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Some protein binding pairs exhibit extreme specificities that functionally insulate them from homologs. Such pairs evolve mostly by accumulating single-point mutations, and mutants are selected if their affinity exceeds the threshold required for function1–4. Thus, homologous and high-specificity binding pairs bring to light an evolutionary conundrum: how does a new specificity evolve while maintaining the required affinity in each intermediate5,6? Until now, a fully functional single-mutation path that connects two orthogonal pairs has only been described where the pairs were mutationally close enabling experimental enumeration of all intermediates2. We present an atomistic and graph-theoretical framework for discovering low molecular strain single-mutation paths that connect two extant pairs and apply it to two orthogonal bacterial colicin endonuclease-immunity pairs separated by 17 interface mutations7. We were not able to find a strain-free and functional path in the sequence space defined by the two extant pairs. By including mutations that bridge amino acids that cannot be exchanged through single-nucleotide mutations, we found a strain-free 19-mutation trajectory that is completely functional in vivo. Despite the long mutational trajectory, the specificity switch is remarkably abrupt, resulting from only one radical mutation on each partner. Each of the critical specificity-switch mutations increases fitness, demonstrating that functional divergence could be driven by positive Darwinian selection. These results reveal how even radical functional changes in an epistatic fitness landscape may evolve.

Albumin is the most abundant protein in the blood serum of mammals and has essential carrier and physiological roles. Albumins are also used in a wide variety of molecular and cellular experiments and in the cultured-meat industry. Despite their importance, however, albumins are challenging for heterologous expression in microbial hosts, likely due to 17 conserved intramolecular disulfide bonds. Therefore, albumins used in research and biotechnological applications either derive from animal serum, despite severe ethical and reproducibility concerns, or from recombinant expression in yeast or rice. We used the PROSS algorithm to stabilize human and bovine serum albumins. All the stabilized designs are highly expressed inE. coli. Design accuracy is verified by crystallographic analysis of a human albumin variant with 16 mutations. This albumin variant exhibits ligand binding properties similar to those of the wild type. Remarkably, a design with 73 mutations relative to human albumin exhibits over 40°C improved stability and is stable beyond the boiling point. Our results suggest that proteins with many disulfide bridges have the potential to exhibit extreme stability when subjected to design. The designed albumins may be used to make economical, reproducible, and animal-free reagents for molecular and cell biology. They also open the way to high-throughput screening to study and enhance albumin carrier properties.Graphical abstractHighlights-Computational design was applied to stabilize human and bovine serum albumin-Albumin designs express solubly inE. coliand exhibit up to 40 °C increased thermostability-Some designs exhibit identical ligand binding properties-Crystal structure confirms design accuracy-Designs can be used in cell culture andin vitroapplications

Lassa virus (LASV) is a human pathogen, causing substantial morbidity and mortality

Imaging of gene-expression patterns in live animals is difficult to achieve with fluorescent proteins because tissues are opaque to visible light. Imaging of transgene expression with magnetic resonance imaging (MRI), which penetrates to deep tissues, has been limited by single reporter visualization capabilities. Moreover, the low-throughput capacity of MRI limits large-scale mutagenesis strategies to improve existing reporters. Here we develop an MRI system, called GeneREFORM, comprising orthogonal reporters for two-color imaging of transgene expression in deep tissues. Starting from two promiscuous deoxyribonucleoside kinases, we computationally designed highly active, orthogonal enzymes ('reporter genes') that specifically phosphorylate two MRI-detectable synthetic deoxyribonucleosides ('reporter probes'). Systemically administered reporter probes exclusively accumulate in cells expressing the designed reporter genes, and their distribution is displayed as pseudo-colored MRI maps based on dynamic proton exchange for noninvasive visualization of transgene expression. We envision that future extensions of GeneREFORM will pave the way to multiplexed deep-tissue mapping of gene expression in live animals.

Solute-binding proteins (SBPs) have evolved to balance the demands of ligand affinity, thermostability, and conformational change to accomplish diverse functions in small molecule transport, sensing, and chemotaxis. Although the ligand-induced conformational changes that occur in SBPs make them useful components in biosensors, they are challenging targets for protein engineering and design. Here, we have engineered a d-alanine-specific SBP into a fluorescence biosensor with specificity for the signaling molecule d-serine (D-serFS). This was achieved through binding site and remote mutations that improved affinity (KD = 6.7 ± 0.5 μM), specificity (40-fold increase vs glycine), thermostability (Tm = 79 °C), and dynamic range (∼14%). This sensor allowed measurement of physiologically relevant changes in d-serine concentration using two-photon excitation fluorescence microscopy in rat brain hippocampal slices. This work illustrates the functional trade-offs between protein dynamics, ligand affinity, and thermostability and how these must be balanced to achieve desirable activities in the engineering of complex, dynamic proteins.

Several methods have been developed to explore interactions among water-soluble proteins or regions of proteins. However, techniques to target transmembrane domains have not been examined thoroughly. Here we developed a novel computational approach to design transmembrane sequences that specifically modulate protein-protein interactions in the membrane. To illustrate this method we demonstrated that BclxL can interact with other members of the Bcl2 family through the transmembrane domain and that these interactions are necessary for BclxL control of cell death. Next, we designed sequences that specifically recognize and sequester the transmembrane domain of BclxL. Hence, we were able to prevent BclxL intra-membrane interactions and cancel its anti-apoptotic effect. These results advance our understanding of protein-protein interactions in membranes and provide new means to modulate them. Moreover, the success of our approach may trigger the development of a new generation of inhibitors targeting interactions between transmembrane domains.

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

Design of structurally diverse enzymes is constrained by long-range interactions that are needed for accurate folding. We introduce an atomistic and machine-learning strategy for Combinatorial Assembly and Design of ENZymes, CADENZ, to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of active and structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a tenfold improved hit rate and >10,000 active enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

Humanization is an essential step in developing animal-derived antibodies into therapeutics, and approximately one third of approved antibodies have been humanized. Conventional humanization approaches graft the complementarity-determining regions (CDRs) of the animal antibody onto several homologous human frameworks. This process, however, often drastically lowers stability and antigen binding, demanding iterative mutational fine-tuning to recover the original antibody properties. Here, we present Computational hUMan AntiBody design (CUMAb), a web-accessible method that starts from an experimental or model antibody structure, systematically grafts the animal CDRs on thousands of human frameworks, and uses Rosetta atomistic simulations to rank the designs by energy and structural integrity (http://CUMAb.weizmann.ac.il). CUMAb designs of five independent antibodies exhibit similar affinities to the parental animal antibody, and some designs show marked improvement in stability. Surprisingly, nonhomologous frameworks are often preferred to the highest-homology ones, and several CUMAb designs that use different human frameworks and differ by dozens of mutations are functionally equivalent. Thus, CUMAb presents a general and streamlined approach to optimizing antibody stability and expressibility while increasing humanness.

Although it is well-known that limited local mutations of enzymes, such as matrix metalloproteinases (MMPs), may change enzyme activity by orders of magnitude as well as its stability, the completely rational design of proteins is still challenging. These local changes alter the electrostatic potential and thus local electrostatic fields, which impacts the dynamics of water molecules close the protein surface. Here we show by a combined computational design, experimental, and molecular dynamics (MD) study that local mutations have not only a local but also a global effect on the solvent: In the specific case of the matrix metalloprotease MMP14, we found that the nature of local mutations, coupled with surface morphology, have the ability to influence large patches of the water hydrogen-bonding network at the protein surface, which is correlated with stability. The solvent contribution can be experimentally probed via terahertz (THz) spectroscopy, thus opening the door to the exciting perspective of rational protein design in which a systematic tuning of hydration water properties allows manipulation of protein stability and enzymatic activity.

Dan (Danny) Tawfik, a leader in biochemistry and protein evolution, sadly died due to a fatal climbing accident on May 4th, 2021. Apart from science, rock climbing was Danny's passion and a source of pride as only a handful of researchers are active climbers. Danny made unique and long-lasting contributions to our understanding of molecular evolution. He was also incredibly generous with his time and insights, and many researchers around the world are indebted to him, not least the two authors of this obituary.

White-rot fungi secrete an impressive repertoire of high-redox potential laccases (HRPLs) and peroxidases for efficient oxidation and utilization of lignin. Laccases are attractive enzymes for green-chemistry applications due to their broad substrate range and low environmental impact. Since expression of functional recombinant HRPLs is challenging, however, iterative directed evolution protocols have been applied to improve their expression, activity and stability. We implement a rational, stabilize-and-diversify strategy to two HRPLs that we could not functionally express: first, we use the PROSS stability-design algorithm to allow functional expression in yeast. Second, we use the stabilized enzymes as starting points for FuncLib active-site design to improve their activity and substrate diversity. Four of the FuncLib designed HRPLs and their PROSS progenitor exhibit substantial diversity in reactivity profiles against high-redox potential substrates, including lignin monomers. Combinations of 3-4 subtle mutations that change the polarity, solvation and sterics of the substrate-oxidation site result in orders of magnitude changes in reactivity profiles. These stable and versatile HRPLs are a step towards the generation of an effective lignin-degrading consortium of enzymes that can be secreted from yeast. More broadly, the stabilize-and-diversify strategy can be applied to other challenging enzyme families to study and expand the utility of natural enzymes.

Chemical science 13(39), 11680 - 11695 (2022). doi:10.1039/D2SC02794A, Over half the proteins in the E. coli cytoplasm form homo or hetero-oligomeric structures. Experimentally determined structures are often considered in determining a protein's oligomeric state, but static structures miss the dynamic equilibrium between different quaternary forms. The problem is exacerbated in homo-oligomers, where the oligomeric states are challenging to characterize. Here, we re-evaluated the oligomeric state of 17 different bacterial proteins across a broad range of protein concentrations and solutions by native mass spectrometry (MS), mass photometry (MP), size exclusion chromatography (SEC), and small-angle X-ray scattering (SAXS), finding that most exhibit several oligomeric states. Surprisingly, some proteins did not show mass-action driven equilibrium between the oligomeric states. For approximately half the proteins, the predicted oligomeric forms described in publicly available databases underestimated the complexity of protein quaternary structures in solution. Conversely, AlphaFold multimer provided an accurate description of the potential multimeric states for most proteins, suggesting that it could help resolve uncertainties on the solution state of many proteins., Published by RSC, Cambridge

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability, and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning-based

De novo-designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) that we expressed in mouse primary T cells and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.

Stabilization of the metastable envelope glycoprotein (Env) of HIV-1 is hypothesized to improve induction of broadly neutralizing antibodies. We improved the expression yield and stability of the HIV-1 envelope glycoprotein BG505SOSIP.664 gp140 by means of a previously described automated sequence and structure-guided computational thermostabilization approach, PROSS. This combines sequence conservation information with computational assessment of mutant stabilization, thus taking advantage of the extensive natural sequence variation present in HIV-1 Env. PROSS is used to design three gp140 variants with 17-45 mutations relative to the parental construct. One of the designs is experimentally observed to have a fourfold improvement in yield and a 4 °C increment in thermostability. In addition, the designed immunogens have similar antigenicity profiles to the native flexible linker version of wild type, BG505SOSIP.664 gp140 (NFL Wt) to major epitopes targeted by broadly neutralizing antibodies. PROSS eliminates the laborious process of screening many variants for stability and functionality, providing a proof of principle of the method for stabilization and improvement of yield without compromising antigenicity for next generation complex, highly glycosylated vaccine candidates.

The branched-chain amino acids (BCAAs) leucine, isoleucine and valine are synthesized via a common biosynthetic pathway. Ketol-acid reductoisomerase (KARI) is the second enzyme in this pathway. In addition to its role in BCAA biosynthesis, KARI catalyzes two rate-limiting steps that are key components of a cell-free biofuel biosynthesis route. For industrial applications, reaction temperature and enzyme stability are key factors that affect process robustness and product yield. Here, we have solved the cryo-EM structure (2.94 Å resolution) of a homododecameric Class I KARI (from Campylobacter jejuni) and demonstrated how a triad of amino acid side chains plays a crucial role in promoting the oligomerization of this enzyme. Importantly, both its thermal and solvent stability are greatly enhanced in the dodecameric state when compared to its dimeric counterpart (apparent melting temperatures (Tm) of 83.1 °C and 51.5 °C, respectively). We also employed protein design (PROSS) for a tetrameric Class II KARI (from Escherichia coli) to generate a variant with improved thermal and solvent stabilities. In total, 34 mutations were introduced, which did not affect the oligomeric state of this enzyme but resulted in a fully functional catalyst with a significantly elevated Tm (58.5 °C vs. 47.9 °C for the native version).

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use, but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel, but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wild type enzymes are functionally expressed in yeast whereas their wild type parents are not. Three of these designs exhibit substantial and useful diversity in reactivity profile and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families.

Botulinum neurotoxin type E (BoNT/E), the fastest acting toxin of all BoNTs, cleaves the 25 kDa synaptosomal associated protein (SNAP-25) in motor neurons, leading to flaccid paralysis. Specific detection and quantification of BoNT/E-cleaved SNAP-25 neoepitope is essential for diagnosis of BoNT/E intoxication as well as for characterization of anti-BoNT/E antibody preparations. In order to isolate highly specific monoclonal antibodies suitable for in vitro immuno-detection of the exposed neoepitope, mice and rabbits were immunized with an eight amino acid peptide composed of the C-terminus of the cleaved SNAP-25. Immunized rabbits developed a specific and robust polyclonal antibody response, whereas immunized mice mostly demonstrated a weak antibody response that could not discriminate between the two forms of SNAP-25. An immune scFv phage-display library was constructed from the immunized rabbits and a panel of antibodies was isolated. Sequence alignment of the isolated clones revealed high similarity between both heavy and light chains, with exceptionally short HCDR3 sequences. A chimeric scFv-Fc antibody was further expressed and characterized, exhibiting a selective, ultra-high affinity (pM) towards the SNAP-25 neoepitope. Moreover, this antibody enabled sensitive detection of the cleaved SNAP-25 in BoNT/E treated SiMa cells with no cross reactivity with the intact SNAP-25. This novel antibody can be further used to develop an in vitro cell-based assay to diagnose BoNT/E intoxication and to characterize antitoxin preparations, thus eliminating the use of animals in the standard mouse bioassay.

SummaryA wide range of SARS-CoV-2 neutralizing monoclonal antibodies (mAbs) were reported to date, most of which target the spike glycoprotein and in particular its receptor binding domain (RBD) and N-terminal domain (NTD) of the S1 subunit. The therapeutic implementation of these antibodies has been recently challenged by emerging SARS-CoV-2 variants that harbor extensively mutated spike versions. Consequently, the re-assessment of mAbs, previously reported to neutralize the original early-version of the virus, is of high priority.Four previously selected mAbs targeting non-overlapping epitopes, were evaluated for their binding potency to RBD versions harboring individual mutations at spike positions 417, 439, 453, 477, 484 and 501. Mutations at these positions represent the prevailing worldwide distributed modifications of the RBD, previously reported to mediate escape from antibody neutralization. Additionally, the in vitro neutralization potencies of the four RBD-specific mAbs, as well as two NTD-specific mAbs, were evaluated against two frequent SARS-CoV-2 variants of concern (VOCs): (i) the B.1.1.7 variant, emerged in the UK and (ii) the B.1.351 variant, emerged in South Africa. Variant B.1.351 was previously suggested to escape many therapeutic mAbs, including those authorized for clinical use. The possible impact of RBD mutations on recognition by mAbs is addressed by comparative structural modelling. Finally, we demonstrate the therapeutic potential of three selected mAbs by treatment of K18-hACE2 transgenic mice two days post infection with each of the virus strains.Our results clearly indicate that despite the accumulation of spike mutations, some neutralizing mAbs preserve their potency against SARS-CoV-2. In particular, the highly potent MD65 and BL6 mAbs are shown to retain their ability to bind the prevalent novel viral mutations and to effectively protect against B.1.1.7 and B.1.351 variants of high clinical concern.

Pseudomonas aeruginosa is an opportunistic human pathogen that develops difficult-to-treat biofilms in immunocompromised individuals, cystic fibrosis patients, and in chronic wounds. P. aeruginosa has an arsenal of physiological attributes that enable it to evade standard antibiotic treatments, particularly in the context of biofilms where it grows slowly and becomes tolerant to many drugs. One of its survival strategies involves the production of the redox-active phenazine, pyocyanin, which promotes biofilm development. We previously identified an enzyme, PodA, that demethylated pyocyanin and disrupted P. aeruginosa biofilm development in vitro. Here, we asked if this protein could be used as a potential therapeutic for P. aeruginosa infections together with tobramycin, an antibiotic typically used in the clinic. A major roadblock to answering this question was the poor yield and stability of wild-type PodA purified from standard Escherichia coli overexpression systems. We hypothesized that the insufficient yields were due to poor packing within PodA’s obligatory homotrimeric interfaces. We therefore applied the protein design algorithm, AffiLib, to optimize the symmetric core of this interface, resulting in a design that incorporated five mutations leading to a 20-fold increase in protein yield from heterologous expression and purification and a substantial increase in stability to environmental conditions. The addition of the designed PodA with tobramycin led to increased killing of P. aeruginosa cultures under oxic and hypoxic conditions in both the planktonic and biofilm states. This study highlights the potential for targeting extracellular metabolites to assist the control of P. aeruginosa biofilms that tolerate conventional antibiotic treatment.

Glycans decorate cell surface, secreted glycoproteins and glycolipids. Altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potentials, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of a single tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that this conformer’s stability selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational method to design a variant with seven core mutations that exhibited tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

Pseudomonas aeruginosa is an opportunistic human pathogen that develops difficult-to-treat biofilms in immunocompromised individuals, cystic fibrosis patients, and in chronic wounds. P. aeruginosa has an arsenal of physiological attributes that enable it to evade standard antibiotic treatments, particularly in the context of biofilms where it grows slowly and becomes tolerant to many drugs. One of its survival strategies involves the production of the redox-active phenazine, pyocyanin, which promotes biofilm development. We previously identified an enzyme, PodA, that demethylated pyocyanin and disrupted P. aeruginosa biofilm development in vitro. Here, we asked if this protein could be used as a potential therapeutic for P. aeruginosa infections together with tobramycin, an antibiotic typically used in the clinic. A major roadblock to answering this question was the poor yield and stability of wild-type PodA purified from standard Escherichia coli overexpression systems. We hypothesized that the insufficient yields were due to poor packing within PodA’s obligatory homotrimeric interfaces. We therefore applied the protein design algorithm, AffiLib, to optimize the symmetric core of this interface, resulting in a design that incorporated five mutations leading to a 20-fold increase in protein yield from heterologous expression and purification and a substantial increase in stability to environmental conditions. The addition of the designed PodA with tobramycin led to increased killing of P. aeruginosa cultures under oxic and hypoxic conditions in both the planktonic and biofilm states. This study highlights the potential for targeting extracellular metabolites to assist the control of P. aeruginosa biofilms that tolerate conventional antibiotic treatment.

Angiotensin-converting enzyme 2 (ACE2) is the cellular receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computational analysis of mammalian ACE2 orthologues suggests various residues at the interface with the viral receptor binding domain that could facilitate tighter interaction compared to the human-ACE2. Introducing several mutations to the human-ACE2 resulted with significantly augmented affinity to the viral spike complex. This modified human-ACE2 fused to an Fc portion of an antibody makes a potent immunoadhesin that effectively targets SARS-CoV-2.

De novo designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs (proCARs) stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.

Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscures these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.

Proteins are increasingly used in basic and applied biomedical research. Many proteins, however, are only marginally stable and can be expressed in limited amounts, thus hampering research and applications. Research has revealed the thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability. With this growing understanding, computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability. Current methods are more general and, by combining phylogenetic analysis with atomistic design, have shown drastic improvements in solubility, thermal stability, and aggregation resistance while maintaining the protein's primary molecular activity. Stability design is opening the way to rational engineering of improved enzymes, therapeutics, and vaccines and to the application of protein design methodology to large proteins and molecular activities that have proven challenging in the past.

Recent progress in structure-prediction methods that rely on deep learning suggests that the atomic structure of almost any protein may soon be predictable directly from its amino acid sequence. This much-awaited revolution was driven by substantial improvements in the reliability of methods for inferring the spatial distances between amino acid pairs from an analysis of homologous sequences. Improved reliability has been accompanied, however, by a reduced ability to detect amino acid relationships that are not due to direct spatial contacts, such as those that arise from protein dynamics or allostery. Given the central importance of dynamics and allostery to protein activity, we argue that an important future advance would extend modeling beyond predicting a single static structure. Here, we briefly review some of the developments that have led to the remarkable recent achievement in structure prediction and speculate what methods and sources of information may be leveraged in the future to develop a modeling framework that addresses protein dynamics and allostery.

In recent decades, antibodies (Abs) have attracted the attention of academia and the biopharmaceutical industry due to their therapeutic properties and versatility in binding a vast spectrum of antigens. Different engineering strategies have been developed for optimizing Ab specificity, efficacy, affinity, stability and production, enabling systematic screening and analysis procedures for selecting lead candidates. This quality assessment is critical but usually demands time-consuming and labor-intensive purification procedures. Here, we harnessed the direct-mass spectrometry (direct-MS) approach, in which the analysis is carried out directly from the crude growth media, for the rapid, structural characterization of designed Abs. We demonstrate that properties such as stability, specificity and interactions with antigens can be defined, without the need for prior purification. This ty.

Glycans decorate the cell surface, secreted glycoproteins and glycolipids, and altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potential, however, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of the tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the potential plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that this conformer's stability selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational algorithm to design a variant with seven core mutations in the variable domain's light-heavy chain interface that exhibits tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

Allosteric regulation underlies living cells' ability to sense changes in nutrient and signaling-molecule concentrations, but the ability to computationally design allosteric regulation into non-allosteric proteins has been elusive. Allosteric-site design is complicated by the requirement to encode the relative stabilities of active and inactive conformations of the same protein in the presence and absence of both ligand and effector. To address this challenge, we used Rosetta to design the backbone of the flexible heavy-chain complementarity-determining region 3 (HCDR3), and used geometric matching and sequence optimization to place a Zn2+ -coordination site in a fluorescein-binding antibody. We predicted that due to HCDR3's flexibility, the fluorescein-binding pocket would configure properly only upon Zn2+ application. We found that regulation by Zn2+ was reversible and sensitive to the divalent ion's identity, and came at the cost of reduced antibody stability and fluorescein-binding affinity. Fluorescein bound at an order of magnitude higher affinity in the presence of Zn2+ than in its absence, and the increase in fluorescein affinity was due almost entirely to faster fluorescein on-rate, suggesting that Zn2+ preorganized the antibody for fluorescein binding. Mutation analysis demonstrated the extreme sensitivity of Zn2+ regulation on the atomic details in and around the metal-coordination site. The designed antibody could serve to study how allosteric regulation evolved from non-allosteric binding proteins, and suggests a way to designing molecular sensors for environmental and biomedical targets.

Improving an enzyme's initially low catalytic efficiency with a new target substrate by an order of magnitude or two may require only a few rounds of mutagenesis and screening or selection. However, subsequent rounds of optimization tend to yield decreasing degrees of improvement (diminishing returns) eventually leading to an optimization plateau. We aimed to optimize the catalytic efficiency of bacterial phosphotriesterase (PTE) toward V-type nerve agents. Previously, we improved the catalytic efficiency of wild-type PTE toward the nerve agent VX by 500-fold, to a catalytic efficiency (kcat/KM) of 5 × 106 M-1 min-1. However, effective in vivo detoxification demands an enzyme with a catalytic efficiency of >107 M-1 min-1. Here, following eight additional rounds of directed evolution and the computational design of a stabilized variant, we evolved PTE variants that detoxify VX with a kcat/KM ≥ 5 × 107 M-1 min-1 and Russian VX (RVX) with a kcat/KM ≥ 107 M-1 min-1. These final 10-fold improvements were the most time consuming and laborious, as most libraries yielded either minor or no improvements. Stabilizing the evolving enzyme, and avoiding tradeoffs in activity with different substrates, enabled us to obtain further improvements beyond the optimization plateau and evolve PTE variants that were overall improved by >5000-fold with VX and by >17 000-fold with RVX. The resulting variants also hydrolyze G-type nerve agents with high efficiency (GA, GB at kcat/KM > 5 × 107 M-1 min-1) and can thus serve as candidates for broad-spectrum nerve-agent prophylaxis and post-exposure therapy using low enzyme doses.