Spatial Multiplexing of Fluorescent Reporters for Imaging Signaling Network Dynamics.

In order to analyze how a signal transduction network converts cellular inputs into cellular outputs, ideally one would measure the dynamics of many signals within the network simultaneously. We found that, by fusing a fluorescent reporter to a pair of self-assembling peptides, it could be stably clustered within cells at random points, distant enough to be resolved by a microscope but close enough to spatially sample the relevant biology. Because such clusters, which we call signaling reporter islands (SiRIs), can be modularly designed, they permit a set of fluorescent reporters to be efficiently adapted for simultaneous measurement of multiple nodes of a signal transduction network within single cells. We created SiRIs for indicators of second messengers and kinases and used them, in hippocampal neurons in culture and intact brain slices, to discover relationships between the speed of calcium signaling, and the amplitude of PKA signaling, upon receiving a cAMP-driving stimulus.

Marginal specificity in protein interactions constrains evolution of a paralogous family.

The evolution of novel functions in biology relies heavily on gene duplication and divergence, creating large paralogous protein families. Selective pressure to avoid detrimental cross-talk often results in paralogs that exhibit exquisite specificity for their interaction partners. But how robust or sensitive is this specificity to mutation? Here, using deep mutational scanning, we demonstrate that a paralogous family of bacterial signaling proteins exhibits marginal specificity, such that many individual substitutions give rise to substantial cross-talk between normally insulated pathways. Our results indicate that sequence space is locally crowded despite overall sparseness, and we provide evidence that this crowding has constrained the evolution of bacterial signaling proteins. These findings underscore how evolution selects for "good enough" rather than optimized phenotypes, leading to restrictions on the subsequent evolution of paralogs.

Peptide design by optimization on a data-parameterized protein interaction landscape.

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-xL, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Iterative optimization yields Mcl-1-targeting stapled peptides with selective cytotoxicity to Mcl-1-dependent cancer cells.

Bcl-2 family proteins regulate apoptosis, and aberrant interactions of overexpressed antiapoptotic family members such as Mcl-1 promote cell transformation, cancer survival, and resistance to chemotherapy. Discovering potent and selective Mcl-1 inhibitors that can relieve apoptotic blockades is thus a high priority for cancer research. An attractive strategy for disabling Mcl-1 involves using designer peptides to competitively engage its binding groove, mimicking the structural mechanism of action of native sensitizer BH3-only proteins. We transformed Mcl-1-binding peptides into α-helical, cell-penetrating constructs that are selectively cytotoxic to Mcl-1-dependent cancer cells. Critical to the design of effective inhibitors was our introduction of an all-hydrocarbon cross-link or "staple" that stabilizes α-helical structure, increases target binding affinity, and independently confers binding specificity for Mcl-1 over related Bcl-2 family paralogs. Two crystal structures of complexes at 1.4 Å and 1.9 Å resolution demonstrate how the hydrophobic staple induces an unanticipated structural rearrangement in Mcl-1 upon binding. Systematic sampling of staple location and iterative optimization of peptide sequence in accordance with established design principles provided peptides that target intracellular Mcl-1. This work provides proof of concept for the development of potent, selective, and cell-permeable stapled peptides for therapeutic targeting of Mcl-1 in cancer, applying a design and validation workflow applicable to a host of challenging biomedical targets.

The Orientations of Large Aspect-Ratio Coiled-Coil Proteins Attached to Gold Nanostructures.

Methods for patterning biomolecules on a substrate at the single molecule level have been studied as a route to sensors with single-molecular sensitivity or as a way to probe biological phenomena at the single-molecule level. However, the arrangement and orientation of single biomolecules on substrates has been less investigated. Here, the arrangement and orientation of two rod-like coiled-coil proteins, cortexillin and tropomyosin, around patterned gold nanostructures is examined. The high aspect ratio of the coiled coils makes it possible to study their orientations and to pursue a strategy of protein orientation via two-point attachment. The proteins are anchored to the surfaces using thiol groups, and the number of cysteine residues in tropomyosin is varied to test how this variation affects the structure and arrangement of the surface-attached proteins. Molecular dynamics studies are used to interpret the observed positional distributions. Based on initial studies of protein attachment to gold post structures, two 31-nm-long tropomyosin molecules are aligned between the two sidewalls of a trench with a width of 68 nm. Because the approach presented in this study uses one of twenty natural amino acids, this method provides a convenient way to pattern biomolecules on substrates using standard chemistry.

Data-driven prediction and design of bZIP coiled-coil interactions.

Selective dimerization of the basic-region leucine-zipper (bZIP) transcription factors presents a vivid example of how a high degree of interaction specificity can be achieved within a family of structurally similar proteins. The coiled-coil motif that mediates homo- or hetero-dimerization of the bZIP proteins has been intensively studied, and a variety of methods have been proposed to predict these interactions from sequence data. In this work, we used a large quantitative set of 4,549 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally conserved residue-residue interactions in the coiled-coil motif. Our model, which expresses interaction energies as a sum of interpretable residue-pair and triplet terms, achieves a correlation with experimental binding free energies of R = 0.68 and significantly out-performs other scoring functions. To use our model in protein design applications, we devised a strategy in which synthetic peptides are built by assembling 7-residue native-protein heptad modules into new combinations. An integer linear program was used to find the optimal combination of heptads to bind selectively to a target human bZIP coiled coil, but not to target paralogs. Using this approach, we designed peptides to interact with the bZIP domains from human JUN, XBP1, ATF4 and ATF5. Testing more than 132 candidate protein complexes using a fluorescence resonance energy transfer assay confirmed the formation of tight and selective heterodimers between the designed peptides and their targets. This approach can be used to make inhibitors of native proteins, or to develop novel peptides for applications in synthetic biology or nanotechnology.

Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins.

Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models.

Design of protein-interaction specificity gives selective bZIP-binding peptides.

Interaction specificity is a required feature of biological networks and a necessary characteristic of protein or small-molecule reagents and therapeutics. The ability to alter or inhibit protein interactions selectively would advance basic and applied molecular science. Assessing or modelling interaction specificity requires treating multiple competing complexes, which presents computational and experimental challenges. Here we present a computational framework for designing protein-interaction specificity and use it to identify specific peptide partners for human basic-region leucine zipper (bZIP) transcription factors. Protein microarrays were used to characterize designed, synthetic ligands for all but one of 20 bZIP families. The bZIP proteins share strong sequence and structural similarities and thus are challenging targets to bind specifically. Nevertheless, many of the designs, including examples that bind the oncoproteins c-Jun, c-Fos and c-Maf (also called JUN, FOS and MAF, respectively), were selective for their targets over all 19 other families. Collectively, the designs exhibit a wide range of interaction profiles and demonstrate that human bZIPs have only sparsely sampled the possible interaction space accessible to them. Our computational method provides a way to systematically analyse trade-offs between stability and specificity and is suitable for use with many types of structure-scoring functions; thus, it may prove broadly useful as a tool for protein design.

A set of computationally designed orthogonal antiparallel homodimers that expands the synthetic coiled-coil toolkit.

Molecular engineering of protein assemblies, including the fabrication of nanostructures and synthetic signaling pathways, relies on the availability of modular parts that can be combined to give different structures and functions. Currently, a limited number of well-characterized protein interaction components are available. Coiled-coil interaction modules have been demonstrated to be useful for biomolecular design, and many parallel homodimers and heterodimers are available in the coiled-coil toolkit. In this work, we sought to design a set of orthogonal antiparallel homodimeric coiled coils using a computational approach. There are very few antiparallel homodimers described in the literature, and none have been measured for cross-reactivity. We tested the ability of the distance-dependent statistical potential DFIRE to predict orientation preferences for coiled-coil dimers of known structure. The DFIRE model was then combined with the CLASSY multistate protein design framework to engineer sets of three orthogonal antiparallel homodimeric coiled coils. Experimental measurements confirmed the successful design of three peptides that preferentially formed antiparallel homodimers that, furthermore, did not interact with one additional previously reported antiparallel homodimer. Two designed peptides that formed higher-order structures suggest how future design protocols could be improved. The successful designs represent a significant expansion of the existing protein-interaction toolbox for molecular engineers.

PairK: Pairwise k-mer alignment for quantifying protein motif conservation in disordered regions.

Protein-protein interactions are often mediated by a modular peptide recognition domain binding to a short linear motif (SLiM) in the disordered region of another protein. The ability to predict domain-SLiM interactions would allow researchers to map protein interaction networks, predict the effects of perturbations to those networks, and develop biologically meaningful hypotheses. Unfortunately, sequence database searches for SLiMs generally yield mostly biologically irrelevant motif matches or false positives. To improve the prediction of novel SLiM interactions, researchers employ filters to discriminate between biologically relevant and improbable motif matches. One promising criterion for identifying biologically relevant SLiMs is the sequence conservation of the motif, exploiting the fact that functional motifs are more likely to be conserved than spurious motif matches. However, the difficulty of aligning disordered regions has significantly hampered the utility of this approach. We present PairK (pairwise k-mer alignment), an MSA-free method to quantify motif conservation in disordered regions. PairK outperforms both standard MSA-based conservation scores and a modern LLM-based conservation score predictor on the task of identifying biologically important motif instances. PairK can quantify conservation over wider phylogenetic distances than MSAs, indicating that SLiMs may be more conserved than is implied by MSA-based metrics. PairK is available as open-source code at https://github.com/jacksonh1/pairk.

Elaboration of the Homer1 recognition landscape reveals incomplete divergence of paralogous EVH1 domains.

Short sequences that mediate interactions with modular binding domains are ubiquitous throughout eukaryotic proteomes. Networks of short linear motifs (SLiMs) and their corresponding binding domains orchestrate many cellular processes, and the low mutational barrier to evolving novel interactions provides a way for biological systems to rapidly sample selectable phenotypes. Mapping SLiM binding specificity and the rules that govern SLiM evolution is fundamental to uncovering the pathways regulated by these networks and developing the tools to manipulate them. We used high-throughput screening of the human proteome to identify sequences that bind to the Enabled/VASP homology 1 (EVH1) domain of the postsynaptic density scaffolding protein Homer1. This expanded our understanding of the determinants of Homer EVH1 binding preferences and defined a new motif that can facilitate the discovery of additional Homer-mediated interactions. Interestingly, the Homer1 EVH1 domain preferentially binds to sequences containing an N-terminally overlapping motif that is bound by the paralogous family of Ena/VASP actin polymerases, and many of these sequences can bind to EVH1 domains from both protein families. We provide evidence from orthologous EVH1 domains in pre-metazoan organisms that the overlap in human Ena/VASP and Homer binding preferences corresponds to an incomplete divergence from a common Ena/VASP ancestor. Given this overlap in binding profiles, promiscuous sequences that can be recognized by both families either achieve specificity through extrinsic regulatory strategies or may provide functional benefits via multi-specificity. This may explain why these paralogs incompletely diverged despite the accessibility of further diverged isoforms.

Elaboration of the Homer1 Recognition Landscape Reveals Incomplete Divergence of Paralogous EVH1 Domains.

Short sequences that mediate interactions with modular binding domains are ubiquitous throughout eukaryotic proteomes. Networks of Short Linear Motifs (SLiMs) and their corresponding binding domains orchestrate many cellular processes, and the low mutational barrier to evolving novel interactions provides a way for biological systems to rapidly sample selectable phenotypes. Mapping SLiM binding specificity and the rules that govern SLiM evolution is fundamental to uncovering the pathways regulated by these networks and developing the tools to manipulate them. We used high-throughput screening of the human proteome to identify sequences that bind to the Enabled/VASP homology 1 (EVH1) domain of the postsynaptic density scaffolding protein Homer1. In doing so, we expanded current understanding of the determinants of Homer EVH1 binding preferences and defined a new motif that can facilitate the discovery of additional Homer-mediated interactions. Interestingly, the Homer1 EVH1 domain preferentially binds to sequences containing an N-terminally overlapping motif that is bound by the paralogous family of Ena/VASP actin polymerases, and many of these sequences can bind to EVH1 domains from both protein families. We provide evidence from orthologous EVH1 domains in pre-metazoan organisms that the overlap in human Ena/VASP and Homer binding preferences corresponds to an incomplete divergence from a common Ena/VASP ancestor. Given this overlap in binding profiles, promiscuous sequences that can be recognized by both families either achieve specificity through extrinsic regulatory strategies or may provide functional benefits via multi-specificity. This may explain why these paralogs incompletely diverged despite the accessibility of further diverged isoforms.

High-throughput discovery of inhibitory protein fragments with AlphaFold.

Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind to protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length proteins in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. Comparisons with experimental measurements establish that our approach is a sensitive predictor of fragment function: Evaluating inhibitory fragments from known protein-protein interaction interfaces, we find 87% are predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. Deep mutational scanning experiments support the predicted binding modes and uncover superior inhibitory peptides in high throughput. Further, FragFold is able to predict previously unknown protein binding modes, explaining prior genetic and biochemical data. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.

bZIP transcription factors affecting secondary metabolism, sexual development and stress responses in Aspergillus nidulans.

The eukaryotic basic leucine zipper (bZIP) transcription factors play critical roles in the organismal response to the environment. Recently, a novel YAP-like bZIP, restorer of secondary metabolism A (RsmA), was found in a suppressor screen of an Aspergillus nidulans secondary metabolism (SM) mutant in which overexpression of rsmA was found to partially remediate loss of SM in Velvet Complex mutants. The Velvet Complex is a conserved fungal transcriptional heteromer that couples SM with sexual development in fungi. Here we characterized and contrasted SM in mutants of RsmA and four other A. nidulans bZIP proteins (NapA, ZipA, ZipB and ZipC) with predicted DNA binding motifs similar to RsmA. Only two overexpression mutants exhibited both SM and sexual abnormalities that were noteworthy: OE : : rsmA resulted in a 100-fold increase in sterigmatocystin and a near loss of meiotic spore production. OE : : napA displayed decreased production of sterigmatocystin, emericellin, asperthecin, shamixanthone and epishamixanthone, coupled with a shift from sexual to asexual development. Quantification of bZIP homodimer and heterodimer formation using fluorescence resonance energy transfer (FRET) suggested that these proteins preferentially self-associate.

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.

Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.

Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization.

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

In silico and in vitro elucidation of BH3 binding specificity toward Bcl-2.

Interactions between Bcl-2-like proteins and BH3 domains play a key role in the regulation of apoptosis. Despite the overall structural similarity of their interaction with helical BH3 domains, Bcl-2-like proteins exhibit an intricate spectrum of binding specificities whose underlying basis is not well understood. Here, we characterize these interactions using Rosetta FlexPepBind, a protocol for the prediction of peptide binding specificity that evaluates the binding potential of different peptides based on structural models of the corresponding peptide-receptor complexes. For two prominent players, Bcl-xL and Mcl-1, we obtain good agreement with a large set of experimental SPOT array measurements and recapitulate the binding specificity of peptides derived by yeast display in a previous study. We extend our approach to a third member of this family, Bcl-2: we test our blind prediction of the binding of 180 BIM-derived peptides with a corresponding experimental SPOT array. Both prediction and experiment reveal a Bcl-2 binding specificity pattern that resembles that of Bcl-xL. Finally, we extend this application to accurately predict the specificity pattern of additional human BH3-only derived peptides. This study characterizes the distinct patterns of binding specificity of BH3-only derived peptides for the Bcl-2 like proteins Bcl-xL, Mcl-1, and Bcl-2 and provides insight into the structural basis of determinants of specificity.

The MCL-1 BH3 helix is an exclusive MCL-1 inhibitor and apoptosis sensitizer.

The development of selective inhibitors for discrete anti-apoptotic BCL-2 family proteins implicated in pathologic cell survival remains a formidable but pressing challenge. Such precisely tailored compounds would serve as molecular probes and targeted therapies to study and treat human diseases driven by specific anti-apoptotic blockades. In particular, MCL-1 has emerged as a major resistance factor in human cancer. By screening a library of stabilized alpha-helix of BCL-2 domains (SAHBs), we determined that the MCL-1 BH3 helix is itself a potent and exclusive MCL-1 inhibitor. X-ray crystallography and mutagenesis studies defined key binding and specificity determinants, including the capacity to harness the hydrocarbon staple to optimize affinity while preserving selectivity. MCL-1 SAHB directly targets MCL-1, neutralizes its inhibitory interaction with pro-apoptotic BAK and sensitizes cancer cells to caspase-dependent apoptosis. By leveraging nature's solution to ligand selectivity, we generated an MCL-1-specific agent that defines the structural and functional features of targeted MCL-1 inhibition.

Tertiary motifs as building blocks for the design of protein-binding peptides.

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre-defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface-complementing fragments or "seeds." We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM-based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide-protein complexes can be covered by seeds generated from single-chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide-covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher-quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide-binding site. These results demonstrate that known peptide-binding structures can be constructed from TERMs in single-chain structures and suggest that TERM information can be applied to efficiently design novel target-complementing binders.

Molecular determinants of TRAF6 binding specificity suggest that native interaction partners are not optimized for affinity.

TRAF6 is an adaptor protein involved in signaling pathways that are essential for development and the immune system. It participates in many protein-protein interactions, some of which are mediated by the C-terminal MATH domain, which binds to short peptide segments containing the motif PxExx[FYWHDE], where x is any amino acid. Blocking MATH domain interactions is associated with favorable effects in various disease models. To better define TRAF6 MATH domain binding preferences, we screened a combinatorial library using bacterial cell-surface peptide display. We identified 236 of the best TRAF6-interacting peptides and a set of 1,200 peptides that match the sequence PxE but do not bind TRAF6 MATH. The peptides that were most enriched in the screen bound TRAF6 tighter than previously measured native peptides. To better understand the structural basis for TRAF6 interaction preferences, we built all-atom structural models of the MATH domain in complex with high-affinity binders and nonbinders identified in the screen. We identified favorable interactions for motif features in binders as well as negative design elements distributed across the motif that can disfavor or preclude binding. Searching the human proteome revealed that the most biologically relevant TRAF6 motif matches occupy a different sequence space from the best hits discovered in combinatorial library screening, suggesting that native interactions are not optimized for affinity. Our experimentally determined binding preferences and structural models support the design of peptide-based interaction inhibitors with higher affinities than endogenous TRAF6 ligands.