Abiotic foldamer quaternary structures.

Abiotic aromatic foldamer sequences have been previously shown to fold in helix-turn-helix motifs in organic solvents. Using simple computational tools, a new helix-turn-helix motif was designed that bears additional hydrogen bond donor OH groups to promote its aggregation into a genuine, trimeric, abiotic quaternary structure. This sequence was synthesized and its self-assembly in solution was investigated by Nuclear Magnetic Resonance (NMR), Circular Dichroism (CD) and Molecular Dynamics (MD) simulations. The existence of two stable discrete aggregates was evidenced, one assigned to the initially designed trimer, the other to a dimer including multiple water molecules. The two species may be quantitatively interconverted upon changing the water content of the solution or the temperature. These results represent important steps in the design of protein-like abiotic architectures.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

Democratizing protein language models with parameter-efficient fine-tuning.

Proteomics has been revolutionized by large protein language models (PLMs), which learn unsupervised representations from large corpora of sequences. These models are typically fine-tuned in a supervised setting to adapt the model to specific downstream tasks. However, the computational and memory footprint of fine-tuning (FT) large PLMs presents a barrier for many research groups with limited computational resources. Natural language processing has seen a similar explosion in the size of models, where these challenges have been addressed by methods for parameter-efficient fine-tuning (PEFT). In this work, we introduce this paradigm to proteomics through leveraging the parameter-efficient method LoRA and training new models for two important tasks: predicting protein-protein interactions (PPIs) and predicting the symmetry of homooligomer quaternary structures. We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters. We additionally show that for the PPI prediction task, training only the classification head also remains competitive with full FT, using five orders of magnitude fewer parameters, and that each of these methods outperform state-of-the-art PPI prediction methods with substantially reduced compute. We further perform a comprehensive evaluation of the hyperparameter space, demonstrate that PEFT of PLMs is robust to variations in these hyperparameters, and elucidate where best practices for PEFT in proteomics differ from those in natural language processing. All our model adaptation and evaluation code is available open-source at https://github.com/microsoft/peft_proteomics. Thus, we provide a blueprint to democratize the power of PLM adaptation to groups with limited computational resources.

Promising properties of cytochrome P450 BM3 reconstituted from separate domains by split intein.

Recombinant cytochrome P450 monooxygenases possess significant potential as biocatalysts, and efforts to improve heme content, electron coupling efficiency, and catalytic activity and stability are ongoing. Domain swapping between heme and reductase domains, whether natural or engineered, has thus received increasing attention. Here, we successfully achieved split intein-mediated reconstitution (IMR) of the heme and reductase domains of P450 BM3 both in vitro and in vivo. Intriguingly, the reconstituted enzymes displayed promising properties for practical use. IMR BM3 exhibited a higher heme content (>50 %) and a greater tendency for oligomerization compared to the wild-type enzyme. Moreover, these reconstituted enzymes exhibited a distinct increase in activity ranging from 165 % to 430 % even under the same heme concentrations. The reproducibility of our results strongly suggests that the proposed reconstitution approach could pave a new path for enhancing the catalytic efficiency of related enzymes.

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

Implementation of FRET Spectrometry Using Temporally Resolved Fluorescence: A Feasibility Study.

Förster resonance energy transfer (FRET) spectrometry is a method for determining the quaternary structure of protein oligomers from distributions of FRET efficiencies that are drawn from pixels of fluorescence images of cells expressing the proteins of interest. FRET spectrometry protocols currently rely on obtaining spectrally resolved fluorescence data from intensity-based experiments. Another imaging method, fluorescence lifetime imaging microscopy (FLIM), is a widely used alternative to compute FRET efficiencies for each pixel in an image from the reduction of the fluorescence lifetime of the donors caused by FRET. In FLIM studies of oligomers with different proportions of donors and acceptors, the donor lifetimes may be obtained by fitting the temporally resolved fluorescence decay data with a predetermined number of exponential decay curves. However, this requires knowledge of the number and the relative arrangement of the fluorescent proteins in the sample, which is precisely the goal of FRET spectrometry, thus creating a conundrum that has prevented users of FLIM instruments from performing FRET spectrometry. Here, we describe an attempt to implement FRET spectrometry on temporally resolved fluorescence microscopes by using an integration-based method of computing the FRET efficiency from fluorescence decay curves. This method, which we dubbed time-integrated FRET (or tiFRET), was tested on oligomeric fluorescent protein constructs expressed in the cytoplasm of living cells. The present results show that tiFRET is a promising way of implementing FRET spectrometry and suggest potential instrument adjustments for increasing accuracy and resolution in this kind of study.

Photobleaching FRET-FLIM-ICS for quaternary structure quantification on cells. Theory and simulations.

The oligomerization of proteins is an important biological control mechanism and has several functions in activity and stability of enzymes, structural proteins, ion channels and transcription factors. The determination of the relevant oligomeric states in terms of geometry (spatial extent), oligomer size (monomer or dimer or oligomer) and affinity (amounts of monomer, dimer and oligomer) is a challenging biophysical problem. Förster resonance energy transfer and fluorescence fluctuation spectroscopy are powerful tools that are sensitive to proximity and oligomerization respectively. Here it is proposed to combine image-based lifetime-detected Forster resonance energy transfer with image correlation spectroscopy and photobleaching to determine distances, oligomer sizes and oligomer distributions. Simulations for simple oligomeric forms illustrate the potential to improve the discrimination between different quaternary states in the cellular milieu.

Hierarchical Self-Assembly of Capsule-Shaped Zirconium Coordination Cages with Quaternary Structure.

Biological macromolecules exhibit emergent functions through hierarchical self-assembly, a concept that is extended to design artificial supramolecular assemblies. Here, the first example of breaking the common parallel arrangement of capsule-shaped zirconium coordination cages is reported by constructing the hierarchical porous framework ZrR-1. ZrR-1 adopts a quaternary structure resembling protein and contains 12-connected chloride clusters, representing the highest connectivity for zirconium-based cages reported thus far. Compared to the parallel framework ZrR-2, ZrR-1 demonstrated enhanced stability in acidic aqueous solutions and a tenfold increase in BET surface area (879 m2  g-1 ). ZrR-1 also exhibits excellent proton conductivity, reaching 1.31 × 10-2 S·cm-1 at 353 K and 98% relative humidity, with a low activation energy of 0.143 eV. This finding provides insights into controlling the hierarchical self-assembly of metal-organic cages to discover superstructures with emergent properties.

Structural studies with crotoxin B from Crotalus durissus collilineatus venom suggest a heterodimeric assembly formed by two new isoforms.

In accidents involving Crotalus snakes, the crotoxin complex (CTX) plays lethal action due to its neurotoxic activity. On the other hand, CTX have potential biotechnological application due to its anti-tumoral, anti-inflammatory, antimicrobial, analgesic and immunomodulatory properties. CTX is a heterodimer composed of Crotoxin A (CA or crotapotin), the acidic nontoxic and non-enzymatic component and; Crotoxin B (CB), a basic, toxic and catalytic PLA2. Currently, there are two classes of CTX isoforms, whose differences in their biological activities have been attributed to features presented in CB isoforms. Here, we present the crystal structure of CB isolated from the Crotalus durissus collilineatus venom. It amino acid sequence was assigned using the SEQUENCE SLIDER software, which revealed that the crystal structure is a heterodimer composed of two new CB isoforms (colCB-A and colCB-B). Bioinformatic and biophysical analyses showed that the toxin forms a tetrameric assembly in solution similar to CB from Crotalus durissus terrificus venom, despite some differences observed at the dimeric interface. By the previously proposed classification, the colCB-B presents features of the class I isoforms while colCB-A cannot be classified into classes I and II based on its amino acid sequence. Due to similar features observed for other CB isoforms found in the NCBI database and the results obtained for colCB-A, we suggest that there are more than two classes of CTX and CB isoforms in crotalic venoms.

Experimental modification in thermal stability of oligomers by alanine substitution and site saturation mutagenesis of interfacial residues.

For certain industrial applications, the stability of protein oligomers is important. In this study, we demonstrated an efficient method to improve the thermal stability of oligomers using the trimeric protein chloramphenicol acetyltransferase (CAT) as the model. We substituted all interfacial residues of CAT with alanine to detect residues critical for oligomer stability. Mutation of six of the forty-nine interfacial residues enhanced oligomer thermal stability. Site saturation mutagenesis was performed on these six residues to optimize the side chains. About 15% of mutations enhanced thermal stability by more than 0.5 °C and most did not disrupt activity of CAT. Certain combinations of mutations further improved thermal stability and resistance against heat treatment. The quadruple mutant, H17V/N34S/F134A/D157C, retained the same activity as the wild-type after heat treatment at 9 °C higher temperature than the wild-type CAT. Furthermore, combinations with only alanine substitutions also improved thermal stability, suggesting the method we developed can be used for rapid modification of industrially important proteins.

Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.

In this work, we investigated the lactate dehydrogenase (LDH) from Selenomonas ruminantium (S. rum), an enzyme that differs at key amino acid positions from canonical allosteric LDHs. The wild type (Wt) of this enzyme recognises pyuvate as all LDHs. However, introducing a single point mutation in the active site loop (I85R) allows S. Rum LDH to recognize the oxaloacetate substrate as a typical malate dehydrogenase (MalDH), whilst maintaining homotropic activation as an LDH. We report the tertiary structure of the Wt and I85RLDH mutant. The Wt S. rum enzyme structure binds NADH and malonate, whilst also resembling the typical compact R-active state of canonical LDHs. The structure of the mutant with I85R was solved in the Apo State (without ligand), and shows no large conformational reorganization such as that observed with canonical allosteric LDHs in Apo state. This is due to a local structural feature typical of S. rum LDH that prevents large-scale conformational reorganization. The S. rum LDH was also studied using Molecular Dynamics simulations, probing specific local deformations of the active site that allow the S. rum LDH to sample the T-inactive state. We propose that, with respect to the LDH/MalDH superfamily, the S. rum enzyme possesses a specificstructural and dynamical way to ensure homotropic activation.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Biophysical tools to study the oligomerization dynamics of Prx1-class peroxiredoxins.

Peroxiredoxins (Prx) are ubiquitous, highly conserved peroxidases whose activity depends on catalytic cysteine residues. The Prx1-class of the peroxiredoxin family, also called typical 2-Cys Prx, organize as head-to-tail homodimers containing two active sites. The peroxidatic cysteine CP of one monomer reacts with the peroxide substrate to form sulfenic acid that reacts with the resolving cysteine (CR) of the adjacent subunit to form an intermolecular disulfide, that is reduced back by the thioredoxin/thioredoxin reductase/NADPH system. Although the minimal catalytic unit is the dimer, these Prx oligomerize into (do)decamers. In addition, these ring-shaped decamers can pile-up into high molecular weight structures. Prx not only display peroxidase activity reducing H2O2, peroxynitrous acid and lipid hydroperoxides (antioxidant enzymes), but also exhibit holdase activity protecting other proteins from unfolding (molecular chaperones). Highly relevant is their participation in redox cellular signaling that is currently under active investigation. The different activities attributed to Prx are strongly ligated to their quaternary structure. In this review, we will describe different biophysical approaches used to characterize the oligomerization dynamics of Prx that include the classical size-exclusion chromatography, analytical ultracentrifugation, calorimetry, and also fluorescence anisotropy and lifetime measurements, as well as mass photometry.

Dual-color Colocalization in Single-molecule Localization Microscopy to Determine the Oligomeric State of Proteins in the Plasma Membrane.

Determining the oligomeric state of membrane proteins is critical for understanding their function. However, traditional ex situ methods like clear native gel electrophoresis can disrupt protein subunit interactions during sample preparation. In situ methods such as stepwise photobleaching have limitations due to high expression levels and limitations of optical resolution in microscopy. Super-resolution microscopy techniques such as single-molecule localization microscopy (SMLM) have the potential to overcome these limitations, but the stochastic nature of signals can lead to miscounting due to over-expression, background noise, and temporal separation of signals. Additionally, this technique has limited application due to the limited selection of fluorescent labels and the demanding control of laser power. To address these issues, we developed a dual color colocalization (DCC) strategy that offers higher tolerance to background noise and simplifies data acquisition and processing for high-throughput and reliable counting. The DCC strategy was used to determine the oligomeric states of membrane proteins of the SLC17 and SLC26 family with SMLM, providing a robust and efficient method for studying protein interactions.

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.

Insight into the role of the Bateman domain at the molecular and physiological levels through engineered IMP dehydrogenases.

Inosine 5'-monophosphate (IMP) dehydrogenase (IMPDH) is an ubiquitous enzyme that catalyzes the NAD+ -dependent oxidation of inosine 5'-monophosphate into xanthosine 5'-monophosphate. This enzyme is formed of two distinct domains, a core domain where the catalytic reaction occurs, and a less-conserved Bateman domain. Our previous studies gave rise to the classification of bacterial IMPDHs into two classes, according to their oligomeric and kinetic properties. MgATP is a common effector but cause to different effects when it binds within the Bateman domain: it is either an allosteric activator for Class I IMPDHs or a modulator of the oligomeric state for Class II IMPDHs. To get insight into the role of the Bateman domain in the dissimilar properties of the two classes, deleted variants of the Bateman domain and chimeras issued from the interchange of the Bateman domain between the three selected IMPDHs have been generated and characterized using an integrative structural biology approach. Biochemical, biophysical, structural, and physiological studies of these variants unveil the Bateman domain as being the carrier of the molecular behaviors of both classes.

Estimation of model accuracy in CASP15 using the ModFOLDdock server.

In CASP15, there was a greater emphasis on multimeric modeling than in previous experiments, with assembly structures nearly doubling in number (41 up from 22) since the previous round. CASP15 also included a new estimation of model accuracy (EMA) category in recognition of the importance of objective quality assessment (QA) for quaternary structure models. ModFOLDdock is a multimeric model QA server developed by the McGuffin group at the University of Reading, which brings together a range of single-model, clustering, and deep learning methods to form a consensus of approaches. For CASP15, three variants of ModFOLDdock were developed to optimize for the different facets of the quality estimation problem. The standard ModFOLDdock variant produced predicted scores optimized for positive linear correlations with the observed scores. The ModFOLDdockR variant produced predicted scores optimized for ranking, that is, the top-ranked models have the highest accuracy. In addition, the ModFOLDdockS variant used a quasi-single model approach to score each model on an individual basis. The scores from all three variants achieved strongly positive Pearson correlation coefficients with the CASP observed scores (oligo-lDDT) in excess of 0.70, which were maintained across both homomeric and heteromeric model populations. In addition, at least one of the ModFOLDdock variants was consistently ranked in the top two methods across all three EMA categories. Specifically, for overall global fold prediction accuracy, ModFOLDdock placed second and ModFOLDdockR placed third; for overall interface quality prediction accuracy, ModFOLDdockR, ModFOLDdock, and ModFOLDdockS were placed above all other predictor methods, and ModFOLDdockR and ModFOLDdockS were placed second and third respectively for individual residue confidence scores. The ModFOLDdock server is available at: https://www.reading.ac.uk/bioinf/ModFOLDdock/. ModFOLDdock is also available as part of the MultiFOLD docker package: https://hub.docker.com/r/mcguffin/multifold.

Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q.

CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q Basic Protocol 2: Exploring conformational diversity in a protein family Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family Basic Protocol 3: Representing conformational diversity in a phylogenetic context.

Understanding a protein fold: The physics, chemistry, and biology of α-helical coiled coils.

Protein science is being transformed by powerful computational methods for structure prediction and design: AlphaFold2 can predict many natural protein structures from sequence, and other AI methods are enabling the de novo design of new structures. This raises a question: how much do we understand the underlying sequence-to-structure/function relationships being captured by these methods? This perspective presents our current understanding of one class of protein assembly, the α-helical coiled coils. At first sight, these are straightforward: sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)n, direct the folding and assembly of amphipathic α helices into bundles. However, many different bundles are possible: they can have two or more helices (different oligomers); the helices can have parallel, antiparallel, or mixed arrangements (different topologies); and the helical sequences can be the same (homomers) or different (heteromers). Thus, sequence-to-structure relationships must be present within the hpphppp repeats to distinguish these states. I discuss the current understanding of this problem at three levels: first, physics gives a parametric framework to generate the many possible coiled-coil backbone structures. Second, chemistry provides a means to explore and deliver sequence-to-structure relationships. Third, biology shows how coiled coils are adapted and functionalized in nature, inspiring applications of coiled coils in synthetic biology. I argue that the chemistry is largely understood; the physics is partly solved, though the considerable challenge of predicting even relative stabilities of different coiled-coil states remains; but there is much more to explore in the biology and synthetic biology of coiled coils.