Computational design of a protein-based enzyme inhibitor

Preclinical proof of principle for orally delivered Th17 antagonist miniproteins

Interleukin (IL)-23 and IL-17 are well-validated therapeutic targets in autoinflammatory diseases. Antibodies targeting IL-23 and IL-17 have shown clinical efficacy but are limited by high costs, safety risks, lack of sustained efficacy, and poor patient convenience as they require parenteral administration. Here, we present designed miniproteins inhibiting IL-23R and IL-17 with antibody-like, low picomolar affinities at a fraction of the molecular size. The minibinders potently block cell signaling in vitro and are extremely stable, enabling oral administration and low-cost manufacturing. The orally administered IL-23R minibinder shows efficacy better than a clinical anti-IL-23 antibody in mouse colitis and has a favorable pharmacokinetics (PK) and biodistribution profile in rats. This work demonstrates that orally administered de novo-designed minibinders can reach a therapeutic target past the gut epithelial barrier. With high potency, gut stability, and straightforward manufacturability, de novo-designed minibinders are a promising modality for oral biologics.

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.

Sequence basis for selectivity of ephrin-B2 ligand for Eph receptors and pathogenic henipavirus G glycoproteins

IMPORTANCE

Ephrin-B2 (EFNB2) is a ligand for six Eph receptors in humans and regulates multiple cell developmental and signaling processes. It also functions as the cell entry receptor for Nipah virus and Hendra virus, zoonotic viruses that can cause respiratory and/or neurological symptoms in humans with high mortality. Here, we investigate the sequence basis of EFNB2 specificity for binding the Nipah virus attachment G glycoprotein over Eph receptors. We then use this information to engineer EFNB2 as a soluble decoy receptor that specifically binds the attachment glycoproteins of the Nipah virus and other related henipaviruses to neutralize infection. These findings further mechanistic understanding of protein selectivity and may facilitate the development of diagnostics or therapeutics against henipavirus infection.

Targeting Ras with protein engineering

Ras proteins are small GTPases that regulate cell growth and division. Mutations in Ras genes are associated with many types of cancer, making them attractive targets for cancer therapy. Despite extensive efforts, targeting Ras proteins with small molecules has been extremely challenging due to Ras's mostly flat surface and lack of small molecule-binding cavities. These challenges were recently overcome by the development of the first covalent small-molecule anti-Ras drug, sotorasib, highlighting the efficacy of Ras inhibition as a therapeutic strategy. However, this drug exclusively inhibits the Ras G12C mutant, which is not a prevalent mutation in most cancer types. Unlike the G12C variant, other Ras oncogenic mutants lack reactive cysteines, rendering them unsuitable for targeting via the same strategy. Protein engineering has emerged as a promising method to target Ras, as engineered proteins have the ability to recognize various surfaces with high affinity and specificity. Over the past few years, scientists have engineered antibodies, natural Ras effectors, and novel binding domains to bind to Ras and counteract its carcinogenic activities via a variety of strategies. These include inhibiting Ras-effector interactions, disrupting Ras dimerization, interrupting Ras nucleotide exchange, stimulating Ras interaction with tumor suppressor genes, and promoting Ras degradation. In parallel, significant advancements have been made in intracellular protein delivery, enabling the delivery of the engineered anti-Ras agents into the cellular cytoplasm. These advances offer a promising path for targeting Ras proteins and other challenging drug targets, opening up new opportunities for drug discovery and development.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

The Sequence Basis for Selectivity of Ephrin-B2 Ligand for Eph Receptors and Pathogenic Henipavirus G Glycoproteins: Selective Ephrin-B2 Decoys for Nipah and Hendra Virus

Ephrin-B2 (EFNB2) is a ligand for six Eph receptors in humans and functions as a cell entry receptor for several henipaviruses including Nipah virus (NiV), a pathogenic zoonotic virus with pandemic potential. To understand the sequence basis of promiscuity for EFNB2 binding to the attachment glycoprotein of NiV (NiV-G) and Eph receptors, we performed deep mutagenesis on EFNB2 to identify mutations that enhance binding to NiV-G over EphB2, one of the highest affinity Eph receptors. The mutations highlight how different EFNB2 conformations are selected by NiV-G versus EphB2. Specificity mutations are enriched at the base of the G-H binding loop of EFNB2, especially surrounding a phenylalanine hinge upon which the G-H loop pivots, and at a phenylalanine hook that rotates away from the EFNB2 core to engage Eph receptors. One EFNB2 mutant, D62Q, possesses pan-specificity to the attachment glycoproteins of closely related henipaviruses and has markedly diminished binding to the six Eph receptors. However, EFNB2-D62Q has high residual binding to EphB3 and EphB4. A second deep mutational scan of EFNB2 identified combinatorial mutations to further enhance specificity to NiV-G. A triple mutant of soluble EFNB2, D62Q-Q130L-V167L, has minimal binding to Eph receptors but maintains binding, albeit reduced, to NiV-G. Soluble EFNB2 decoy receptors carrying the specificity mutations were potent neutralizers of chimeric henipaviruses. These findings demonstrate how specific residue changes at the shared binding interface of a promiscuous ligand (EFNB2) can influence selectivity for multiple receptors, and may also offer insight towards the development of henipavirus therapeutics and diagnostics.

Targeting Ras-binding domain of ELMO1 by computational nanobody design

The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.

Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 μM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

Engineering the T cell receptor for fun and profit: Uncovering complex biology, interrogating the immune system, and targeting disease

T cell receptors (TCRs) orchestrate cellular immunity by recognizing peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. Due to the TCR's central role in immunity and tight connection with human health, there has been significant interest in modulating TCR properties through protein engineering methods. Complicating these efforts is the complexity and vast diversity of TCR-peptide/MHC interfaces, the interdependency between TCR affinity, specificity, and cross-reactivity, and the sophisticated relationships between TCR binding properties and T cell function, many aspects of which are not well understood. Here we review TCR engineering, starting with a brief historical overview followed by discussions of more recent developments, including new efforts and opportunities to engineer TCR affinity, modulate specificity, and develop novel TCR-based constructs.

Engineering Proteins by Combining Deep Mutational Scanning and Yeast Display

Protein engineering using display platforms such as yeast display and phage display has allowed discovery of proteins with therapeutic and industrial applications. Antibodies and T cell receptors developed for therapeutic applications are often engineered by constructing libraries of mutations in loops of five to ten residues called complementarity determining regions that are in proximity to the antigen. In the past decade, deep mutational scanning has become a powerful tool in a protein engineer's toolbox, as it allows one to compare the impact of all 20 amino acids at each position, across the length of the protein. Thus, a single experiment can provide a sequence-activity landscape with information about hotspots or suboptimal binding sites in the original proteins. These residues or regions may be overlooked by engineering methods that are driven solely by structures or directed evolution of error-prone PCR libraries. Here, we describe experimental methods to engineer proteins by combining yeast display and deep mutational scanning mutagenesis, using T cell receptors as an example.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Opening of a cryptic pocket in β-lactamase increases penicillinase activity

A protein is a shape-shifter, but it is currently unclear which of the many structures a protein can adopt are relevant for its function. Here, we examine conformations that contain a “cryptic” pocket (i.e., a pocket absent in ligand-free structures). Cryptic pockets have potential utility in drug discovery efforts because they provide a means to target “undruggable” proteins (i.e., proteins lacking known pockets) or enhance rather than inhibit protein function. In this study, we use a combination of thiol-labeling and kinetic assays, NMR, and molecular dynamic simulations to identify the function of the Ω-loop cryptic pocket in β-lactamase enzymes. We find that an open pocket population is beneficial for hydrolysis of the substrate benzylpenicillin.

Understanding the functional role of protein-excited states has important implications in protein design and drug discovery. However, because these states are difficult to find and study, it is still unclear if excited states simply result from thermal fluctuations and generally detract from function or if these states can actually enhance protein function. To investigate this question, we consider excited states in β-lactamases and particularly a subset of states containing a cryptic pocket which forms under the Ω-loop. Given the known importance of the Ω-loop and the presence of this pocket in at least two homologs, we hypothesized that these excited states enhance enzyme activity. Using thiol-labeling assays to probe Ω-loop pocket dynamics and kinetic assays to probe activity, we find that while this pocket is not completely conserved across β-lactamase homologs, those with the Ω-loop pocket have a higher activity against the substrate benzylpenicillin. We also find that this is true for TEM β-lactamase variants with greater open Ω-loop pocket populations. We further investigate the open population using a combination of NMR chemical exchange saturation transfer experiments and molecular dynamics simulations. To test our understanding of the Ω-loop pocket’s functional role, we designed mutations to enhance/suppress pocket opening and observed that benzylpenicillin activity is proportional to the probability of pocket opening in our designed variants. The work described here suggests that excited states containing cryptic pockets can be advantageous for function and may be favored by natural selection, increasing the potential utility of such cryptic pockets as drug targets.

Targeted Mitochondrial Fluorescence Imaging-Guided Tumor Antimetabolic Therapy with the Imprinted Polymer Nanomedicine Capable of Specifically Recognizing Dihydrofolate Reductase

As we all know, inhibiting the activity of dihydrofolate reductase (DHFR) has always been an effective strategy for folate antimetabolites to treat tumors. In the past, it mainly relied on chemical drugs. Here, we propose a new strategy, (3-propanecarboxyl)triphenylphosphonium bromide (CTPB)-modified molecularly imprinted polymer nanomedicine (MIP-CTPB). MIP-CTPB prepared by imprinting the active center of DHFR can specifically bind to the active center to block the catalytic activity of DHFR, thereby inhibiting the synthesis of DNA and ultimately inhibiting the tumor growth. The modification of CTPB allows the nanomedicine to be targeted and enriched in mitochondria, where DHFR is abundant. The confocal laser imaging results show that MIP-CTPB can target mitochondria. Cytotoxicity experiments show that MIP-CTPB inhibits HeLa cell proliferation by 42.2%. In vivo experiments show that the tumor volume of the MIP-CTPB-treated group is only one-sixth of that of the untreated group. The fluorescent and paramagnetic properties of the nanomedicine enable targeted fluorescence imaging of mitochondria and T2-weighted magnetic resonance imaging of tumors. This research not only opens up a new direction for the application of molecular imprinting, but also provides a new idea for tumor antimetabolic therapy guided by targeted mitochondrial imaging.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC₅₀ values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

TAPBPR promotes antigen loading on MHC-I molecules using a peptide trap

Chaperones Tapasin and TAP-binding protein related (TAPBPR) perform the important functions of stabilizing nascent MHC-I molecules (chaperoning) and selecting high-affinity peptides in the MHC-I groove (editing). While X-ray and cryo-EM snapshots of MHC-I in complex with TAPBPR and Tapasin, respectively, have provided important insights into the peptide-deficient MHC-I groove structure, the molecular mechanism through which these chaperones influence the selection of specific amino acid sequences remains incompletely characterized. Based on structural and functional data, a loop sequence of variable lengths has been proposed to stabilize empty MHC-I molecules through direct interactions with the floor of the groove. Using deep mutagenesis on two complementary expression systems, we find that important residues for the Tapasin/TAPBPR chaperoning activity are located on a large scaffolding surface, excluding the loop. Conversely, loop mutations influence TAPBPR interactions with properly conformed MHC-I molecules, relevant for peptide editing. Detailed biophysical characterization by solution NMR, ITC and FP-based assays shows that the loop hovers above the MHC-I groove to promote the capture of incoming peptides. Our results suggest that the longer loop of TAPBPR lowers the affinity requirements for peptide selection to facilitate peptide loading under conditions and subcellular compartments of reduced ligand concentration, and to prevent disassembly of high-affinity peptide-MHC-I complexes that are transiently interrogated by TAPBPR during editing.

Transferrin receptor targeting by de novo sheet extension

The de novo design of polar protein-protein interactions is challenging because of the thermodynamic cost of stripping water away from the polar groups. Here, we describe a general approach for designing proteins which complement exposed polar backbone groups at the edge of beta sheets with geometrically matched beta strands. We used this approach to computationally design small proteins that bind to an exposed beta sheet on the human transferrin receptor (hTfR), which shuttles interacting proteins across the blood-brain barrier (BBB), opening up avenues for drug delivery into the brain. We describe a design which binds hTfR with a 20 nM Kd, is hyperstable, and crosses an in vitro microfluidic organ-on-a-chip model of the human BBB. Our design approach provides a general strategy for creating binders to protein targets with exposed surface beta edge strands.

Computational design and experimental optimization of protein binders with prospects for biomedical applications

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

An engineered decoy receptor for SARS-CoV-2 broadly binds protein S sequence variants

The spike S of SARS-CoV-2 recognizes ACE2 on the host cell membrane to initiate entry. Soluble decoy receptors, in which the ACE2 ectodomain is engineered to block S with high affinity, potently neutralize infection and, because of close similarity with the natural receptor, hold out the promise of being broadly active against virus variants without opportunity for escape. Here, we directly test this hypothesis. We find that an engineered decoy receptor, sACE22v2.4, tightly binds S of SARS-associated viruses from humans and bats, despite the ACE2-binding surface being a region of high diversity. Saturation mutagenesis of the receptor-binding domain followed by in vitro selection, with wild-type ACE2 and the engineered decoy competing for binding sites, failed to find S mutants that discriminate in favor of the wild-type receptor. We conclude that resistance to engineered decoys will be rare and that decoys may be active against future outbreaks of SARS-associated betacoronaviruses.

An engineered decoy receptor for SARS-CoV-2 broadly binds protein S sequence variants

The spike S of SARS-CoV-2 recognizes ACE2 on the host cell membrane to initiate entry. Soluble decoy receptors, in which the ACE2 ectodomain is engineered to block S with high affinity, potently neutralize infection and, due to close similarity with the natural receptor, hold out the promise of being broadly active against virus variants without opportunity for escape. Here, we directly test this hypothesis. We find an engineered decoy receptor, sACE2 2 .v2.4, tightly binds S of SARS-associated viruses from humans and bats, despite the ACE2-binding surface being a region of high diversity. Saturation mutagenesis of the receptor-binding domain (RBD) followed by in vitro selection, with wild type ACE2 and the engineered decoy competing for binding sites, failed to find S mutants that discriminate in favor of the wild type receptor. Variant N501Y in the RBD, which has emerged in a rapidly spreading lineage (B.1.1.7) in England, enhances affinity for wild type ACE2 20-fold but remains tightly bound to engineered sACE22.v2.4. We conclude that resistance to engineered decoys will be rare and that decoys may be active against future outbreaks of SARS-associated betacoronaviruses.

A De Novo Designed Esterase with p-Nitrophenyl Acetate Hydrolysis Activity

Esterases are a large family of enzymes with wide applications in the industry. However, all esterases originated from natural sources, limiting their use in harsh environments or newly- emerged reactions. In this study, we designed a new esterase to develop a new protocol to satisfy the needs for better biocatalysts. The ideal spatial conformation of the serine catalytic triad and the oxygen anion hole at the substrate-binding site was constructed by quantum mechanical calculation. The catalytic triad and oxygen anion holes were then embedded in the protein scaffold using the new enzyme protocol in Rosetta 3. The design results were subsequently evaluated, and optimized designs were used for expression and purification. The designed esterase had significant lytic activities towards p-nitrophenyl acetate, which was confirmed by point mutations. Thus, this study developed a new protocol to obtain novel enzymes that may be useful in unforgiving environments or novel reactions.

Deep mutagenesis in the study of COVID-19: a technical overview for the proteomics community

INTRODUCTION

The spike (S) of SARS coronavirus 2 (SARS-CoV-2) engages angiotensin-converting enzyme 2 (ACE2) on a host cell to trigger viral-cell membrane fusion and infection. The extracellular region of ACE2 can be administered as a soluble decoy to compete for binding sites on the receptor-binding domain (RBD) of S, but it has only moderate affinity and efficacy. The RBD, which is targeted by neutralizing antibodies, may also change and adapt through mutation as SARS-CoV-2 becomes endemic, posing challenges for therapeutic and vaccine development.

AREAS COVERED

Deep mutagenesis is a Big Data approach to characterizing sequence variants. A deep mutational scan of ACE2 expressed on human cells identified mutations that increase S affinity and guided the engineering of a potent and broad soluble receptor decoy. A deep mutational scan of the RBD displayed on the surface of yeast has revealed residues tolerant of mutational changes that may act as a source for drug resistance and antigenic drift.

EXPERT OPINION

Deep mutagenesis requires a selection of diverse sequence variants; an in vitro evolution experiment that is tracked with next-generation sequencing. The choice of expression system, diversity of the variant library and selection strategy have important consequences for data quality and interpretation.

Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2

For severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter human cells, the spike protein on the surface of the virus must bind to the host receptor protein, angiotensin-converting enzyme 2 (ACE2). A soluble version of the receptor is being explored as a therapeutic. Chan et al. used deep mutagenesis to identify ACE2 mutants that bind more tightly to the spike protein and combined mutations to further increase binding affinity (see the Perspective by DeKosky). A promising variant was engineered to be a stable dimer that has a binding affinity for the spike protein; it is comparable with neutralizing antibodies and neutralized both SARS-CoV-2 and SARS-CoV-1 in a cell-based assay. In addition, the similarity to the natural receptor may limit the possibility for viral escape.

Science, this issue p. 1261; see also p. 1167

The spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds angiotensin-converting enzyme 2 (ACE2) on host cells to initiate entry, and soluble ACE2 is a therapeutic candidate that neutralizes infection by acting as a decoy. By using deep mutagenesis, mutations in ACE2 that increase S binding are found across the interaction surface, in the asparagine 90–glycosylation motif and at buried sites. The mutational landscape provides a blueprint for understanding the specificity of the interaction between ACE2 and S and for engineering high-affinity decoy receptors. Combining mutations gives ACE2 variants with affinities that rival those of monoclonal antibodies. A stable dimeric variant shows potent SARS-CoV-2 and -1 neutralization in vitro. The engineered receptor is catalytically active, and its close similarity with the native receptor may limit the potential for viral escape.

Structure-guided engineering of the affinity and specificity of CARs against Tn-glycopeptides

The potency of adoptive T cell therapies targeting the cell surface antigen CD19 has been demonstrated in hematopoietic cancers. It has been difficult to identify appropriate targets in nonhematopoietic tumors, but one class of antigens that have shown promise is aberrant O-glycoprotein epitopes. It has long been known that dysregulated synthesis of O-linked (threonine or serine) sugars occurs in many cancers, and that this can lead to the expression of cell surface proteins containing O-glycans comprised of a single N-acetylgalactosamine (GalNAc, known as Tn antigen) rather than the normally extended carbohydrate. Previously, we used the scFv fragment of antibody 237 as a chimeric antigen receptor (CAR) to mediate recognition of mouse tumor cells that bear its cognate Tn-glycopeptide epitope in podoplanin, also called OTS8. Guided by the structure of the 237 Fab:Tn-OTS8-glycopeptide complex, here we conducted a deep mutational scan showing that residues flanking the Tn-glycan contributed significant binding energy to the interaction. Design of 237-scFv libraries in the yeast display system allowed us to isolate scFv variants with higher affinity for Tn-OTS8. Selection with a noncognate human antigen, Tn-MUC1, yielded scFv variants that were broadly reactive with multiple Tn-glycoproteins. When configured as CARs, engineered T cells expressing these scFv variants showed improved activity against mouse and human cancer cell lines defective in O-linked glycosylation. This strategy provides CARs with Tn-peptide specificities, all based on a single scFv scaffold, that allows the same CAR to be tested for toxicity in mice and efficacy against mouse and human tumors.

Engineered receptors for human cytomegalovirus that are orthogonal to normal human biology

A trimeric glycoprotein complex on the surface of human cytomegalovirus (HCMV) binds to platelet-derived growth factor (PDGF) receptor α (PDGFRα) to mediate host cell recognition and fusion of the viral and cellular membranes. Soluble PDGFRα potently neutralizes HCMV in tissue culture, and its potential use as an antiviral therapeutic has the benefit that any escape mutants will likely be attenuated. However, PDGFRα binds multiple PDGF ligands in the human body as part of developmental programs in embryogenesis and continuing through adulthood. Any therapies with soluble receptor therefore come with serious efficacy and safety concerns, especially for the treatment of congenital HCMV. Soluble virus receptors that are orthogonal to human biology might resolve these concerns. This engineering problem is solved by deep mutational scanning on the D2-D3 domains of PDGFRα to identify variants that maintain interactions with the HCMV glycoprotein trimer in the presence of competing PDGF ligands. Competition by PDGFs is conformation-dependent, whereas HCMV trimer binding is independent of proper D2-D3 conformation, and many mutations at the receptor-PDGF interface are suitable for functionally separating trimer from PDGF interactions. Purified soluble PDGFRα carrying a targeted mutation succeeded in displaying wild type affinity for HCMV trimer with a simultaneous loss of PDGF binding, and neutralizes trimer-only and trimer/pentamer-expressing HCMV strains infecting fibroblasts or epithelial cells. Overall, this work makes important progress in the realization of soluble HCMV receptors for clinical application.

Human cytomegalovirus (HCMV) causes severe disease in transplant recipients and immunocompromised patients, and infections in a fetus or neonate are responsible for life-long neurological defects. Cell entry is in part mediated by a trimeric glycoprotein complex on the viral surface, which binds tightly to the host receptor PDGFRα. The soluble extracellular region of PDGFRα can be used as an antiviral agent to potently neutralize the virus in vitro. However, PDGFRα ordinarily binds growth factors in the human body to regulate developmental programs, which will limit the in vivo efficacy and safety of soluble PDGFRα. Using saturation mutagenesis and selections in human cell culture, mutations in PDGFRα are identified that eliminate off-target growth factor interactions while preserving HCMV binding and neutralization.

The sequence of human ACE2 is suboptimal for binding the S spike protein of SARS coronavirus 2

The rapid and escalating spread of SARS coronavirus 2 (SARS-CoV-2) poses an immediate public health emergency. The viral spike protein S binds ACE2 on host cells to initiate molecular events that release the viral genome intracellularly. Soluble ACE2 inhibits entry of both SARS and SARS-2 coronaviruses by acting as a decoy for S binding sites, and is a candidate for therapeutic, prophylactic and diagnostic development. Using deep mutagenesis, variants of ACE2 are identified with increased binding to the receptor binding domain of S. Mutations are found across the interface, in the N90-glycosylation motif, and at buried sites where they are predicted to enhance local folding and presentation of the interaction epitope. When single substitutions are combined, large increases in binding can be achieved. The mutational landscape offers a blueprint for engineering high affinity proteins and peptides that block receptor binding sites on S to meet this unprecedented challenge.

Variant effect predictions capture some aspects of deep mutational scanning experiments

BACKGROUND

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.

RESULTS

On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.

CONCLUSIONS

DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Multi-input chemical control of protein dimerization for programming graded cellular responses

Chemical and optogenetic methods for post-translationally controlling protein function have enabled modulation and engineering of cellular functions. However, most of these methods only confer single-input, single-output control. To increase the diversity of post-translational behaviors that can be programmed, we built a system based on a single protein receiver that can integrate multiple drug inputs, including approved therapeutics. Our system translates drug inputs into diverse outputs using a suite of engineered reader proteins to provide variable dimerization states of the receiver protein. We show that our single receiver protein architecture can be used to program a variety of cellular responses, including graded and proportional dual-output control of transcription and mammalian cell signaling. We apply our tools to titrate the competing activities of the Rac and Rho GTPases to control cell morphology. Our versatile tool set will enable researchers to post-translationally program mammalian cellular processes and to engineer cell therapies.

Unintended specificity of an engineered ligand-binding protein facilitated by unpredicted plasticity of the protein fold

Attempts to create novel ligand-binding proteins often focus on formation of a binding pocket with shape complementarity against the desired ligand (particularly for compounds that lack distinct polar moieties). Although designed proteins often exhibit binding of the desired ligand, in some cases they display unintended recognition behavior. One such designed protein, that was originally intended to bind tetrahydrocannabinol (THC), was found instead to display binding of 25-hydroxy-cholecalciferol (25-D3) and was subjected to biochemical characterization, further selections for enhanced 25-D3 binding affinity and crystallographic analyses. The deviation in specificity is due in part to unexpected altertion of its conformation, corresponding to a significant change of the orientation of an α-helix and an equally large movement of a loop, both of which flank the designed ligand-binding pocket. Those changes led to engineered protein constructs that exhibit significantly more contacts and complementarity towards the 25-D3 ligand than the initial designed protein had been predicted to form towards its intended THC ligand. Molecular dynamics simulations imply that the initial computationally designed mutations may contribute to the movement of the helix. These analyses collectively indicate that accurate prediction and control of backbone dynamics conformation, through a combination of improved conformational sampling and/or de novo structure design, represents a key area of further development for the design and optimization of engineered ligand-binding proteins.

Conformational Engineering of HIV-1 Env Based on Mutational Tolerance in the CD4 and PG16 Bound States

HIV-1 infection is initiated by viral Env engaging the host receptor CD4, triggering Env to transition from a "closed" to "open" conformation during the early events of virus-cell membrane fusion. To understand how Env sequence accommodates this conformational change, mutational landscapes decoupled from virus replication were determined for Env from BaL (clade B) and DU422 (clade C) isolates interacting with CD4 or antibody PG16 that preferentially recognizes closed trimers. Sequence features uniquely important to each bound state were identified, including glycosylation and binding sites. Notably, the Env apical domain and trimerization interface are under selective pressure for PG16 binding. Based on this key observation, mutations were found that increase presentation of quaternary epitopes associated with properly conformed trimers when Env is expressed at the plasma membrane. Many mutations reduce electrostatic repulsion at the Env apex and increase PG16 recognition of Env sequences from clades A and B. Other mutations increase hydrophobic packing at the gp120 inner-outer domain interface and were broadly applicable for engineering Env from diverse strains spanning tiers 1, 2, and 3 across clades A, B, C, and BC recombinants. Core mutations predicted to introduce steric strain in the open state show markedly reduced CD4 interactions. Finally, we demonstrate how our methodology can be adapted to interrogate interactions between membrane-associated Env and the matrix domain of Gag. These findings and methods may assist vaccine design.IMPORTANCE HIV-1 Env is dynamic and undergoes large conformational changes that drive fusion of virus and host cell membranes. Three Env proteins in a trimer contact each other at their apical tips to form a closed conformation that presents epitopes recognized by broadly neutralizing antibodies. The apical tips separate, among other changes, to form an open conformation that binds tightly to host receptors. Understanding how Env sequence facilitates these structural changes can inform the biophysical mechanism and aid immunogen design. Using deep mutational scans decoupled from virus replication, we report mutational landscapes for Env from two strains interacting with conformation-dependent binding proteins. Residues in the Env trimer interface and apical domains are preferentially conserved in the closed conformation, and conformational diversity is facilitated by electrostatic repulsion and an underpacked core between domains. Specific mutations are described that enhance presentation of the trimeric closed conformation across diverse HIV-1 strains.

Structural architecture of a dimeric class C GPCR based on co-trafficking of sweet taste receptor subunits

Class C G protein-coupled receptors (GPCRs) are obligatory dimers that are particularly important for neuronal responses to endogenous and environmental stimuli. Ligand recognition through large extracellular domains leads to the reorganization of transmembrane regions to activate G protein signaling. Although structures of individual domains are known, the complete architecture of a class C GPCR and the mechanism of interdomain coupling during receptor activation are unclear. By screening a mutagenesis library of the human class C sweet taste receptor subunit T1R2, we enhanced surface expression and identified a dibasic intracellular retention motif that modulates surface expression and co-trafficking with its heterodimeric partner T1R3. Using a highly expressed T1R2 variant, dimerization sites along the entire subunit within all the structural domains were identified by a comprehensive mutational scan for co-trafficking with T1R3 in human cells. The data further reveal that the C terminus of the extracellular cysteine-rich domain needs to be properly folded for T1R3 dimerization and co-trafficking, but not for surface expression of T1R2 alone. These results guided the modeling of the T1R2-T1R3 dimer in living cells, which predicts a twisted arrangement of domains around the central axis, and a continuous folded structure between transmembrane domain loops and the cysteine-rich domains. These insights have implications for how conformational changes between domains are coupled within class C GPCRs.

De novo design of a fluorescence-activating β-barrel

The regular arrangements of β-strands around a central axis in β-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with β-barrels. Here we show that accurate de novo design of β-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of β-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI, and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.

Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas

Given the constantly improving cost and speed of genome sequencing, it is reasonable to expect that personal genomes will soon be known for many millions of humans. This stands in stark contrast with our limited ability to interpret the sequence variants which we find. Although it is, perhaps, easiest to interpret variants in coding regions, knowledge of functional impact is unknown for the vast majority of missense variants. While many computational approaches can predict the impact of coding variants, they are given a little weight in the current guidelines for interpreting clinical variants. Laboratory assays produce comparatively more trustworthy results, but until recently did not scale to the space of all possible mutations. The development of deep mutational scanning and other multiplexed assays of variant effect has now brought feasibility of this endeavour within view. Here, we review progress in this field over the last decade, break down the different approaches into their components, and compare methodological differences.

Mapping Interaction Sites on Human Chemokine Receptors by Deep Mutational Scanning

Chemokine receptors CXCR4 and CCR5 regulate WBC trafficking and are engaged by the HIV-1 envelope glycoprotein gp120 during infection. We combine a selection of human CXCR4 and CCR5 libraries comprising nearly all of ∼7000 single amino acid substitutions with deep sequencing to define sequence-activity landscapes for surface expression and ligand interactions. After consideration of sequence constraints for surface expression, known interaction sites with HIV-1-blocking Abs were appropriately identified as conserved residues following library sorting for Ab binding, validating the use of deep mutational scanning to map functional interaction sites in G protein-coupled receptors. Chemokine CXCL12 was found to interact with residues extending asymmetrically into the CXCR4 ligand-binding cavity, similar to the binding surface of CXCR4 recognized by an antagonistic viral chemokine previously observed crystallographically. CXCR4 mutations distal from the chemokine binding site were identified that enhance chemokine recognition. This included disruptive mutations in the G protein-coupling site that diminished calcium mobilization, as well as conservative mutations to a membrane-exposed site (CXCR4 residues H79^2.45 and W161^4.50) that increased ligand binding without loss of signaling. Compared with CXCR4-CXCL12 interactions, CCR5 residues conserved for gp120 (HIV-1 BaL strain) interactions map to a more expansive surface, mimicking how the cognate chemokine CCL5 makes contacts across the entire CCR5 binding cavity. Acidic substitutions in the CCR5 N terminus and extracellular loops enhanced gp120 binding. This study demonstrates how comprehensive mutational scanning can define functional interaction sites on receptors, and novel mutations that enhance receptor activities can be found simultaneously.

Creation of artificial protein-protein interactions using α-helices as interfaces

Designing novel protein-protein interactions (PPIs) with high affinity is a challenging task. Directed evolution, a combination of randomization of the gene for the protein of interest and selection using a display technique, is one of the most powerful tools for producing a protein binder. However, the selected proteins often bind to the target protein at an undesired surface. More problematically, some selected proteins bind to their targets even though they are unfolded. Current state-of-the-art computational design methods have successfully created novel protein binders. These computational methods have optimized the non-covalent interactions at interfaces and thus produced artificial protein complexes. However, to date there are only a limited number of successful examples of computationally designed de novo PPIs. De novo design of coiled-coil proteins has been extensively performed and, therefore, a large amount of knowledge of the sequence-structure relationship of coiled-coil proteins has been accumulated. Taking advantage of this knowledge, de novo design of inter-helical interactions has been used to produce artificial PPIs. Here, we review recent progress in the in silico design and rational design of de novo PPIs and the use of α-helices as interfaces.

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Antibody Affinity Maturation by Computational Design

The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.

Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein

With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.

Subtle changes at the variable domain interface of the T-cell receptor can strongly increase affinity

Most affinity-maturation campaigns for antibodies and T-cell receptors (TCRs) operate on the residues at the binding site, located within the loops known as complementarity-determining regions (CDRs). Accordingly, mutations in contact residues, or so-called "second shell" residues, that increase affinity are typically identified by directed evolution involving combinatorial libraries. To determine the impact of residues located at a distance from the binding site, here we used single-codon libraries of both CDR and non-CDR residues to generate a deep mutational scan of a human TCR against the cancer antigen MART-1·HLA-A2. Non-CDR residues included those at the interface of the TCR variable domains (Vα and Vβ) and surface-exposed framework residues. Mutational analyses showed that both Vα/Vβ interface and CDR residues were important in maintaining binding to MART-1·HLA-A2, probably due to either structural requirements for proper Vα/Vβ association or direct contact with the ligand. More surprisingly, many Vα/Vβ interface substitutions yielded improved binding to MART-1·HLA-A2. To further explore this finding, we constructed interface libraries and selected them for improved stability or affinity. Among the variants identified, one conservative substitution (F45βY) was most prevalent. Further analysis of F45βY showed that it enhanced thermostability and increased affinity by 60-fold. Thus, introducing a single hydroxyl group at the Vα/Vβ interface, at a significant distance from the TCR·peptide·MHC-binding site, remarkably affected ligand binding. The variant retained a high degree of specificity for MART-1·HLA-A2, indicating that our approach provides a general strategy for engineering improvements in either soluble or cell-based TCRs for therapeutic purposes.

Scratching the Surface: Resurfacing Proteins to Endow New Properties and Function

Protein engineering is an emerging discipline that dovetails modern molecular biology techniques with high-throughput screening, laboratory evolution technologies, and computational approaches to modify sequence, structure, and, in some cases, function and properties of proteins. The ultimate goal is to develop new proteins with improved or designer functions for use in biotechnology, medicine, and basic research. One way to engineer proteins is to change their solvent-exposed regions through focused or random "protein resurfacing." In this review we explain what protein resurfacing is, and discuss recent examples of how this strategy is used to generate proteins with altered or broadened recognition profiles, improved stability, solubility, and expression, cell-penetrating ability, and reduced immunogenicity. Additionally we comment on how these properties can be further improved using chemical resurfacing approaches. Protein resurfacing will likely play an increasingly important role as more biologics enter clinical use, and we present some arguments to support this view.

Sampling and energy evaluation challenges in ligand binding protein design

The steroid hormone 17α-hydroxylprogesterone (17-OHP) is a biomarker for congenital adrenal hyperplasia and hence there is considerable interest in development of sensors for this compound. We used computational protein design to generate protein models with binding sites for 17-OHP containing an extended, nonpolar, shape-complementary binding pocket for the four-ring core of the compound, and hydrogen bonding residues at the base of the pocket to interact with carbonyl and hydroxyl groups at the more polar end of the ligand. Eight of 16 designed proteins experimentally tested bind 17-OHP with micromolar affinity. A co-crystal structure of one of the designs revealed that 17-OHP is rotated 180° around a pseudo-two-fold axis in the compound and displays multiple binding modes within the pocket, while still interacting with all of the designed residues in the engineered site. Subsequent rounds of mutagenesis and binding selection improved the ligand affinity to nanomolar range, while appearing to constrain the ligand to a single bound conformation that maintains the same "flipped" orientation relative to the original design. We trace the discrepancy in the design calculations to two sources: first, a failure to model subtle backbone changes which alter the distribution of sidechain rotameric states and second, an underestimation of the energetic cost of desolvating the carbonyl and hydroxyl groups of the ligand. The difference between design model and crystal structure thus arises from both sampling limitations and energy function inaccuracies that are exacerbated by the near two-fold symmetry of the molecule.

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

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.

An Evolution-Based Approach to De Novo Protein Design

EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs.

Affinity Maturation of a Cyclic Peptide Handle for Therapeutic Antibodies Using Deep Mutational Scanning

Meditopes are cyclic peptides that bind in a specific pocket in the antigen-binding fragment of a therapeutic antibody such as cetuximab. Provided their moderate affinity can be enhanced, meditope peptides could be used as specific non-covalent and paratope-independent handles in targeted drug delivery, molecular imaging, and therapeutic drug monitoring. Here we show that the affinity of a recently reported meditope for cetuximab can be substantially enhanced using a combination of yeast display and deep mutational scanning. Deep sequencing was used to construct a fitness landscape of this protein-peptide interaction, and four mutations were identified that together improved the affinity for cetuximab 10-fold to 15 nm Importantly, the increased affinity translated into enhanced cetuximab-mediated recruitment to EGF receptor-overexpressing cancer cells. Although in silico Rosetta simulations correctly identified positions that were tolerant to mutation, modeling did not accurately predict the affinity-enhancing mutations. The experimental approach reported here should be generally applicable and could be used to develop meditope peptides with low nanomolar affinity for other therapeutic antibodies.

A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces

T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.