Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair

Limiting the Broadcast Range of a Secreting Cell during Intercellular Signaling Using Protease-Mediated Degradation

Synthetic biology is revolutionizing our approaches to biocomputing, diagnostics, and environmental monitoring through the use of designed genetic circuits that perform a function within a single cell. More complex functions can be performed by multiple cells that coordinate as they perform different subtasks. Cell-cell communication using molecular signals is particularly suited for aiding in this communication, but the number of molecules that can be used in different communication channels is limited. Here we investigate how proteases can limit the broadcast range of communicating cells. We find that adding barrierpepsin to Saccharomyces cerevisiae cells in two-dimensional multicellular networks that use α-factor signaling prevents cells beyond a specific radius from responding to α-factor signals. Such limiting of the broadcast range of cells could allow multiple cells to use the same signaling molecules to direct different communication processes and functions, provided that they are far enough from one another. These results suggest a means by which complex synthetic cellular networks using only a few signals for communication could be created by structuring a community of cells to create distinct broadcast environments.

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

De novo protein design-From new structures to programmable functions

Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.

Enabling Systemic Identification and Functionality Profiling for Cdc42 Homeostatic Modulators

Homeostatic modulation is pivotal in modern therapeutics. However, the discovery of bioactive materials to achieve this functionality is often random and unpredictive. Here, we enabled a systemic identification and functional classification of chemicals that elicit homeostatic modulation of signaling through Cdc42, a classical small GTPase of Ras superfamily. Rationally designed for high throughput screening, the capture of homeostatic modulators (HMs) along with molecular re-docking uncovered at least five functionally distinct classes of small molecules. This enabling led to partial agonists, hormetic agonists, bona fide activators and inhibitors, and ligand-enhanced agonists. Novel HMs exerted striking functionality in bradykinin-Cdc42 activation of actin remodelingand modified Alzheimer's disease-like behavior in mouse model. This concurrent computer-aided and experimentally empowered HM profiling highlights a model path for predicting HM landscape.

One Sentence Summary

With concurrent experimental biochemical profiling and in silico computer-aided drug discovery (CADD) analysis, this study enabled a systemic identification and holistic classification of Cdc42 homeostatic modulators (HMs) and demonstrated the power of CADD to predict HM classes that can mimic the pharmacological functionality of interests.

Introduction

Maintainingbody homeostasisis the ultimate keyto health. Thereare rich resources of bioactive materials for this functionality from both natural and synthetic chemical repertories including partial agonists (PAs) and various allosteric modulators. These homeostatic modulators (HMs) play a unique role in modern therapeutics for human diseases such as mental disorders and drug addiction. Buspirone, for example, acts as a PA for serotonin 5-HT 1A receptor but is an antagonist of the dopamine D 2 receptor. Such medical useto treat general anxietydisorders (GADs) has become one of the most-commonly prescribed medications. However, most HMs in current uses target membrane proteins and are often derived from random discoveries. HMs as therapeutics targeting cytoplasmic proteins are even more rare despite that they are in paramount needs (e. g. targeting Ras superfamily small GTPases).

Rationale

Cdc42, a classical member of small GTPases of Ras superfamily, regulates PI3K-AKT and Raf-MEK-ERK pathways and has been implicated in various neuropsychiatric and mental disorders as well as addictive diseases and cancer. We previously reported the high-throughput in-silico screening followed by biological characterization of novel small molecule modulators (SMMs) of Cdc42-intersectin (ITSN) protein-protein interactions (PPIs). Based on a serendipitously discovered SMM ZCL278 with PA profile as a model compound, we hypothesized that there are more varieties of such HMs of Cdc42 signaling, and the model HMs can be defined by their distinct Cdc42-ITSN binding mechanisms using computer-aided drug discovery (CADD) analysis. We further reasoned that molecular modeling coupled with experimental profiling can predict HM spectrum and thus open the door for the holistic identification and classification of multifunctional cytoplasmic target-dependent HMs as therapeutics.

Results

The originally discovered Cdc42 inhibitor ZCL278 displaying PA properties prompted the inquiry whether this finding represented a random encounter of PAs or whether biologically significant PAs can be widely present. The top ranked compounds were initially defined by structural fitness and binding scores to Cdc42. Because higher binding scores do not necessarily translate to higher functionality, we performed exhaustive experimentations with over 2,500 independent Cdc42-GEF (guanine nucleotide exchange factor) assays to profile the GTP loading activities on all 44 top ranked compounds derived from the SMM library. The N-MAR-GTP fluorophore-based Cdc42-GEF assay platform provided the first glimpse of the breadth of HMs. A spectrum of Cdc42 HMs was uncovered that can be categorized into five functionally distinct classes: Class I-partial competitive agonists, Class II-hormetic agonists, Class III- bona fide inhibitors (or inverse agonists), Class IV- bona fide activators or agonists, and Class V-ligand-enhanced agonists. Remarkably, model HMs such as ZCL278, ZCL279, and ZCL367 elicited striking biological functionality in bradykinin-Cdc42 activation of actin remodeling and modified Alzheimer's disease (AD)-like behavior in mouse model. Concurrently, we applied Schrödinger-enabled analyses to perform CADD predicted classification of Cdc42 HMs. We modified the classic molecular docking to instill a preferential binding pocket order (PBPO) of Cdc42-ITSN, which was based on the five binding pockets in interface of Cdc42-ITSN. We additionally applied a structure-based pharmacophore hypothesis generation for the model compounds. Then, using Schrödinger's Phase Shape, 3D ligand alignments assigned HMs to Class I, II, III, IV, and V compounds. In this HM library compounds, PBPO, matching pharmacophoric featuring, and shape alignment, all put ZCL993 in Class II compound category, which was confirmed in the Cdc42-GEF assay.

Conclusion

HMs can target diseased cells or tissues while minimizing impacts on tissues that are unaffected. Using Cdc42 HM model compounds as a steppingstone, GTPase activation-based screening of SMM library uncovered five functionally distinct Cdc42 HM classes among which novel efficacies towards alleviating dysregulated AD-like features in mice were identified. Furthermore, molecular re-docking of HM model compounds led to the concept of PBPO. The CADD analysis with PBPO revealed similar profile in a color-coded spectrum to these five distinct classes of Cdc42 HMs identified by biochemical functionality-based screening. The current study enabled a systemic identification and holistic classification of Cdc42 HMs and demonstrated the power of CADD to predict an HM category that can mimic the pharmacological functionality of interests. With artificial intelligence/machine learning (AI/ML) on the horizon to mirror experimental pharmacological discovery like AlphaFold for protein structure prediction, our study highlights a model path to actively capture and profile HMs in potentially any PPI landscape.

Graphic Abstract

Identification and functional classification of Cdc42 homeostatic modulators HMs

Using Cdc42 HM model compounds as reference, GTPase activation-based screening of compound libraries uncovered five functionally distinct Cdc42 HM classes. HMs showed novel efficacies towards alleviating dysregulated Alzheimer's disease (AD)-like behavioral and molecular deficits. In parallel, molecular re-docking of HM model compounds established their preferential binding pocket orders (PBPO). PBPO-based profiling (Red reflects the most, whereas green reflects the least, preferable binding pocket) revealed trends of similar pattern to the five classes from the functionality-based classification.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Structural basis for selective AMPylation of Rac-subfamily GTPases by Bartonella effector protein 1 (Bep1)

Mammalian cells regulate diverse cellular processes in response to extracellular cues. Small GTPases of the Rho family act as molecular switches to rapidly regulate discrete cellular activities, such as cytoskeletal dynamics, cell movement, and innate immune responses. Numerous bacterial virulence factors modulate the function of Rho-family GTPases and thereby manipulate intracellular signaling. For many of these virulence factors we have gained detailed understanding how they covalently modify individual Rho-family GTPases to reprogram their activities; however, their mechanisms of selective targeting of distinct subsets of Rho-family GTPases remained elusive. Using a combination of structural biology and biochemistry, we demonstrate for the effector protein Bep1 exclusive specificity for Rac-subfamily GTPases and propose the underlying mechanism of target selectivity.

Small GTPases of the Ras-homology (Rho) family are conserved molecular switches that control fundamental cellular activities in eukaryotic cells. As such, they are targeted by numerous bacterial toxins and effector proteins, which have been intensively investigated regarding their biochemical activities and discrete target spectra; however, the molecular mechanism of target selectivity has remained largely elusive. Here we report a bacterial effector protein that selectively targets members of the Rac subfamily in the Rho family of small GTPases but none in the closely related Cdc42 or RhoA subfamilies. This exquisite target selectivity of the FIC domain AMP-transferase Bep1 from Bartonella rochalimae is based on electrostatic interactions with a subfamily-specific pair of residues in the nucleotide-binding G4 motif and the Rho insert helix. Residue substitutions at the identified positions in Cdc42 enable modification by Bep1, while corresponding Cdc42-like substitutions in Rac1 greatly diminish modification. Our study establishes a structural understanding of target selectivity toward Rac-subfamily GTPases and provides a highly selective tool for their functional analysis.

Programmable protein circuit design

A fundamental challenge in synthetic biology is to create molecular circuits that can program complex cellular functions. Because proteins can bind, cleave, and chemically modify one another and interface directly and rapidly with endogenous pathways, they could extend the capabilities of synthetic circuits beyond what is possible with gene regulation alone. However, the very diversity that makes proteins so powerful also complicates efforts to harness them as well-controlled synthetic circuit components. Recent work has begun to address this challenge, focusing on principles such as orthogonality and composability that permit construction of diverse circuit-level functions from a limited set of engineered protein components. These approaches are now enabling the engineering of circuits that can sense, transmit, and process information; dynamically control cellular behaviors; and enable new therapeutic strategies, establishing a powerful paradigm for programming biology.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Gαs directly drives PDZ-RhoGEF signaling to Cdc42

Gα proteins promote dynamic adjustments of cell shape directed by actin-cytoskeleton reorganization via their respective RhoGEF effectors. For example, Gα13 binding to the RGS-homology (RH) domains of several RH-RhoGEFs allosterically activates these proteins, causing them to expose their catalytic Dbl-homology (DH)/pleckstrin-homology (PH) regions, which triggers downstream signals. However, whether additional Gα proteins might directly regulate the RH-RhoGEFs was not known. To explore this question, we first examined the morphological effects of expressing shortened RH-RhoGEF DH/PH constructs of p115RhoGEF/ARHGEF1, PDZ-RhoGEF (PRG)/ARHGEF11, and LARG/ARHGEF12. As expected, the three constructs promoted cell contraction and activated RhoA, known to be downstream of Gα13 Intriguingly, PRG DH/PH also induced filopodia-like cell protrusions and activated Cdc42. This pathway was stimulated by constitutively active Gαs (GαsQ227L), which enabled endogenous PRG to gain affinity for Cdc42. A chemogenetic approach revealed that signaling by Gs-coupled receptors, but not by those coupled to Gi or Gq, enabled PRG to bind Cdc42. This receptor-dependent effect, as well as CREB phosphorylation, was blocked by a construct derived from the PRG:Gαs-binding region, PRG-linker. Active Gαs interacted with isolated PRG DH and PH domains and their linker. In addition, this construct interfered with GαsQ227L's ability to guide PRG's interaction with Cdc42. Endogenous Gs-coupled prostaglandin receptors stimulated PRG binding to membrane fractions and activated signaling to PKA, and this canonical endogenous pathway was attenuated by PRG-linker. Altogether, our results demonstrate that active Gαs can recognize PRG as a novel effector directing its DH/PH catalytic module to gain affinity for Cdc42.

SAXS studies of X-ray induced disulfide bond damage: Engineering high-resolution insight from a low-resolution technique

A significant problem in biological X-ray crystallography is the radiation chemistry caused by the incident X-ray beam. This produces both global and site-specific damage. Site specific damage can misdirect the biological interpretation of the structural models produced. Cryo-cooling crystals has been successful in mitigating damage but not eliminating it altogether; however, cryo-cooling can be difficult in some cases and has also been shown to limit functionally relevant protein conformations. The doses used for X-ray crystallography are typically in the kilo-gray to mega-gray range. While disulfide bonds are among the most significantly affected species in proteins in the crystalline state at both cryogenic and higher temperatures, there is limited information on their response to low X-ray doses in solution, the details of which might inform biomedical applications of X-rays. In this work we engineered a protein that dimerizes through a susceptible disulfide bond to relate the radiation damage processes seen in cryo-cooled crystals to those closer to physiologic conditions. This approach enables a low-resolution technique, small angle X-ray scattering (SAXS), to detect and monitor a residue specific process. A dose dependent fragmentation of the engineered protein was seen that can be explained by a dimer to monomer transition through disulfide bond cleavage. This supports the crystallographically derived mechanism and demonstrates that results obtained crystallographically can be usefully extrapolated to physiologic conditions. Fragmentation was influenced by pH and the conformation of the dimer, providing information on mechanism and pointing to future routes for investigation and potential mitigation. The novel engineered protein approach to generate a large-scale change through a site-specific interaction represents a promising tool for advancing radiation damage studies under solution conditions.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Advances in protein structure prediction and design

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

Emerging technologies in protein interface engineering for biomedical applications

Protein interactions communicate critical information from the environment into cells to orchestrate functional responses relevant to health and disease. Whereas the natural repertoire of protein interfaces is finite, biomolecular engineering tools provide access to an unlimited scope of potential interactions that can be custom-designed for affinity, specificity, mechanism, or other properties of interest. This review highlights recent developments in protein interface engineering that offer insight into human physiology to inform the design of new pharmaceuticals, with a particular focus on immunotherapeutics. We cover three innovative and translationally promising approaches: (1) reprogramming receptor oligomerization to manipulate signaling pathways; (2) computational protein interface design strategies; and (3) engineering bioorthogonal protein interaction networks.

Ultrahigh specificity in a network of computationally designed protein-interaction pairs

Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity.

The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.

Computational design of chemogenetic and optogenetic split proteins

Controlling protein activity with chemogenetics and optogenetics has proven to be powerful for testing hypotheses regarding protein function in rapid biological processes. Controlling proteins by splitting them and then rescuing their activity through inducible reassembly offers great potential to control diverse protein activities. Building split proteins has been difficult due to spontaneous assembly, difficulty in identifying appropriate split sites, and inefficient induction of effective reassembly. Here we present an automated approach to design effective split proteins regulated by a ligand or by light (SPELL). We develop a scoring function together with an engineered domain to enable reassembly of protein halves with high efficiency and with reduced spontaneous assembly. We demonstrate SPELL by applying it to proteins of various shapes and sizes in living cells. The SPELL server (spell.dokhlab.org) offers an automated prediction of split sites.

Designing split protein approaches is time consuming and often results in high background activity due to spontaneous assembly. Here the authors present an automated approach which uses a split energy scoring function to identify optimal protein split sites and reduces spontaneous assembly.

Computational design of orthogonal membrane receptor-effector switches for rewiring signaling pathways

Membrane receptors regulate numerous intracellular functions. However, the molecular underpinnings remain poorly understood because most receptors initiate multiple signaling pathways through distinct interaction interfaces that are structurally uncharacterized. We present an integrated computational and experimental approach to model and rationally engineer membrane receptor-intracellular protein systems signaling with novel pathway selectivity. We targeted the dopamine D2 receptor (D2), a G-protein-coupled receptor (GPCR), which primarily signals through Gi, but triggers also the Gq and beta-arrestin pathways. Using this approach, we designed orthogonal D2-Gi complexes, which coupled with high specificity and triggered exclusively the Gi-dependent signaling pathway. We also engineered an orthogonal chimeric D2-Gs/i complex that rewired D2 signaling from a Gi-mediated inhibitory into a Gs-dependent activating pathway. Reinterpreting the evolutionary history of GPCRs in light of the designed proteins, we uncovered an unforeseen hierarchical code of GPCR-G-protein coupling selectivity determinants. The results demonstrate that membrane receptor-cytosolic protein systems can be rationally engineered to regulate mammalian cellular functions. The method should prove useful for creating orthogonal molecular switches that redirect signals at the cell surface for cell-engineering applications.

Selective targeting of engineered T cells using orthogonal IL-2 cytokine-receptor complexes

Interleukin-2 (IL-2) is a cytokine required for effector T cell expansion, survival, and function, especially for engineered T cells in adoptive cell immunotherapy, but its pleiotropy leads to simultaneous stimulation and suppression of immune responses as well as systemic toxicity, limiting its therapeutic use. We engineered IL-2 cytokine-receptor orthogonal (ortho) pairs that interact with one another, transmitting native IL-2 signals, but do not interact with their natural cytokine and receptor counterparts. Introduction of orthoIL-2Rβ into T cells enabled the selective cellular targeting of orthoIL-2 to engineered CD4⁺ and CD8⁺ T cells in vitro and in vivo, with limited off-target effects and negligible toxicity. OrthoIL-2 pairs were efficacious in a preclinical mouse cancer model of adoptive cell therapy and may therefore represent a synthetic approach to achieving selective potentiation of engineered cells.

Computational protein design-the next generation tool to expand synthetic biology applications

One powerful approach to engineer synthetic biology pathways is the assembly of proteins sourced from one or more natural organisms. However, synthetic pathways often require custom functions or biophysical properties not displayed by natural proteins, limitations that could be overcome through modern protein engineering techniques. Structure-based computational protein design is a powerful tool to engineer new functional capabilities in proteins, and it is beginning to have a profound impact in synthetic biology. Here, we review efforts to increase the capabilities of synthetic biology using computational protein design. We focus primarily on computationally designed proteins not only validated in vitro, but also shown to modulate different activities in living cells. Efforts made to validate computational designs in cells can illustrate both the challenges and opportunities in the intersection of protein design and synthetic biology. We also highlight protein design approaches, which although not validated as conveyors of new cellular function in situ, may have rapid and innovative applications in synthetic biology. We foresee that in the near-future, computational protein design will vastly expand the functional capabilities of synthetic cells.

Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation

Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.

Reprogramming cellular functions with engineered membrane proteins

Taking inspiration from Nature, synthetic biology utilizes and modifies biological components to expand the range of biological functions for engineering new practical devices and therapeutics. While early breakthroughs mainly concerned the design of gene circuits, recent efforts have focused on engineering signaling pathways to reprogram cellular functions. Since signal transduction across cell membranes initiates and controls intracellular signaling, membrane receptors have been targeted by diverse protein engineering approaches despite limited mechanistic understanding of their function. The modular architecture of several receptor families has enabled the empirical construction of chimeric receptors combining domains from distinct native receptors which have found successful immunotherapeutic applications. Meanwhile, progress in membrane protein structure determination, computational modeling and rational design promise to foster the engineering of a broader range of membrane receptor functions. Marrying empirical and rational membrane protein engineering approaches should enable the reprogramming of cells with widely diverse fine-tuned functions.

Multistate Computational Protein Design with Backbone Ensembles

The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Flexible Backbone Methods for Predicting and Designing Peptide Specificity

Protein-protein interactions play critical roles in essentially every cellular process. These interactions are often mediated by protein interaction domains that enable proteins to recognize their interaction partners, often by binding to short peptide motifs. For example, PDZ domains, which are among the most common protein interaction domains in the human proteome, recognize specific linear peptide sequences that are often at the C-terminus of other proteins. Determining the set of peptide sequences that a protein interaction domain binds, or it's "peptide specificity," is crucial for understanding its cellular function, and predicting how mutations impact peptide specificity is important for elucidating the mechanisms underlying human diseases. Moreover, engineering novel cellular functions for synthetic biology applications, such as the biosynthesis of biofuels or drugs, requires the design of protein interaction specificity to avoid crosstalk with native metabolic and signaling pathways. The ability to accurately predict and design protein-peptide interaction specificity is therefore critical for understanding and engineering biological function. One approach that has recently been employed toward accomplishing this goal is computational protein design. This chapter provides an overview of recent methodological advances in computational protein design and highlights examples of how these advances can enable increased accuracy in predicting and designing peptide specificity.

Mammalian synthetic biology for studying the cell

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology.

Rational design of TNFα binding proteins based on the de novo designed protein DS119

De novo protein design offers templates for engineering tailor-made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home-made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1-4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence-structure-function studies.

Discovery of Covalent Ligands via Noncovalent Docking by Dissecting Covalent Docking Based on a "Steric-Clashes Alleviating Receptor (SCAR)" Strategy

Covalent ligands modulating protein activities/signals have attracted unprecedented attention in recent years, but the insufficient understanding of their advantages in the early days of drug discovery has hindered their rational discovery and development. This also left us inadequate knowledge on the rational design of covalent ligands, e.g., how to balance the contribution from the covalent group and the noncovalent group, respectively. In this work, we dissected the noncovalent docking from covalent docking by creating SCARs (steric-clashes alleviating receptors). We showed that the SCAR method outperformed those specifically developed but more complicated covalent docking protocols. We furthermore provided a "proof-of-principle" example by implementing this method in the first high-throughput screening and discovery of novel covalent inhibitors of S-adenosylmethionine decarboxylase. This work demonstrated that noncovalent groups play a predeterminate role in the design of covalent ligands, and would be of great value in accelerating the discovery and development of covalent ligands.

Structural Mechanisms and Drug Discovery Prospects of Rho GTPases

Rho GTPases regulate cellular morphology and dynamics, and some are key drivers of cancer progression. This superfamily offers attractive potential targets for therapeutic intervention, with RhoA, Rac1 and Cdc42 being prime examples. The challenges in developing agents that act on these signaling enzymes include the lack of obvious druggable pockets and their membrane-bound activities. However, progress in targeting the similar Ras protein is illuminating new strategies for specifically inhibiting oncogenic GTPases. The structures of multiple signaling and regulatory states of Rho proteins have been determined, and the post-translational modifications including acylation and phosphorylation points have been mapped and their functional effects examined. The development of inhibitors to probe the significance of overexpression and mutational hyperactivation of these GTPases underscores their importance in cancer progression. The ability to integrate in silico, in vitro, and in vivo investigations of drug-like molecules indicates the growing tractability of GTPase systems for lead optimization. Although no Rho-targeted drug molecules have yet been clinically approved, this family is clearly showing increasing promise for the development of precision medicine and combination cancer therapies.

A morphospace for synthetic organs and organoids: the possible and the actual

Efforts in evolutionary developmental biology have shed light on how organs are developed and why evolution has selected some structures instead of others. These advances in the understanding of organogenesis along with the most recent techniques of organotypic cultures, tissue bioprinting and synthetic biology provide the tools to hack the physical and genetic constraints in organ development, thus opening new avenues for research in the form of completely designed or merely altered settings. Here we propose a unifying framework that connects the concept of morphospace (i.e. the space of possible structures) with synthetic biology and tissue engineering. We aim for a synthesis that incorporates our understanding of both evolutionary and architectural constraints and can be used as a guide for exploring alternative design principles to build artificial organs and organoids. We present a three-dimensional morphospace incorporating three key features associated to organ and organoid complexity. The axes of this space include the degree of complexity introduced by developmental mechanisms required to build the structure, its potential to store and react to information and the underlying physical state. We suggest that a large fraction of this space is empty, and that the void might offer clues for alternative ways of designing and even inventing new organs.

Synthetic mechanobiology: engineering cellular force generation and signaling

Mechanobiology seeks to understand and control mechanical and related biophysical communication between cells and their surroundings. While experimental efforts in this field have traditionally emphasized manipulation of the extracellular force environment, a new suite of approaches has recently emerged in which cell phenotype and signaling are controlled by directly engineering the cell itself. One route is to control cell behavior by modulating gene expression using conditional promoters. Alternatively, protein activity can be actuated directly using synthetic protein ligands, chemically induced protein dimerization, optogenetic strategies, or functionalized magnetic nanoparticles. Proof-of-principle studies are already demonstrating the translational potential of these approaches, and future technological development will permit increasingly precise control over cell mechanobiology and improve our understanding of the underlying signaling events.

Protein backbone ensemble generation explores the local structural space of unseen natural homologs

MOTIVATION

Mutations in homologous proteins affect changes in the backbone conformation that involve a complex interplay of forces which are difficult to predict. Protein design algorithms need to anticipate these backbone changes in order to accurately calculate the energy of the structure given an amino acid sequence, without knowledge of the final, designed sequence. This is related to the problem of predicting small changes in the backbone between highly similar sequences.

RESULTS

We explored the ability of the Rosetta suite of protein design tools to move the backbone from its position in one structure (template) to its position in a close homologous structure (target) as a function of the diversity of a backbone ensemble constructed using the template structure, the percent sequence identity between the template and target, and the size of local zone being considered in the ensemble. We describe a pareto front in the likelihood of moving the backbone toward the target as a function of ensemble diversity and zone size. The equations and protocols presented here will be useful for protein design.

AVAILABILITY AND IMPLEMENTATION

PyRosetta scripts available at www.bioinfo.rpi.edu/bystrc/downloads.html#ensemble

CONTACT

bystrc@rpi.edu.

Endothelial RhoGEFs: A systematic analysis of their expression profiles in VEGF-stimulated and tumor endothelial cells

Rho guanine nucleotide exchange factors (RhoGEFs) integrate cell signaling inputs into morphological and functional responses. However, little is known about the endothelial repertoire of RhoGEFs and their regulation. Thus, we assessed the expression of 81 RhoGEFs (70 homologous to Dbl and 11 of the DOCK family) in endothelial cells. Further, in the case of DH-RhoGEFs, we also determined their responses to VEGF exposure in vitro and in the context of tumors. A phylogenetic analysis revealed the existence of four groups of DH-RhoGEFs and two of the DOCK family. Among them, we found that the most abundant endothelial RhoGEFs were: Tuba, FGD5, Farp1, ARHGEF17, TRIO, P-Rex1, ARHGEF15, ARHGEF11, ABR, Farp2, ARHGEF40, ALS, DOCK1, DOCK7 and DOCK6. Expression of RASGRF2 and PREX2 increased significantly in response to VEGF, but most other RhoGEFs were unaffected. Interestingly murine endothelial cells isolated from tumors showed that all four phylogenetic subgroups of DH-RhoGEFs were altered when compared to non-tumor endothelial cells. In summary, our results provide a detailed assessment of RhoGEFs expression profiles in the endothelium and set the basis to systematically address their regulation in vascular signaling.

Protein engineering: a new frontier for biological therapeutics

Protein engineering holds the potential to transform the metabolic drug landscape through the development of smart, stimulusresponsive drug systems. Protein therapeutics are a rapidly expanding segment of Food and Drug Administration approved drugs that will improve clinical outcomes over the long run. Engineering of protein therapeutics is still in its infancy, but recent general advances in protein engineering capabilities are being leveraged to yield improved control over both pharmacokinetics and pharmacodynamics. Stimulus- responsive protein therapeutics are drugs which have been designed to be metabolized under targeted conditions. Protein engineering is being utilized to develop tailored smart therapeutics with biochemical logic. This review focuses on applications of targeted drug neutralization, stimulus-responsive engineered protein prodrugs, and emerging multicomponent smart drug systems (e.g., antibody-drug conjugates, responsive engineered zymogens, prospective biochemical logic smart drug systems, drug buffers, and network medicine applications).

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

Designing new protein–ligand interactions has tremendous potential for engineering sensitive biosensors for diagnostics or new enzymes useful in biotechnology, but these applications are extremely challenging, both because of inaccuracies of the energy functions used in modeling and design, and because protein active and binding sites are highly sensitive to subtle changes in structure. Here we describe a new method that addresses the second problem and couples changes in the structure of the protein backbone and of the amino acid side chains, the amino acid sequence, and the conformation of the ligand and its orientation in the binding site. We show that our method improvements significantly increase the accuracy of designing protein–ligand interactions compared to current state-of-the-art design methods. We assess these improvements in two important tests: the first predicts mutations that change ligand-binding preferences in enzymes, and the second predicts protein sequences that bind a given ligand. In these tests, subtle conformational changes made in our model are essential to recapitulate both the results from engineering experiments and the sequence diversity occurring in natural protein families. These results therefore shed light on the mechanisms of how new protein functions might have emerged and can be engineered in the laboratory.

Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences

Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

Towards the computational design of protein post-translational regulation

Protein post-translational modifications (PTMs) are a fast and versatility mechanism used by the cell to regulate the function of proteins in response to changing conditions. PTMs can alter the activity of proteins by allosteric regulation or by controlling protein interactions, localization and abundance. Recent advances in proteomics have revealed the extent of regulation by PTMs and the different mechanisms used in nature to exert control over protein function via PTMs. These developments can serve as the foundation for the rational design of protein regulation. Here we review the advances in methods to determine the function of PTMs, protein allosteric control and examples of rational design of PTM regulation. These advances create an opportunity to move synthetic biology forward by making use of a level of regulation that is of yet unexplored.

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.

Discovery of substrates for a SET domain lysine methyltransferase predicted by multistate computational protein design

Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM]-1-K(∗)-[AFYMSHRK]+1-[LYK]+2 around the target lysine K(∗). Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes.

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.

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

Reengineering protein-protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of "second-site suppressors," where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein-protein interfaces. To extend this approach, it would be advantageous to be able to "transplant" existing engineered and experimentally validated specificity changes to other homologous protein-protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain-peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein-protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. Although the context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein-protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

Synthetic biology in mammalian cells: next generation research tools and therapeutics

Recent progress in DNA manipulation and gene circuit engineering has greatly improved our ability to programme and probe mammalian cell behaviour. These advances have led to a new generation of synthetic biology research tools and potential therapeutic applications. Programmable DNA-binding domains and RNA regulators are leading to unprecedented control of gene expression and elucidation of gene function. Rebuilding complex biological circuits such as T cell receptor signalling in isolation from their natural context has deepened our understanding of network motifs and signalling pathways. Synthetic biology is also leading to innovative therapeutic interventions based on cell-based therapies, protein drugs, vaccines and gene therapies.

Disentangling biological signaling networks by dynamic coupling of signaling lipids to modifying enzymes

An unresolved problem in biological signal transduction is how particular branches of highly interconnected signaling networks can be decoupled, allowing activation of specific circuits within complex signaling architectures. Although signaling dynamics and spatiotemporal mechanisms serve critical roles, it remains unclear if these are the only ways cells achieve specificity within networks. The transcription factor Steroidogenic Factor-1 (SF-1) is an excellent model to address this question, as it forms dynamic complexes with several chemically distinct lipid species (phosphatidylinositols, phosphatidylcholines and sphingolipids). This property is important since lipids bound to SF-1 are modified by lipid signaling enzymes (IPMK & PTEN), regulating SF-1 biological activity in gene expression. Thus, a particular SF-1/lipid complex can interface with a lipid signaling enzyme only if SF-1 has been loaded with a chemically compatible lipid substrate. This mechanism permits dynamic downstream responsiveness to constant upstream input, disentangling specific pathways from the full network. The potential of this paradigm to apply generally to nuclear lipid signaling is discussed, with particular attention given to the nuclear receptor superfamily of transcription factors and their phospholipid ligands.

Computational protein design quantifies structural constraints on amino acid covariation

Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations.

Proteins generally fold into specific three-dimensional structures to perform their cellular functions, and the presence of misfolded proteins is often deleterious for cellular and organismal fitness. For these reasons, maintenance of protein structure is thought to be one of the major fitness pressures acting on proteins. Consequently, the sequences of today's naturally occurring proteins contain signatures reflecting the constraints imposed by protein structure. Here we test the ability of computational protein design methods to recapitulate and explain these signatures. We focus on the physical basis of evolutionary pressures that act on interactions between amino acids in folded proteins, which are critical in determining protein structure and function. Such pressures can be observed from the appearance of amino acid covariation, where the amino acids at certain positions in protein sequences are correlated with each other. We find similar patterns of amino acid covariation in natural sequences and sequences optimized for their structures using computational protein design, demonstrating the importance of structural constraints in protein molecular evolution and providing insights into the structural mechanisms leading to covariation. In addition, these results characterize the ability of computational methods to model the precise details of correlated amino acid changes, which is critical for engineering new proteins with useful functions beyond those seen in nature.