Tanja Kortemme

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

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the multistate design problem of the foldswitching protein RfaH as an in-depth case study, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach.

Protein-based switches that respond to different inputs to regulate cellular outputs, such as gene expression, are central to synthetic biology. For increased controllability, multi-input switches that integrate several cooperating and competing signals for the regulation of a shared output are of particular interest. The nuclear hormone receptor (NHR) superfamily offers promising starting points for engineering multi-input-controlled responses to clinically approved drugs. Starting from the VgEcR/RXR pair, we demonstrate that novel (multi)drug regulation can be achieved by exchange of the ecdysone receptor (EcR) ligand binding domain (LBD) for other human NHR-derived LBDs. For responses activated to saturation by an agonist for the first LBD, we show that outputs can be boosted by an agonist targeting the second LBD. In combination with an antagonist, output levels are tunable by up to three simultaneously …

Machine learning has revolutionized computational protein design, enabling significant progress in protein backbone generation and sequence design. Here, we introduce Frame2seq, a structure-conditioned masked language model for protein sequence design. Frame2seq generates sequences in a single pass, achieves 49.1% sequence recovery on the CATH 4.2 test dataset, and accurately estimates the error in its own predictions, outperforming the autoregressive ProteinMPNN model with over six times faster inference. To probe the ability of Frame2seq to generate novel designs beyond the native-like sequence space it was trained on, we experimentally test 26 Frame2seq designs for de novo backbones with low identity to the starting sequences. We show that Frame2seq successfully designs soluble (22/26), monomeric, folded, and stable proteins (17/26), including a design with 0% sequence identity to native. The speed and accuracy of Frame2seq will accelerate exploration of novel sequence space across diverse design tasks, including challenging applications such as multi-objective optimization.

Provided herein are protein biosensors, fusion proteins, compositions, and methods that are useful in detecting SARS-CoV-2 viruses in a sample from a subject. The viral detection assays described herein are solution-based, rapid, and quantitative. The protein biosensors and fusion proteins herein are able to bind to SARS-CoV-2 viral proteins. Use of the fusion proteins in proximity assays (eg, split reporter assays) allows sensitive detection of SARS-CoV-2 virus in samples.

Allosteric regulation is classically defined as action at a distance, where a perturbation outside of a protein active site affects function. While this definition has motivated many studies of allosteric mechanisms at the level of protein structure, translating these insights to the allosteric regulation of entire cellular processes – and their crosstalk – has received less attention, despite the broad importance of allostery for cellular regulation foreseen by Jacob and Monod. Here, we revisit an evolutionary model for the widespread emergence of allosteric regulation in colocalized proteins, describe supporting evidence, and discuss emerging advances in mapping allostery in cellular networks that link precise and often allosteric perturbations at the molecular level to functional changes at the pathway and systems levels.

Allosteric regulation is central to protein function in cellular networks. A fundamental open question is whether cellular regulation of allosteric proteins occurs only at a few defined positions or at many sites distributed throughout the structure. Here, we probe the regulation of GTPases—protein switches that control signaling through regulated conformational cycling—at residue-level resolution by deep mutagenesis in the native biological network. For the GTPase Gsp1/Ran, we find that 28% of the 4,315 assayed mutations show pronounced gain-of-function responses. Twenty of the sixty positions enriched for gain-of-function mutations are outside the canonical GTPase active site switch regions. Kinetic analysis shows that these distal sites are allosterically coupled to the active site. We conclude that the GTPase switch mechanism is broadly sensitive to cellular allosteric regulation. Our systematic discovery of new …

Biological regulation ubiquitously depends on protein allostery, but the regulatory mechanisms are incompletely understood, especially in proteins that undergo ligand-induced allostery with few structural changes. Here we used hydrogen-deuterium exchange with mass spectrometry (HDX/MS) to map allosteric effects in a paradigm ligand-responsive transcription factor, the lac repressor (LacI), in different functional states (apo, or bound to inducer, anti-inducer, and/or DNA). Although X-ray crystal structures of the LacI core domain in these states are nearly indistinguishable, HDX/MS experiments reveal widespread differences in flexibility. We integrate these results with modeling of protein-ligand-solvent interactions to propose a revised model for allostery in LacI, where ligand binding allosterically shifts the conformational ensemble as a result of distinct changes in the rigidity of secondary structures in the different …

This disclosure describes recombinant angiotensin-converting enzyme II (ACE2) polypeptides, fusion proteins, and compositions thereof having improved binding affinity for the SARS-CoV-2 spike protein receptor binding domain relative to wild-type ACE2. Also provided are methods of using the recombinant ACE2 polypeptides, fusion proteins, and compositions thereof for treating subjects infected with a SARS-CoV-2 virus (ie, subjects with COVID-19), subjects having symptoms suggestive of a SARS-CoV-2 infection, and subjects exposed to or at risk of exposure to SARS-CoV-2 virus. Other virus infections may also be treated.

The SARS-CoV-2 Omicron variant, with 15 mutations in Spike receptor-binding domain (Spike-RBD), renders virtually all clinical monoclonal antibodies against WT SARS-CoV-2 ineffective. We recently engineered the SARS-CoV-2 host entry receptor, ACE2, to tightly bind WT-RBD and prevent viral entry into host cells ("receptor traps"). Here we determine cryo-EM structures of our receptor traps in complex with stabilized Spike ectodomain. We develop a multi-model pipeline combining Rosetta protein modeling software and cryo-EM to allow interface energy calculations even at limited resolution and identify interface side chains that allow for high-affinity interactions between our ACE2 receptor traps and Spike-RBD. Our structural analysis provides a mechanistic rationale for the high-affinity (0.53–4.2 nM) binding of our ACE2 receptor traps to Omicron-RBD confirmed with biolayer interferometry measurements …

Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Page 1 Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Jr., Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray* J. Chem. Theory Comput. 2017, 13 (6), 3031−3048. DOI: 10.1021/acs.jctc.7b00125 Cite This: J. Chem. Theory Comput. 2022, 18, 4594−4594 Read Online ACCESS Metrics & More Article Recommendations In the initially published version of this article, the potential for the disulfide dihedral CαCβSS was incorrect in Figure 4E, with a sign error in A_{2,…

Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Page 1 Correction to “The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design” Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank P. DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram K. Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Jr., Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray* J. Chem. Theory Comput. 2017, 13 (6), 3031−3048. DOI: 10.1021/acs.jctc.7b00125 Cite This: J. Chem. Theory Comput. 2022, 18, 4594−4594 Read Online ACCESS Metrics & More Article Recommendations In the initially published version of this article, the potential for the disulfide dihedral CαCβSS was incorrect in Figure 4E, with a sign error in A_{2,…

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input–output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit’s …

Proteins achieve their complex functions, such as molecular recognition with high affinity and specificity, through intricate three-dimensional geometries in functional sites. To engineer new protein functions, accurate positioning of amino acid functional groups is therefore critical but has remained difficult to achieve by computational methods because of current limitations in the design of new conformations with arbitrary user-defined geometries. Here, we introduce two computational methods capable of generating and predicting new local protein geometries: fragment kinematic closure (FKIC) and loophash kinematic closure (LHKIC). FKIC and LHKIC integrate two approaches: robotics-inspired kinematics of protein conformations and insertion of peptide fragments. We show that FKIC and LHKIC predict native-like conformations at atomic accuracy and with up to 140-fold improvements in sampling efficiency over …

Protein switches perform essential roles in many biological processes and are exciting targets for de novo protein design, which aims to produce proteins of arbitrary shape and functionality. However, the biophysical requirements for switch function - multiple conformational states, fine-tuned energetics, and stimuli-responsiveness - pose a formidable challenge for design by computation (or intuition). A variety of methods have been developed toward tackling this challenge, usually taking inspiration from the wealth of sequence and structural information available for naturally occurring protein switches. More recently, modular switches have been designed computationally, and new methods have emerged for sampling unexplored structure space, providing promising new avenues toward the generation of purpose-built switches and de novo signaling systems for cellular engineering.

The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a …

In response to our recent publication describing affinity-enhanced, long− half-life ACE2-based receptor traps for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralization (1), Liu et al.(2) point to their parallel work showing substratedependent peptidase activity of ACE2 active site mutants (3). Understanding the physiologically relevant ACE2 peptidase activity determinants is critical, as several groups are developing ACE2-based receptor traps with intact (4, 5), modestly attenuated (6), or ablated activity (1, 7, 8) to separate the effects of blocking on angiotensin II (Ang II) conversion. Toward this end, we introduced an H345L mutation that is postulated to form part of the oxyanion binding site (9); mutations in the oxyanion hole for zinc metalloproteases (10) are well known to disrupt the tetrahedral oxyanion in the transition state and dramatically reduce activity. Indeed, the H345L mutation was …

Allostery, broadly defined as a protein’s functional response to distal perturbations, is fundamental to biological regulation. In classical models, allosteric ligand binding produces a defined set of structural changes in the protein, resulting in a different low-energy conformation. Proteins that undergo ligand-induced allostery with few observable structural changes therefore frustrate interpretations by classical models. Here we used hydrogen-deuterium exchange with mass spectrometry (HDX/MS) to map the allosteric effects in a paradigm ligand-responsive allosteric transcription factor, the lac repressor (LacI). X-ray crystal structures of the core domain of LacI bound to different small molecule ligands, or the DNA operator, show less than 1.5 Å difference in the protein all-atom root-mean-square-deviation (RMSD) between any two structures. Despite this high degree of similarity among static structures, our HDX/MS experiments reveal widespread and unexpected differences in the flexibility of secondary structures in the LacI core domain in each functional state. We propose a model in which ligand binding allosterically switches the functional response of the repressor by selectively changing the dynamics of particular secondary structure elements relative to each other, shifting the conformational ensemble of the protein between mutually incompatible DNA-bound and inducer-bound states. Our model also provides a mechanistic context for the altered functions of thousands of documented LacI mutants. Furthermore, our approach provides a platform for characterizing and engineering allosteric responses in proteins.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of …