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ProteinTuneRL is a framework designed to harness the power of reinforcement learning for advanced protein design. The project enables fine-tuning of generative models to explore and optimize protein sequences with tailored structural and functional properties.
Protein adsorption at solid–liquid interfaces underlies many biomedical and materials applications, yet the mechanisms governing adsorption of proteins and their assemblies remain poorly understood. Here we investigate de novo–designed proteins that self-assemble into three fibrillar morphologies — small (S), large (L), and helical (H)—on muscovite mica. S-fibers are metastable, forming first but diminishing as L- and H-fibers develop and deposit. Adsorption of both fibers and monomers depends on fiber morphology and solvent environment, which is modulated by the substrate. The anisotropic surface features of the fiber types—long axis of S- and L-fibers and helical grooves of H-fibers—correlate with specific crystallographic directions on mica (001). S- and L-fibers align along the unique lattice axis, while the substrate-facing groove of H-fibers preferentially aligns along the remaining symmetry-related directions. Increasing potassium chloride (KCl) concentrations to molar levels alters adsorption, decreasing monomer coverage relative to fibers. These observations, interpreted through the effect of interfacial interface, indicate that ion-mediated modulation of hydration layers governs electrostatic interactions and alignment. This study reveals how coupling between protein topography, substrate crystallography, and hydration structure dictates selective adsorption and orientation of protein assemblies, offering insights for rational biomolecular material design.
Many proteins exist naturally as symmetrical homooligomers or homopolymers. The emergent structural and functional properties of such protein assemblies have inspired extensive efforts in biomolecular design2–5. As synthesized by ribosomes, proteins are inherently asymmetric. Therefore, they must acquire multiple surface patches that selectively associate to generate the different symmetry elements needed to form higher-order architectures—a daunting task for protein design. Here we address this problem using an inorganic chemical approach, whereby multiple modes of protein–protein interactions and symmetry are simultaneously achieved by selective, ‘one-pot’ coordination of soft and hard metal ions. We show that a monomeric protein (protomer) appropriately modified with biologically inspired hydroxamate groups and zinc-binding motifs assembles through concurrent Fe 3+ and Zn 2+ coordination into discrete dodecameric and hexameric cages. Our cages closely resemble natural polyhedral protein architectures and are, to our knowledge, unique among designed systems in that they possess tightly packed shells devoid of large apertures. At the same time, they can assemble and disassemble in response to diverse stimuli, owing to their heterobimetallic construction on minimal interprotein-bonding footprints. With stoichiometries ranging from [2 Fe:9 Zn:6 protomers] to [8 Fe:21 Zn:12 protomers], these protein cages represent some of the compositionally most complex protein assemblies—or inorganic coordination complexes—obtained by design.
Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many molecules, fluorine is incorporated to enhance the half-life and improve bioavailability. Fluorinated compounds enter the human body through food, water, and xenobiotics including pharmaceuticals, exposing gut microbes to these substances. The human gut microbiota is known for its xenobiotic biotransformation capabilities, but it was not previously known whether gut microbial enzymes could break carbon-fluorine bonds, potentially altering the toxicity of these compounds. Here, through the development of a rapid, miniaturized fluoride detection assay for whole-cell screening, we identified active gut microbial defluorinases. We biochemically characterized enzymes from diverse human gut microbial classes including Clostridia, Bacilli, and Coriobacteriia, with the capacity to hydrolyze (di)fluorinated organic acids and a fluorinated amino acid. Whole-protein alanine scanning, molecular dynamics simulations, and chimeric protein design enabled the identification of a disordered C-terminal protein segment involved in defluorination activity. Domain swapping exclusively of the C-terminus conferred defluorination activity to a nondefluorinating dehalogenase. To advance our understanding of the structural and sequence differences between defluorinating and nondefluorinating dehalogenases, we trained machine learning models which identified protein termini as important features. Models trained on 41-amino acid segments from protein C termini alone predicted defluorination activity with 83% accuracy (compared to 95% accuracy based on full-length protein features). This work is relevant for therapeutic interventions and environmental and human health by uncovering specificity-determining signatures of fluorine biochemistry from the gut microbiome.
The design of protein–protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein–protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning–based methods to unlock the widespread application of designed protein–protein interfaces and self-assembling protein nanomaterials in biotechnology.
Potent and selective binders of the key proapoptotic proteins BAK and BAX have not been described. We use computational protein design to generate high affinity binders of BAK and BAX with greater than 100-fold specificity for their target. Both binders activate their targets when at low concentration, driving pore formation, but inhibit membrane permeabilization when in excess. Crystallography shows that the BAK binder induces BAK unfolding, exposing the α6 helix and BH3 domain. Together, these data suggest that upon binding, BAK or BAX unfold; at high binder concentrations, self-association of the partially folded BAK or BAX proteins is blocked and the membrane remains intact, whereas at low concentrations, dimers form, and the membrane ruptures. Our designed binders modulate apoptosis via direct, specific interactions with BAK and BAX and reveal that for therapeutic strategies targeting BAK and BAX, inhibition requires saturating binder concentrations at the site of action.
Synthetic biology is defined by Design-Build-Test-Learn cycles. Machine learning has yielded significant improvements in protein design; thus, we propose that “Learning” can precede “Design” to optimize engineering workflows. Additionally, shifting to cell-free platforms will further accelerate “Build” and “Test” capabilities by allowing the rapid expression of proteins and functional testing without the need for cellular transformation or isolation.
Noncanonical Amino Acids (NCAAs) provide numerous avenues for introduction of novel functionality to peptides and proteins. NCAAs can be incorporated through solid phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis, wide chemical space and lack of empirically resolved structures modeling the effects of NCAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations NCAAs introduce we utilize molecular potentials that describe the forces in protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parameterized in include novel NCAAs. Here, in this work, we introduce NCAP a software package to generate CHARMM compatible parameters from quantum chemical calculation. Unlike currently available tools NCAP is designed to recognize NCAA structure and automatically bridge the gap between DFT calculations and potential parameters. For our software we discuss workflow, validation against canonical parameter sets and comparison to published NCAA-protein structures.
The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C 3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems.
Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.
CRISPR construct design is a key step in the practice of genome editing, which includes identification of appropriate Cas proteins, design and selection of guide RNAs (gRNAs), and selection of regulatory elements to express gRNAs and Cas proteins. Here, we review the choices of CRISPR-based genome editors suited for different needs in plant genome editing applications. We consider the technical aspects of gRNA design and the associated computational tools. We also discuss strategies for the design of multiplex CRISPR constructs for high-throughput manipulation of complex biological processes or polygenic traits. We provide recommendations for different elements of CRISPR constructs and discuss the remaining challenges of CRISPR construct optimization in plant genome editing.
Ribosomes are often used in synthetic biology as a tool to produce desired proteins with enhanced properties or entirely new functions. However, repurposing ribosomes for producing designer proteins is challenging due to the limited number of engineering solutions available to alter the natural activity of these enzymes. In this study, we advance ribosome engineering by describing a novel strategy based on functional fusions of ribosomal RNA (rRNA) with messenger RNA (mRNA). Specifically, we create an mRNA–ribosome fusion called RiboU, where the 16S rRNA is covalently attached to selenocysteine insertion sequence (SECIS), a regulatory RNA element found in mRNAs encoding selenoproteins. When SECIS sequences are present in natural mRNAs, they instruct ribosomes to decode UGA codons as selenocysteine (Sec, U) codons instead of interpreting them as stop codons. This enables ribosomes to insert Sec into the growing polypeptide chain at the appropriate site. Our work demonstrates that the SECIS sequence maintains its functionality even when inserted into the ribosome structure. As a result, the engineered ribosomes RiboU interpret UAG codons as Sec codons, allowing easy and site-specific insertion of Sec in a protein of interest with no further modification to the natural machinery of protein synthesis. To validate this approach, we use RiboU ribosomes to produce three functional target selenoproteins in Escherichia coli by site-specifically inserting Sec into the proteins’ active sites. Overall, our work demonstrates the feasibility of creating functional mRNA–rRNA fusions as a strategy for ribosome engineering, providing a novel tool for producing Sec-containing proteins in live bacterial cells.
Electron transfer coupled to redox chemistry is at the heart of metabolism. The proteins responsible for moving electrons (protein electron carriers) must have emerged at the origin of life. The small iron–sulfur-binding bacterial ferredoxins were likely among these first proteins. Embedded within the ferredoxin sequence and structure is a symmetry that points to an ancient gene duplication event. Little is understood about the nature of ferredoxins prior to this duplication event or what environmental factors may have driven the selection for more complex forms. The deep-time molecular history of ferredoxins goes back billions of years and cannot be reconstructed by phylogenetic analyses based on amino acid sequences. Here, we use structure-guided protein design to model a fossil half-ferredoxin stage in the evolution of this fold, the semidoxins, and their symmetric full-length counterparts, the symdoxins. Semidoxin designs homodimerize, exhibiting structural, thermodynamic, and electrochemical behaviors in most cases identical to cognate symdoxins. However, the semi- and symdoxin fossil stages behave differently when incorporated into an in vivo electron transfer complementation assay. Both can support bacterial growth dependent on protein expression. Growth rates of bacteria expressing the semidoxins are much more sensitive to oxygen than those of bacteria expressing symdoxins. Motivated by the in vivo functionality of designed semidoxins, we identified putative naturally occurring semidoxins in extant anaerobic microorganisms. This is consistent with the observed in vivo oxygen sensitivity of the semidoxin designs. One natural semidoxin is shown to be folded and redox active. However, it exists as a mixture of monomers and dimers, suggesting a potential connection between semidoxins and even simpler single iron–sulfur cluster-binding peptides.
Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions.
The de novo design of three protein chains that associate to form a heterotrimer (but not any of the possible two-chain heterodimers) and that can drive the assembly of higher-order branching structures is an important challenge for protein design. We designed helical heterotrimers with specificity conferred by buried hydrogen bond networks and large aromatic residues to enhance shape complementary packing. We obtained ten designs for which all three chains cooperatively assembled into heterotrimers with few or no other species present. Crystal structures of a helical bundle heterotrimer and extended versions, with helical repeat proteins fused to individual subunits, showed all three chains assembling in the designed orientation. We used these heterotrimers as building blocks to construct larger cyclic oligomers, which were structurally validated by electron microscopy. Our three-way junction designs provide new routes to complex protein nanostructures and enable the scaffolding of three distinct ligands for modulation of cell signaling.
ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.
The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (k cat /K M 146 M -1 s -1 ). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (k cat /K M 103,000 M -1 s -1 ) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.
Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here, in this paper, we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.