core protein structure. Single-site mutations and kinetic studies were used to confirm that the NTPS serves as internal competitive inhibitor and to develop a model of the associated process of interfacial activation. These structure-function studies provide the basis for engineering RCL lipases with enhanced catalytic activities.

A Proteomic Screen of Neuronal Cell-Surface Molecules Reveals IgLONs as Structurally Conserved Interaction Modules at the Synapse

In the developing brain, cell-surface proteins play crucial roles, but their protein-protein interaction network remains largely unknown. A proteomic screen identified 200 interactions, 89 of which were not previously published. Among these interactions, we find that the IgLONs, a family of five cell-surface neuronal proteins implicated in various human disorders, interact as homo- and heterodimers. We reveal their interaction patterns and report the dimeric crystal structures of Neurotrimin (NTRI), IgLON5, and the neuronal growth regulator 1 (NEGR1)/IgLON5 complex. We show that IgLONs maintain an extended conformation and that their dimerization occurs through the first Ig domain of each monomer and is Ca²⁺ independent. Cell aggregation shows that NTRI and NEGR1 homo- and heterodimerize in trans. Taken together, we report 89 unpublished cell-surface ligand-receptor pairs and describe structural models of trans interactions of IgLONs, showing that their structures are compatible with a model of interaction across the synaptic cleft.

The copBL operon protects Staphylococcus aureus from copper toxicity: CopL is an extracellular membrane-associated copper-binding protein

As complications associated with antibiotic resistance have intensified, copper (Cu) is attracting attention as an antimicrobial agent. Recent studies have shown that copper surfaces decrease microbial burden, and host macrophages use Cu to increase bacterial killing. Not surprisingly, microbes have evolved mechanisms to tightly control intracellular Cu pools and protect against Cu toxicity. Here, we identified two genes (copB and copL) encoded within the Staphylococcus aureus arginine-catabolic mobile element (ACME) that we hypothesized function in Cu homeostasis. Supporting this hypothesis, mutational inactivation of copB or copL increased copper sensitivity. We found that copBL are co-transcribed and that their transcription is increased during copper stress and in a strain in which csoR, encoding a Cu-responsive transcriptional repressor, was mutated. Moreover, copB displayed genetic synergy with copA, suggesting that CopB functions in Cu export. We further observed that CopL functions independently of CopB or CopA in Cu toxicity protection and that CopL from the S. aureus clone USA300 is a membrane-bound and surface-exposed lipoprotein that binds up to four Cu+ ions. Solution NMR structures of the homologous Bacillus subtilis CopL, together with phylogenetic analysis and chemical-shift perturbation experiments, identified conserved residues potentially involved in Cu+ coordination. The solution NMR structure also revealed a novel Cu-binding architecture. Of note, a CopL variant with defective Cu+ binding did not protect against Cu toxicity in vivo Taken together, these findings indicate that the ACME-encoded CopB and CopL proteins are additional factors utilized by the highly successful S. aureus USA300 clone to suppress copper toxicity.

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.

Antiparallel Coiled-Coil Interactions Mediate the Homodimerization of the DNA Damage-Repair Protein PALB2

Deficits in DNA damage-repair pathways are the root cause of several human cancers. In mammalian cells, DNA double-strand break repair is carried out by multiple mechanisms, including homologous recombination (HR). The partner and localizer of BRCA2 (PALB2), which is an essential factor for HR, binds to the breast cancer susceptibility 1 (BRCA1) protein at DNA double-strand breaks. At the break site, PALB2 also associates with the breast cancer susceptibility 2 (BRCA2) protein to form a multiprotein complex that facilitates HR. The BRCA1-PALB2 interaction is mediated by association of predicted helical coiled-coil regions in both proteins. PALB2 can also homodimerize through the formation of a coiled coil by the self-association of helical elements at the N-terminus of the PALB2 protein, and this homodimerization has been proposed to regulate the efficiency of HR. We have produced a segment of PALB2, designated PALB2cc (PALB2 coiled coil segment) that forms α-helical structures, which assemble into stable homodimers. PALB2cc also forms heterodimers with a helical segment of BRCA1, called BRCA1cc (BRCA1 coiled coil segment). The three-dimensional structure of the homodimer formed by PALB2cc was determined by solution NMR spectroscopy. This PALB2cc homodimer is a classical antiparallel coiled-coil leucine zipper. NMR chemical-shift perturbation studies were used to study dimer formation for both the PALB2cc homodimer and the PALB2cc/BRCA1cc heterodimer. The mutation of residue Leu24 of PALB2cc  significantly reduces its homodimer stability, but has a more modest effect on the stability of the heterodimer formed between PALB2cc and BRCA1cc. We show that mutation of Leu24 leads to genomic instability and reduced cell viability after treatment with agents that induce DNA double-strand breaks. These studies may allow the identification of distinct mutations of PALB2cc that selectively disrupt homodimeric versus heterodimeric interactions, and reveal the specific role of PALB2cc homodimerization in HR.

Minimal Heterochiral de Novo Designed 4Fe-4S Binding Peptide Capable of Robust Electron Transfer

Ambidoxin is a designed, minimal dodecapeptide consisting of alternating L and D amino acids that binds a 4Fe-4S cluster through ligand-metal interactions and an extensive network of second-shell hydrogen bonds. The peptide can withstand hundreds of oxidation-reduction cycles at room temperature. Ambidoxin suggests how simple, prebiotic peptides may have achieved robust redox catalysis on the early Earth.