Design of a novel globular protein fold with atomic-level accuracy

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants

Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardization, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid probabilities inside the Rosetta software framework that allows for the side-by-side comparison of these models. Subsequently, existing protein fitness landscapes were used to benchmark novel ML methods in realistic protein design settings. We focused on the traditional problems of protein design: sampling and scoring. A major finding of our study is that ML approaches are better at purging the sampling space from deleterious mutations. Nevertheless, scoring resulting mutations without model fine-tuning showed no clear improvement over scoring with Rosetta. We conclude that ML now complements, rather than replaces, biophysical methods in protein design.

A combined in silico approach to design peptide ligands with increased receptor-subtype selectivity

G-protein coupled receptors are major drug targets that change their conformation upon binding of ligands to their extracellular binding pocket to transduce the signal to intracellular G-proteins or arrestins. In drug screening campaigns, computational methods are frequently used to predict binding affinities for chemical compounds in silico before experimental testing. Some of these methods take into consideration the inherent flexibility of the ligand and to some extent also of the receptor. Due to high structural flexibility, peptide ligands are exceptionally difficult to handle and approaches to effectively sample in silico receptor-peptide ligand interactions are limited. Here we describe a pipeline starting from microseconds molecular dynamics simulations of receptor and receptor ligand complexes to find reasonable starting conformations and derive constraints for subsequent flexible docking of peptide ligands, using Rosetta's FlexPepDock approach. We applied this approach to predict binding affinities for dynorphin and its variants to members of the opioid receptor family. Using an ensemble of docking poses, Rosetta's fixbb protein design method explored the sequence space at defined positions, to enhance binding affinities, aiming to increase subtype selectivity towards κ-opioid receptor while decreasing it towards μ-opioid receptor. The results of our computations were validated experimentally in a related study (Zangrandi et al., 2024[1]). Four out of six proposed variants lead to a significant increase in subtype selectivity in favor of κ-opioid receptor, highlighting the potential of our approach to design subtype selective peptide variants. The established workflow may also apply for other receptor types activated by peptide ligands.

Assembling branched and macrocyclic peptides on proteins

A two-step, biocompatible strategy enables site-specific generation of branched and macrocyclic peptide-protein conjugates. Solvent-exposed cysteines on proteins are modified by a small bifunctional reagent at near-physiological pH, followed by cyanopyridine-aminothiol click reactions to create branched or macrocyclic peptide architectures. This method offers design strategies for next-generation protein therapeutics.

Direct coupling analysis and the attention mechanism

Proteins are involved in nearly all cellular functions, encompassing roles in transport, signaling, enzymatic activity, and more. Their functionalities crucially depend on their complex three-dimensional arrangement. For this reason, being able to predict their structure from the amino acid sequence has been and still is a phenomenal computational challenge that the introduction of AlphaFold solved with unprecedented accuracy. However, the inherent complexity of AlphaFold's architectures makes it challenging to understand the rules that ultimately shape the protein's predicted structure. This study investigates a single-layer unsupervised model based on the attention mechanism. More precisely, we explore a Direct Coupling Analysis (DCA) method that mimics the attention mechanism of several popular Transformer architectures, such as AlphaFold itself. The model's parameters, notably fewer than those in standard DCA-based algorithms, can be directly used for extracting structural determinants such as the contact map of the protein family under study. Additionally, the functional form of the energy function of the model enables us to deploy a multi-family learning strategy, allowing us to effectively integrate information across multiple protein families, whereas standard DCA algorithms are typically limited to single protein families. Finally, we implemented a generative version of the model using an autoregressive architecture, capable of efficiently generating new proteins in silico.

Structure-based computational design of antibody mimetics: challenges and perspectives

The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences

This Faraday Discussion was the first to focus on the increasingly central role of big data, machine learning, and artificial intelligence in the chemical sciences. The aim was to critically discuss these topics, and to explore the question of how data can enable new discoveries in chemistry, both now and in the future. The programme spanned computational and experimental work, and encompassed emerging topics such as natural language processing, machine-learned potentials, optimization strategies, and robotics and self-driving laboratories. Here I provide some brief introductory comments on the history of this field, along with some personal views on the discussion topics covered, concluding with three future challenges for this area.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.

Molecular Origami: Designing Functional Molecules of the Future

In the field of chemical biology, DNA origami has been actively researched. This technique, which involves folding DNA strands like origami to assemble them into desired shapes, has made it possible to create complex nanometer-sized structures, marking a major breakthrough in nanotechnology. On the other hand, controlling the folding mechanisms and folded structures of proteins or shorter peptides has been challenging. However, recent advances in techniques such as protein origami, peptide origami, and de novo design peptides have made it possible to construct various nanoscale structures and create functional molecules. These approaches suggest the emergence of new molecular design principles, which can be termed "molecular origami". In this review, we provide an overview of recent research trends in protein/peptide origami and DNA/RNA origami and explore potential future applications of molecular origami technologies in electrochemical biosensors.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

Coarse-Grain Model of Ultrarigid Polymer Rods Comprising Bifunctionally Linked Peptide Bundlemers

Computationally designed homotetrameric helical peptide bundles have been functionalized at their N-termini to achieve supramolecular polymers, wherein individual bundles ("bundlemers") are the monomeric units. Adjacent bundles are linked via two covalent cross-links. The polymers exhibit a range of conformational properties, including formation of rigid-rods with micrometer-scale persistence lengths. Herein, a coarse-grained model is used to illuminate how molecular features affect the rod-like behavior of the polymers. With increasing affinity between bundlemer ends, a sharp transition in the persistence length is observed. Doubly linked chains exhibit larger persistence lengths and more robust formation of rigid-rod structures than singly linked chains. Chain stiffness increases with decreasing temperatures. Increasing the length of the cross-linker results in more flexible chains. This model provides insights into how molecular features control the structural properties of chains comprising doubly linked rigid bundlemers.

Atomic molecular dynamics simulation advances of de novo-designed proteins

Proteins are vital biological macromolecules that execute biological functions and form the core of synthetic biological systems. The history of de novo protein has evolved from initial successes in subordinate structural design to more intricate protein creation, challenging the complexities of natural proteins. Recent strides in protein design have leveraged computational methods to craft proteins for functions beyond their natural capabilities. Molecular dynamics (MD) simulations have emerged as a crucial tool for comprehending the structural and dynamic properties of de novo-designed proteins. In this study, we examined the pivotal role of MD simulations in elucidating the sampling methods, force field, water models, stability, and dynamics of de novo-designed proteins, highlighting their potential applications in diverse fields. The synergy between computational modeling and experimental validation continued to play a crucial role in the creation of novel proteins tailored for specific functions and applications.

A foldon-free prefusion F trimer vaccine for respiratory syncytial virus to reduce off-target immune responses

Respiratory syncytial virus (RSV) is a major cause of severe respiratory disease in infants and older people. Current RSV subunit vaccines are based on a fusion protein that is stabilized in the prefusion conformation and linked to a heterologous foldon trimerization domain to obtain a prefusion F (preF) trimer. Here we show that current RSV vaccines induce undesirable anti-foldon antibodies in non-human primates, mice and humans. To overcome this, we designed a foldon-free RSV preF trimer by elucidating the structural basis of trimerization-induced preF destabilization through molecular dynamics simulations and by introducing amino acid substitutions that negate hotspots of charge repulsion. The highly stable prefusion conformation was validated using antigenic and cryo-electron microscopy analysis. The preF is immunogenic and protective in naive mouse models and boosts neutralizing antibody titres in RSV-pre-exposed mice and non-human primates, while achieving similar titres to approved RSV vaccines in mice. This stable preF design is a promising option as a foldon-independent candidate for a next-generation RSV vaccine immunogen.

Comprehensive Understanding of Laboratory Evolution for Food Enzymes: From Design to Screening Innovations

In the field of food processing, enzymes play a pivotal role in improving product quality and flavor, and extending shelf life. However, the exposure of traditional food enzymes to high temperatures during processing often leads to a decrease in activity or even inactivation, limiting the effectiveness of their application under high-temperature conditions. Therefore, the modification of thermostability and activity of enzymes to adapt to extreme conditions through protein engineering has become a key way to improve the efficiency and economic benefits of industrial production. Directed evolution and semirational design strategies in the laboratory have proven to be broadly applicable frameworks for biochemical researchers in the food field, including those who are beginners. In this review, we systematically summarize semirational design strategies and high-throughput screening strategies, and introduce some intuitive computer simulation software to improve the thermostability and enzyme activity of food enzymes. The application of these strategies and techniques provides a comprehensive guide for the optimization of food enzymes. In addition, the latest hot topics of genetically engineered food enzymes in the field of application are discussed.

Tau Protein and Tauopathies: Exploring Tau Protein-Protein and Microtubule Interactions, Cross-Interactions and Therapeutic Strategies

Tau, a microtubule-associated protein (MAP), is essential to maintaining neuronal stability and function in the healthy brain. However, aberrant modifications and pathological aggregations of Tau are implicated in various neurodegenerative disorders, collectively known as tauopathies. The most common Tauopathy is Alzheimer's Disease (AD) counting nowadays more than 60 million patients worldwide. This comprehensive review delves into the multifaceted realm of Tau protein, puzzling out its intricate involvement in both physiological and pathological roles. Emphasis is put on Tau Protein-Protein Interactions (PPIs), depicting its interaction with tubulin, microtubules and its cross-interaction with other proteins such as Aβ1-42, α-synuclein, and the chaperone machinery. In the realm of therapeutic strategies, an overview of diverse possibilities is presented with their relative clinical progresses. The focus is mostly addressed to Tau protein aggregation inhibitors including recent small molecules, short peptides and peptidomimetics with specific focus on compounds that showed a double anti aggregative activity on both Tau protein and Aβ amyloid peptide. This review amalgamates current knowledge on Tau protein and evolving therapeutic strategies, providing a comprehensive resource for researchers seeking to deepen their understanding of the Tau protein and for scientists involved in the development of new peptide-based anti-aggregative Tau compounds.

Inventing Novel Protein Folds

The vastness of unexplored protein fold universe remains a significant question. Through systematic de novo design of proteins with novel αβ-folds, we demonstrated that nature has only explored a tiny portion of the possible folds. Numerous possible protein folds are still untouched by nature. This review outlines this study and discusses the prospects for design of functional proteins with novel folds.

De novo protein design with a denoising diffusion network independent of pretrained structure prediction models

The recent success of RFdiffusion, a method for protein structure design with a denoising diffusion probabilistic model, has relied on fine-tuning the RoseTTAFold structure prediction network for protein backbone denoising. Here, we introduce SCUBA-diffusion (SCUBA-D), a protein backbone denoising diffusion probabilistic model freshly trained by considering co-diffusion of sequence representation to enhance model regularization and adversarial losses to minimize data-out-of-distribution errors. While matching the performance of the pretrained RoseTTAFold-based RFdiffusion in generating experimentally realizable protein structures, SCUBA-D readily generates protein structures with not-yet-observed overall folds that are different from those predictable with RoseTTAFold. The accuracy of SCUBA-D was confirmed by the X-ray structures of 16 designed proteins and a protein complex, and by experiments validating designed heme-binding proteins and Ras-binding proteins. Our work shows that deep generative models of images or texts can be fruitfully extended to complex physical objects like protein structures by addressing outstanding issues such as the data-out-of-distribution errors.

Structure-function relationship of dynorphin B variants using naturally occurring amino acid substitutions

Dynorphins (Dyn) represent the subset of endogenous opioid peptides with the highest binding affinity to kappa opioid receptors (KOPrs). Activation of the G-protein-coupled pathway of KOPrs has strong anticonvulsant effects. Dyn also bind to mu (MOPrs) and delta opioid receptors (DOPrs) with lower affinity and can activate the β-arrestin pathway. To fully exploit the therapeutic potential of dynorphins and reduce potential unwanted effects, increased selectivity for KOPrs combined with reduced activation of the mTOR complex would be favorable. Therefore, we investigated a series of dynorphin B (DynB) variants, substituted in one or two positions with naturally occurring amino acids for differential opioid receptor activation, applying competitive radio binding assays, GTPγS assays, PRESTO-Tango, and Western blotting on single-opioid receptor-expressing cells. Seven DynB derivatives displayed at least 10-fold increased selectivity for KOPrs over either MOPrs or DOPrs. The highest selectivity for KOPrs over MOPrs was obtained with DynB_G3M/Q8H, and the highest selectivity for KOPrs over DOPrs was obtained with DynB_L5S. Increased selectivity for KOPr over MOPr and DOPr was based on a loss of affinity or potency at MOPr and DOPr rather than a higher affinity or potency at KOPr. This suggests that the investigated amino acid exchanges in positions 3, 5, and 8 are of higher importance for binding and activation of MOPr or DOPr than of KOPr. In tests for signal transduction using the GTPγS assay, none of the DynB derivatives displayed increased potency. The three tested variants with substitutions of glycine to methionine in position 3 displayed reduced efficacy and are, therefore, considered partial agonists. The two most promising activating candidates were further investigated for functional selectivity between the G-protein and the β-arrestin pathway, as well as for activation of mTOR. No difference was detected in the respective read-outs, compared to wild-type DynB. Our data indicate that the assessment of affinity to KOPr alone is not sufficient to predict either potency or efficacy of peptidergic agonists on KOPr. Further assessment of downstream pathways is required to allow more reliable predictions of in vivo effects.

The Nobel Prize in Chemistry: past, present, and future of AI in biology

A Comment on the transformative progress of artificial intelligence for structural and protein biology, referencing the 2024 Nobel Prize in Chemistry.

Scalable protein design using optimization in a relaxed sequence space

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.

Exploring "dark-matter" protein folds using deep learning

De novo protein design explores uncharted sequence and structure space to generate novel proteins not sampled by evolution. A main challenge in de novo design involves crafting "designable" structural templates to guide the sequence searches toward adopting target structures. We present a convolutional variational autoencoder that learns patterns of protein structure, dubbed Genesis. We coupled Genesis with trRosetta to design sequences for a set of protein folds and found that Genesis is capable of reconstructing native-like distance and angle distributions for five native folds and three novel, the so-called "dark-matter" folds as a demonstration of generalizability. We used a high-throughput assay to characterize the stability of the designs through protease resistance, obtaining encouraging success rates for folded proteins. Genesis enables exploration of the protein fold space within minutes, unrestricted by protein topologies. Our approach addresses the backbone designability problem, showing that small neural networks can efficiently learn structural patterns in proteins. A record of this paper's transparent peer review process is included in the supplemental information.

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

From De Novo to Xeno: Advancing Macromolecule Design beyond Proteins

Protein synthesis methods have been adapted to incorporate an ever-growing level of non-natural components. Meanwhile, design of de novo protein structure and function has rapidly emerged as a viable capability. Yet, these two exciting trends have yet to intersect in a meaningful way. The ability to perform de novo design with non-proteinogenic components requires that synthesis and computation align on common targets and applications. This perspective examines the state of the art in these areas and identifies specific, consequential applications to advance the field toward generalized macromolecule design.

Protein superfolds are characterised as frustration-free topologies: A case study of pure parallel β-sheet topologies

A protein superfold is a type of protein fold that is observed in at least three distinct, non-homologous protein families. Structural classification studies have revealed a limited number of prevalent superfolds alongside several infrequent occurring folds, and in α/β type superfolds, the C-terminal β-strand tends to favor the edge of the β-sheet, while the N-terminal β-strand is often found in the middle. The reasons behind these observations, whether they are due to evolutionary sampling bias or physical interactions, remain unclear. This article offers a physics-based explanation for these observations, specifically for pure parallel β-sheet topologies. Our investigation is grounded in several established structural rules that are based on physical interactions. We have identified "frustration-free topologies" which are topologies that can satisfy all the rules simultaneously. In contrast, topologies that cannot are termed "frustrated topologies." Our findings reveal that frustration-free topologies represent only a fraction of all theoretically possible patterns, these topologies strongly favor positioning the C-terminal β-strand at the edge of the β-sheet and the N-terminal β-strand in the middle, and there is significant overlap between frustration-free topologies and superfolds. We also used a lattice protein model to thoroughly investigate sequence-structure relationships. Our results show that frustration-free structures are highly designable, while frustrated structures are poorly designable. These findings suggest that superfolds are highly designable due to their lack of frustration, and the preference for positioning C-terminal β-strands at the edge of the β-sheet is a direct result of frustration-free topologies. These insights not only enhance our understanding of sequence-structure relationships but also have significant implications for de novo protein design.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

De Novo Designed Cell-Penetrating Peptide Self-Assembly Featuring Distinctive Tertiary Structure

Recent attention has focused on the de novo design of proteins, paralleling advancements in biopharmaceuticals. Achieving protein designs with both structure and function poses a significant challenge, particularly considering the importance of quaternary structures, such as oligomers, in protein function. The cell penetration properties of peptides are of particular interest as they involve the penetration of large molecules into cells. We previously suggested a link between the oligomerization propensity of amphipathic peptides and their cell penetration abilities, yet concrete evidence at cellular-relevant concentrations was lacking due to oligomers' instability. In this study, we sought to characterize oligomerization states using various techniques, including X-ray crystallography, acceptor photobleaching Förster resonance energy transfer (FRET), native mass spectrometry (MS), and differential scanning calorimetry (DSC), while exploring the function related to oligomer status. X-ray crystallography revealed the atomic structures of oligomers formed by LK-3, a bis-disulfide bridged dimer with amino acid sequence LKKLCLKLKKLCKLAG, and its derivatives, highlighting the formation of hexamers, specifically the trimer of dimers, which exhibited a stable hydrophobic core. FRET experiments showed that LK-3 oligomer formation was associated with cell penetration. Native MS confirmed higher-order oligomers of LK-3, while an intriguing finding was the enhanced cell-penetrating capability of a 1:1 mixture of l/d-peptide dimers compared to pure enantiomers. DSC analysis supported the notion that this enantiomeric mixture promotes the formation of functional oligomers, crucial for cell penetration. In conclusion, our study provides direct evidence that amphipathic peptide LK-3 forms oligomers at low nanomolar concentrations, underscoring their significance in cell penetration behavior.

Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer

The prefusion conformation of human metapneumovirus fusion protein (hMPV Pre-F) is critical for eliciting the most potent neutralizing antibodies and is the preferred immunogen for an efficacious vaccine against hMPV respiratory infections. Here we show that an additional cleavage event in the F protein allows closure and correct folding of the trimer. We therefore engineered the F protein to undergo double cleavage, which enabled screening for Pre-F stabilizing substitutions at the natively folded protomer interfaces. To identify these substitutions, we developed an AI convolutional classifier that successfully predicts complex polar interactions often overlooked by physics-based methods and visual inspection. The combination of additional processing, stabilization of interface regions and stabilization of the membrane-proximal stem, resulted in a Pre-F protein vaccine candidate without the need for a heterologous trimerization domain that exhibited high expression yields and thermostability. Cryo-EM analysis shows the complete ectodomain structure, including the stem, and a specific interaction of the newly identified cleaved C-terminus with the adjacent protomer. Importantly, the protein induces high and cross-neutralizing antibody responses resulting in near complete protection against hMPV challenge in cotton rats, making the highly stable, double-cleaved hMPV Pre-F trimer an attractive vaccine candidate.

Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting

In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction ("in silico melting"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

Insights into PCSK9-LDLR Regulation and Trafficking via the Differential Functions of MHC-I Proteins HFE and HLA-C

PCSK9 is implicated in familial hypercholesterolemia via targeting the cell surface PCSK9-LDLR complex toward lysosomal degradation. The M2 repeat in the PCSK9's C-terminal domain is essential for its extracellular function, potentially through its interaction with an unidentified "protein X". The M2 repeat was recently shown to bind an R-x-E motif in MHC-class-I proteins (implicated in the immune system), like HLA-C, and causing their lysosomal degradation. These findings suggested a new role of PCSK9 in the immune system and that HLA-like proteins could be "protein X" candidates. However, the participation of each member of the MHC-I protein family in this process and their regulation of PCSK9's function have yet to be determined. Herein, we compared the implication of MHC-I-like proteins such as HFE (involved in iron homeostasis) and HLA-C on the extracellular function of PCSK9. Our data revealed that the M2 domain regulates the intracellular sorting of the PCSK9-LDLR complex to lysosomes, and that HFE is a new target of PCSK9 that inhibits its activity on the LDLR, whereas HLA-C enhances its function. This work suggests the potential modulation of PCSK9's functions through interactions of HFE and HLA-C.

Mutational dissection of a hole hopping route in a lytic polysaccharide monooxygenase (LPMO)

Oxidoreductases have evolved tyrosine/tryptophan pathways that channel highly oxidizing holes away from the active site to avoid damage. Here we dissect such a pathway in a bacterial LPMO, member of a widespread family of C-H bond activating enzymes with outstanding industrial potential. We show that a strictly conserved tryptophan is critical for radical formation and hole transference and that holes traverse the protein to reach a tyrosine-histidine pair in the protein's surface. Real-time monitoring of radical formation reveals a clear correlation between the efficiency of hole transference and enzyme performance under oxidative stress. Residues involved in this pathway vary considerably between natural LPMOs, which could reflect adaptation to different ecological niches. Importantly, we show that enzyme activity is increased in a variant with slower radical transference, providing experimental evidence for a previously postulated trade-off between activity and redox robustness.

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

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

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

De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.

Engineering strategies and challenges of endolysin as an antibacterial agent against Gram-negative bacteria

Bacteriophage endolysin is a novel antibacterial agent that has attracted much attention in the prevention and control of drug-resistant bacteria due to its unique mechanism of hydrolysing peptidoglycans. Although endolysin exhibits excellent bactericidal effects on Gram-positive bacteria, the presence of the outer membrane of Gram-negative bacteria makes it difficult to lyse them extracellularly, thus limiting their application field. To enhance the extracellular activity of endolysin and facilitate its crossing through the outer membrane of Gram-negative bacteria, researchers have adopted physical, chemical, and molecular methods. This review summarizes the characterization of endolysin targeting Gram-negative bacteria, strategies for endolysin modification, and the challenges and future of engineering endolysin against Gram-negative bacteria in clinical applications, to promote the application of endolysin in the prevention and control of Gram-negative bacteria.

Subtle Structural Changes across the Boundary between A2AR/A2BR Dual Antagonism and A2BR Antagonism: A Novel Class of 2-Aminopyrimidine-Based Derivatives

Aberrantly elevated adenosine in the tumor microenvironment exerts its immunosuppressive functions through adenosine receptors A2AR and A2BR. Antagonism of A2AR and A2BR has the potential to suppress tumor growth. Herein, we report a systemic assessment of the effects of an indole modification at position 4, 5, 6, or 7 on both A2AR/A2BR activity and selectivity of novel 2-aminopyrimidine compounds. Substituting indole at the 4-/5-position produced potent A2AR/A2BR dual antagonism, whereas the 6-position of indole substitution gave highly selective A2BR antagonism. Molecular dynamics simulation showed that the 5-cyano compound 7ai had a lower binding free energy than the 6-cyano compound 7aj due to water-bridged hydrogen bond interactions with E169 or F168 in A2AR. Of note, dual A2AR/A2BR antagonism by compound 7ai can profoundly promote the activation and cytotoxic function of T cells. This work provided a strategy for obtaining novel dual A2AR/A2BR or A2BR antagonists by fine-tuning structural modification.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Design of complicated all-α protein structures

A wide range of de novo protein structure designs have been achieved, but the complexity of naturally occurring protein structures is still far beyond these designs. Here, to expand the diversity and complexity of de novo designed protein structures, we sought to develop a method for designing 'difficult-to-describe' α-helical protein structures composed of irregularly aligned α-helices like globins. Backbone structure libraries consisting of a myriad of α-helical structures with five or six helices were generated by combining 18 helix-loop-helix motifs and canonical α-helices, and five distinct topologies were selected for de novo design. The designs were found to be monomeric with high thermal stability in solution and fold into the target topologies with atomic accuracy. This study demonstrated that complicated α-helical proteins are created using typical building blocks. The method we developed will enable us to explore the universe of protein structures for designing novel functional proteins.

MinD-RNase E interplay controls localization of polar mRNAs in E. coli

The E. coli transcriptome at the cell's poles (polar transcriptome) is unique compared to the membrane and cytosol. Several factors have been suggested to mediate mRNA localization to the membrane, but the mechanism underlying polar localization of mRNAs remains unknown. Here, we combined a candidate system approach with proteomics to identify factors that mediate mRNAs localization to the cell poles. We identified the pole-to-pole oscillating protein MinD as an essential factor regulating polar mRNA localization, although it is not able to bind RNA directly. We demonstrate that RNase E, previously shown to interact with MinD, is required for proper localization of polar mRNAs. Using in silico modeling followed by experimental validation, the membrane-binding site in RNase E was found to mediate binding to MinD. Intriguingly, not only does MinD affect RNase E interaction with the membrane, but it also affects its mode of action and dynamics. Polar accumulation of RNase E in ΔminCDE cells resulted in destabilization and depletion of mRNAs from poles. Finally, we show that mislocalization of polar mRNAs may prevent polar localization of their protein products. Taken together, our findings show that the interplay between MinD and RNase E determines the composition of the polar transcriptome, thus assigning previously unknown roles for both proteins.

De novo protein design-From new structures to programmable functions

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

Protein sequence design on given backbones with deep learning

Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

De novo design of drug-binding proteins with predictable binding energy and specificity

The de novo design of small-molecule-binding proteins has seen exciting recent progress; however, the ability to achieve exquisite affinity for binding small molecules while tuning specificity has not yet been demonstrated directly from computation. Here, we develop a computational procedure that results in the highest affinity binders to date with predetermined relative affinities, targeting a series of PARP1 inhibitors. Two of four designed proteins bound with affinities ranging from < 5 nM to low μM, in a predictable manner. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free-energy calculations performed directly on the designed models are in excellent agreement with the experimentally measured affinities, suggesting that the de novo design of small-molecule-binding proteins with tuned interaction energies is now feasible entirely from computation. We expect these methods to open many opportunities in biomedicine, including rapid sensor development, antidote design, and drug delivery vehicles.