Top-down design of protein architectures with reinforcement learning

Protein identification using Cryo-EM and artificial intelligence guides improved sample purification

Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However, standard purification strategies often encounter challenges, such as unintended co-purification of contaminants alongside the target protein. This issue is particularly problematic for self-assembling protein nanomaterials, where unexpected geometries may reflect novel assembly states, cross-contamination, or native proteins originating from the expression host. Here, we used an automated structure-to-sequence pipeline to first identify an unknown co-purifying protein found in several purified designed protein samples. By integrating cryo-electron microscopy (Cryo-EM), ModelAngelo's sequence-agnostic model-building, and Protein BLAST, we identified the contaminant as dihydrolipoamide succinyltransferase (DLST). This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold 3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical methods. The identification informed subsequent modifications to our purification protocol, which successfully excluded DLST from future preparations. To explore the potential broader utility of this approach, we benchmarked four computational methods for DLST identification across varying resolution ranges. This study demonstrates the successful application of a structure-to-sequence protein identification workflow, integrating Cryo-EM, ModelAngelo, Protein BLAST, and AlphaFold 3 predictions, to identify and ultimately help guide the removal of DLST from sample purification efforts. It highlights the potential of combining Cryo-EM with AI-driven tools for accurate protein identification and addressing purification challenges across diverse contexts in protein science.

A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering

The recent success of large language models (LLMs) in performing natural language processing tasks has increased interest in applying generative artificial intelligence (AI) to scientific research. However, a common problem of LLMs is their tendency to produce inaccurate and sometimes "hallucinated" outputs. Here, we established a generative AI tool, NanoSafari, to automatically extract knowledge from the biomedical nanoscience literature and address scientific queries. We developed the Grouped Iterative Validation based Information Extraction (GIVE) method to extract contextual information on nanoparticle characteristics from >20,000 published articles and established a database that was incorporated into the generative LLM to provide accurate nanomaterial design parameters. Blinded evaluation by biomedical nanoscientists showed that NanoSafari outperformed the baseline model in providing more reliable parameters for nanomaterial design tasks, as further validated by bench experiments. Together, these findings demonstrate the utility of AI-based methods for automated learning from "real-world" published work to provide accurate and reliable scientific references for biomaterial and bioengineering applications.

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

An Improved Monte Carlo Method for Quantitative Analysis of Transparency Degradation Caused by Corneal Edema

Purpose

To address the impact of corneal edema on transparency, we proposed an improved Monte Carlo (MC) method that uses a cost-effective and easily applicable approach to extract fibril features. This method aims to enhance computational accuracy and elucidate microscopic mechanisms of corneal transparency.

Methods

Fresh ex vivo porcine corneas were immersed in deionized water to simulate various states of corneal edema. For each edema state, we carefully analyzed its transparency and fibrous conditions. The diameter and spatial distribution of corneal fibrils were measured using image recognition algorithms. An improved MC method, analyzing the multiple scattering of each incident light, was proposed to simulate the propagation of incident light in the cornea.

Results

Higher edema reduces transparency. After 10 hours of hydration, corneal water content increased by 7% and transparency decreased by over 30%. Cross-sectional observations indicated that the radius increases from the original 15.1 nm to 16.8 nm, the fibril-to-base material area ratio decreases from the initial 15% to roughly 7%, and the minimum gap between fibrils expanded by 35%. The proposed MC method provides more accurate transparency estimates compared to the direct summation of field theory.

Conclusions

Corneal transparency decreases as a result of multiple factors, including fibril and cornea thickening, variations in the refractive index, and changes in the fibril distribution.

Translational Relevance

The MC method is cost-effective, easily applicable, and accurate in predicting the transparency of corneas with varying water contents. Moreover, this method intuitively demonstrates the scattering of light during the propagation process.

Artificial Intelligence Technology Assists Enzyme Prediction and Rational Design

Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked a technological revolution, infusing new vitality into theoretical studies of enzymology and the advancement of enzyme engineering techniques. This Review outlines the development process and main methods of AI applied in the structural elucidation and functional prediction of enzymes. Furthermore, it emphasizes AI-based rational design of enzymes and provides a detailed exposition of representative AI algorithms and case studies. With the support of AI technology, the comprehension of enzyme structure and function and their relationship will become deeper and more efficient, thereby promoting the widespread application of enzyme engineering in various fields.

AI-driven antibody design with generative diffusion models: current insights and future directions

Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential, optimizing antibodies for superior efficacy presents significant challenges in both monetary and time costs. Recent strides in computational and artificial intelligence (AI), especially generative diffusion models, have begun to address these challenges, offering novel approaches for antibody design. This review delves into specific diffusion-based generative methodologies tailored for antibody design tasks, de novo antibody design, and optimization of complementarity-determining region (CDR) loops, along with their evaluation metrics. We aim to provide an exhaustive overview of this burgeoning field, making it an essential resource for leveraging diffusion-based generative models in antibody design endeavors.

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, they primarily focus on designing static structures. Here we characterize three distinct computationally designed protein assemblies that exhibit unanticipated structural diversity arising from flexibility in their subunits. Cryo-EM single-particle reconstructions and native mass spectrometry reveal two distinct architectures for two assemblies, while six cryo-EM reconstructions for the third likely represent a subset of its solution-phase structures. Structural modeling and molecular dynamics simulations indicate that constrained flexibility within the subunits of each assembly promotes a defined range of architectures rather than nonspecific aggregation. Redesigning the flexible region in one building block rescues the intended monomorphic assembly. These findings highlight structural flexibility as a powerful design principle, enabling exploration of new structural and functional spaces in protein assembly design.

Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects

Nanophotonics, which explores significant light-matter interactions at the nanoscale, has facilitated significant advancements across numerous research fields. A key objective in this area is the design of ultra-compact, high-performance nanophotonic devices to pave the way for next-generation photonics. While conventional brute-force, intuition-based forward design methods have produced successful nanophotonic solutions over the past several decades, recent developments in optimization methods and artificial intelligence offer new potential to expand these capabilities. In this review, we delve into the latest progress in the inverse design of nanophotonic devices, where AI and optimization methods are leveraged to automate and enhance the design process. We discuss representative methods commonly employed in nanophotonic design, including various meta-heuristic algorithms such as trajectory-based, evolutionary, and swarm-based approaches, in addition to adjoint-based optimization. Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. Additionally, we summarize the open-source inverse design tools and commercial foundries. Finally, we provide our perspectives on the current challenges of inverse design, while offering insights into future directions that could further advance this rapidly evolving field.

A Macrocycle-Mediated Protein Cage

Engineered protein cages are of great interest considering their diverse applications in delivery and catalysis. Here, we describe macrocycle-triggered icosahedral cage assembly of a designed β-propeller. Cage assembly was evidenced by small-angle X-ray scattering and X-ray crystallography.

Advances in stress-tolerance elements for microbial cell factories

Microorganisms, particularly extremophiles, have evolved multiple adaptation mechanisms to address diverse stress conditions during survival in unique environments. Their responses to environmental coercion decide not only survival in severe conditions but are also an essential factor determining bioproduction performance. The design of robust cell factories should take the balance of their growing and bioproduction into account. Thus, mining and redesigning stress-tolerance elements to optimize the performance of cell factories under various extreme conditions is necessary. Here, we reviewed several stress-tolerance elements, including acid-tolerant elements, saline-alkali-resistant elements, thermotolerant elements, antioxidant elements, and so on, providing potential materials for the construction of cell factories and the development of synthetic biology. Strategies for mining and redesigning stress-tolerance elements were also discussed. Moreover, several applications of stress-tolerance elements were provided, and perspectives and discussions for potential strategies for screening stress-tolerance elements were made.

Biofuel production from lignocellulose via thermophile-based consolidated bioprocessing

The depletion of fossil fuels and their impact on the environment have led to efforts to develop alternative sustainable fuels. While biofuel derived from lignocellulose is considered a sustainable, renewable, and green energy source, enhancing biofuel production and achieving a cost-effective bioconversion of lignocellulose at existing bio-refineries remains a challenge. Consolidated bioprocessing (CBP) using thermophiles can simplify this operation by integrating multiple processes, such as hydrolytic enzyme production, lignocellulose degradation, biofuel fermentation, and product distillation. This paper reviews recent developments in the conversion of lignocellulose to biofuel using thermophile-based CBP. First, advances in thermostable enzyme and thermophilic lignocellulolytic microorganism discovery and development for lignocellulosic biorefinery use are outlined. Then, several thermophilic CBP candidates and thermophilic microbes engineered to drive CBP of lignocellulose are reviewed. CRISPR/Cas-based genome editing tools developed for thermophiles are also highlighted. The potential applications of the Design-Build-Test-Learn (DBTL) synthetic biology strategy for designing and constructing thermophilic CBP hosts are also discussed in detail. Overall, this review illustrates how to develop highly sophisticated thermophilic CBP hosts for use in lignocellulosic biorefinery applications.

Reconfigurable nanomaterials folded from multicomponent chains of DNA origami voxels

In cells, proteins rapidly self-assemble into sophisticated nanomachines. Bioinspired self-assembly approaches, such as DNA origami, have been used to achieve complex three-dimensional (3D) nanostructures and devices. However, current synthetic systems are limited by low yields in hierarchical assembly and challenges in rapid and efficient reconfiguration between diverse structures. Here, we developed a modular system of DNA origami "voxels" with programmable 3D connections. We demonstrate multifunctional pools of up to 12 unique voxels that can assemble into many shapes, prototyping 50 structures. Programmable switching of local connections between flexible and rigid states achieved rapid and reversible reconfiguration of global structures in three dimensions. Multistep assembly pathways were then explored to increase the yield. Voxels were assembled via flexible chain intermediates into rigid structures, increasing yield up to 100-fold. We envision that foldable chains of DNA origami voxels can achieve increased complexity in reconfigurable nanomaterials, providing modular components for the assembly of nanorobotic systems with future applications in synthetic biology, assembly of inorganic materials, and nanomedicine.

Nanocarriers for intracellular delivery of proteins in biomedical applications: strategies and recent advances

Protein drugs are of great importance in maintaining the normal functioning of living organisms. Indeed, they have been instrumental in combating tumors and genetic diseases for decades. Among these pharmaceutical agents, those that target intracellular components necessitate the use of therapeutic proteins to exert their effects within the targeted cells. However, the use of protein drugs is limited by their short half-life and potential adverse effects in the physiological environment. The advent of nanoparticles offers a promising avenue for prolonging the half-life of protein drugs. This is achieved by encapsulating proteins, thereby safeguarding their biological activity and ensuring precise delivery into cells. This nanomaterial-based intracellular protein drug delivery system mitigates the rapid hydrolysis and unwarranted diffusion of proteins, thereby minimizing potential side effects and circumventing the limitations inherent in traditional techniques like electroporation. This review examines established protein drug delivery systems, including those based on polymers, liposomes, and protein nanoparticles. We delve into the operational principles and transport mechanisms of nanocarriers, discussing the various considerations essential for designing cutting-edge delivery platforms. Additionally, we investigate innovative designs and applications of traditional cytosolic protein delivery systems in medical research and clinical practice, particularly in areas like tumor treatment, gene editing and fluorescence imaging. This review sheds light on the current restrictions of protein delivery systems and anticipates future research avenues, aiming to foster the continued advancement in this field.

Empowering biomedical discovery with AI agents

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

Transformation of peptides to small molecules in medicinal chemistry: Challenges and opportunities

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field.

AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Designed 2D protein crystals as dynamic molecular gatekeepers for a solid-state device

The sensitivity and responsiveness of living cells to environmental changes are enabled by dynamic protein structures, inspiring efforts to construct artificial supramolecular protein assemblies. However, despite their sophisticated structures, designed protein assemblies have yet to be incorporated into macroscale devices for real-life applications. We report a 2D crystalline protein assembly of C98/E57/E66L-rhamnulose-1-phosphate aldolase (CEERhuA) that selectively blocks or passes molecular species when exposed to a chemical trigger. CEERhuA crystals are engineered via cobalt(II) coordination bonds to undergo a coherent conformational change from a closed state (pore dimensions <1 nm) to an ajar state (pore dimensions ~4 nm) when exposed to an HCN(g) trigger. When layered onto a mesoporous silicon (pSi) photonic crystal optical sensor configured to detect HCN(g), the 2D CEERhuA crystal layer effectively blocks interferents that would otherwise result in a false positive signal. The 2D CEERhuA crystal layer opens in selective response to low-ppm levels of HCN(g), allowing analyte penetration into the pSi sensor layer for detection. These findings illustrate that designed protein assemblies can function as dynamic components of solid-state devices in non-aqueous environments.

Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a "spectrum-structure" correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.

Accelerated Engineering of ELP-Based Materials through Hybrid Biomimetic-De Novo Predictive Molecular Design

Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins.

Generative artificial intelligence for de novo protein design

Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example, in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

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.

Artificial structural proteins: Synthesis, assembly and material applications

Structural proteins have evolved over billions of years and offer outstanding mechanical properties, such as resilience, toughness and stiffness. Advances in modular protein engineering, polypeptide modification, and synthetic biology have led to the development of novel biomimetic structural proteins to perform in biomedical and military fields. However, the development of customized structural proteins and assemblies with superior performance remains a major challenge, due to the inherent limitations of biosynthesis, difficulty in mimicking the complexed macroscale assembly, etc. This review summarizes the approaches for the design and production of biomimetic structural proteins, and their chemical modifications for multiscale assembly. Furthermore, we discuss the function tailoring and current applications of biomimetic structural protein assemblies. A perspective of future research is to reveal how the mechanical properties are encoded in the sequences and conformations. This review, therefore, provides an important reference for the development of structural proteins-mimetics from replication of nature to even outperforming nature.

Artificial intelligence-driven design of the assembled major cat allergen Fel d 1 to improve its spatial folding and IgE-reactivity

BACKGROUND

Cat-derived allergens are considered as one of the most common causes of allergic diseases worldwide. Fel d 1 is a major cat allergen and plays an important role in immunoglobulin E (IgE)-reaction diagnosis. However, the two separate chains of Fel d 1 exhibited lower IgE-reactivity than its complete molecule of an assembled form, which makes it difficult to efficiently prepare and limits the application of Fel d 1 in molecular diagnosis of cat allergy.

METHODS

We first applied artificial intelligence (AI) based tool AlphaFold2 to build the 3-dimensional structures of Fel d 1 with different connection modes between two chains, which were evaluated by ERRAT program and were expressed in Escherichia coli. We then calculated the expression ratios of soluble form/inclusion bodies form of optimized Fel d 1. The Circular Dichroism (CD), High Performance Liquid Chromatography-Size Exclusion Chromatography (HPLC-SEC) and reducing/non-reducing SDS-PAGE were performed to characterize the folding status and dimerization of the optimized fusion Fel d 1. The improvement of specific-IgE reactivity to optimized fusion Fel d 1 was investigated by enzyme linked immunosorbent assay (ELISA).

RESULTS

Among several linkers, 2 × GGGGS got the highest scores, with an overall quality factor of 100. The error value of the residues around the junction of 2 × GGGGS was lower than others. It exhibited highest proportion of soluble protein than other Fel d 1 constructs with ERRAT (GGGGS, KK as well as direct fusion Fel d 1). The results of CD and HPLC-SEC showed the consistent folding and dimerization of two fused subunits between the optimized fusion Fel d 1 and previously well-defined direct fusion Fel d 1. The overall IgE-binding absorbance of optimized fusion Fel d 1 tested by ELISA was improved compared with that of the direct fusion Fel d 1.

CONCLUSION

We firstly provided an AI-design strategy to optimize the Fel d 1, which could spontaneously fold into its native-like structure without additional refolding process or eukaryotic folding factors. The improved IgE-binding activity and simplified preparation method could greatly facilitate it to be a robust allergen material for molecular diagnosis of cat allergy.

Integrating cellular electron microscopy with multimodal data to explore biology across space and time

Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

Applying artificial intelligence and computational modeling to nanomedicine

Achieving specific and targeted delivery of nanomedicines to diseased tissues is a major challenge. This is because the process of designing, formulating, testing, and selecting a nanoparticle delivery vehicle for a specific disease target is governed by complex multivariate interactions. Computational modeling and artificial intelligence are well-suited for analyzing and modeling large multivariate datasets in short periods of time. Computational approaches can be applied to help design nanomedicine formulations, interpret nanoparticle-biological interactions, and create models from high-throughput screening techniques to improve the selection of the ideal nanoparticle carrier. In the future, many steps in the nanomedicine development process will be done computationally, reducing the number of experiments and time needed to select the ideal nanomedicine formulation.

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.

Programmable synthetic receptors: the next-generation of cell and gene therapies

Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.

Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination

Immunogen design approaches aim to control the specificity and quality of antibody responses elicited by next-generation vaccines. Here, we use computational protein design to generate a nanoparticle vaccine platform based on the receptor-binding domain (RBD) of influenza hemagglutinin (HA) that enables precise control of antigen conformation and spacing. HA RBDs are presented as either monomers or native-like closed trimers that are connected to the underlying nanoparticle by a rigid linker that is modularly extended to precisely control antigen spacing. Nanoparticle immunogens with decreased spacing between trimeric RBDs elicit antibodies with improved hemagglutination inhibition and neutralization potency as well as binding breadth across diverse H1 HAs. Our "trihead" nanoparticle immunogen platform provides insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used in next-generation vaccines against influenza and other viruses.

The role and pathway of VQ family in plant growth, immunity, and stress response

MAIN CONCLUSION

This review provides a detailed description of the function and mechanism of VQ family gene, which is helpful for further research and application of VQ gene resources to improve crops. Valine-glutamine (VQ) motif-containing proteins are a large class of transcriptional regulatory cofactors. VQ proteins have their own unique molecular characteristics. Amino acids are highly conserved only in the VQ domain, while other positions vary greatly. Most VQ genes do not contain introns and the length of their proteins is less than 300 amino acids. A majority of VQ proteins are predicted to be localized in the nucleus. The promoter of many VQ genes contains stress or growth related elements. Segment duplication and tandem duplication are the main amplification mechanisms of the VQ gene family in angiosperms and gymnosperms, respectively. Purification selection plays a crucial role in the evolution of many VQ genes. By interacting with WRKY, MAPK, and other proteins, VQ proteins participate in the multiple signaling pathways to regulate plant growth and development, as well as defense responses to biotic and abiotic stresses. Although there have been some reports on the VQ gene family in plants, most of them only identify family members, with little functional verification, and there is also a lack of complete, detailed, and up-to-date review of research progress. Here, we comprehensively summarized the research progress of VQ genes that have been published so far, mainly including their molecular characteristics, biological functions, importance of VQ motif, and working mechanisms. Finally, the regulatory network and model of VQ genes were drawn, a precise molecular breeding strategy based on VQ genes was proposed, and the current problems and future prospects were pointed out, providing a powerful reference for further research and utilization of VQ genes in plant improvement.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

Deep learning for metabolic pathway design

The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

Many naturally occurring protein assemblies have dynamic structures that allow them to perform specialized functions. For example, clathrin coats adopt a wide variety of architectures to adapt to vesicular cargos of various sizes. Although computational methods for designing novel self-assembling proteins have advanced substantially over the past decade, most existing methods focus on designing static structures with high accuracy. Here we characterize the structures of three distinct computationally designed protein assemblies that each form multiple unanticipated architectures, and identify flexibility in specific regions of the subunits of each assembly as the source of structural diversity. Cryo-EM single-particle reconstructions and native mass spectrometry showed that only two distinct architectures were observed in two of the three cases, while we obtained six cryo-EM reconstructions that likely represent a subset of the architectures present in solution in the third case. Structural modeling and molecular dynamics simulations indicated that the surprising observation of a defined range of architectures, instead of non-specific aggregation, can be explained by constrained flexibility within the building blocks. Our results suggest that deliberate use of structural flexibility as a design principle will allow exploration of previously inaccessible structural and functional space in designed protein assemblies.

Engineering RNA export for measurement and manipulation of living cells

A system for programmable export of RNA molecules from living cells would enable both non-destructive monitoring of cell dynamics and engineering of cells capable of delivering executable RNA programs to other cells. We developed genetically encoded cellular RNA exporters, inspired by viruses, that efficiently package and secrete cargo RNA molecules from mammalian cells within protective nanoparticles. Exporting and sequencing RNA barcodes enabled non-destructive monitoring of cell population dynamics with clonal resolution. Further, by incorporating fusogens into the nanoparticles, we demonstrated the delivery, expression, and functional activity of exported mRNA in recipient cells. We term these systems COURIER (controlled output and uptake of RNA for interrogation, expression, and regulation). COURIER enables measurement of cell dynamics and establishes a foundation for hybrid cell and gene therapies based on cell-to-cell delivery of RNA.

Protein engineering of pores for separation, sensing, and sequencing

Proteins are critical to cellular function and survival. They are complex molecules with precise structures and chemistries, which allow them to serve diverse functions for maintaining overall cell homeostasis. Since the discovery of the first enzyme in 1833, a gamut of advanced experimental and computational tools has been developed and deployed for understanding protein structure and function. Recent studies have demonstrated the ability to redesign/alter natural proteins for applications in industrial processes of interest and to make customized, novel synthetic proteins in the laboratory through protein engineering. We comprehensively review the successes in engineering pore-forming proteins and correlate the amino acid-level biochemistry of different pore modification strategies to the intended applications limited to nucleotide/peptide sequencing, single-molecule sensing, and precise molecular separations.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises 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.

Adjuvant physiochemistry and advanced nanotechnology for vaccine development

Vaccines comprising innovative adjuvants are rapidly reaching advanced translational stages, such as the authorized nanotechnology adjuvants in mRNA vaccines against COVID-19 worldwide, offering new strategies to effectively combat diseases threatening human health. Adjuvants are vital ingredients in vaccines, which can augment the degree, extensiveness, and longevity of antigen specific immune response. The advances in the modulation of physicochemical properties of nanoplatforms elevate the capability of adjuvants in initiating the innate immune system and adaptive immunity, offering immense potential for developing vaccines against hard-to-target infectious diseases and cancer. In this review, we provide an essential introduction of the basic principles of prophylactic and therapeutic vaccination, key roles of adjuvants in augmenting and shaping immunity to achieve desired outcomes and effectiveness, and the physiochemical properties and action mechanisms of clinically approved adjuvants for humans. We particularly focus on the preclinical and clinical progress of highly immunogenic emerging nanotechnology adjuvants formulated in vaccines for cancer treatment or infectious disease prevention. We deliberate on how the immune system can sense and respond to the physicochemical cues (e.g., chirality, deformability, solubility, topology, and chemical structures) of nanotechnology adjuvants incorporated in the vaccines. Finally, we propose possible strategies to accelerate the clinical implementation of nanotechnology adjuvanted vaccines, such as in-depth elucidation of nano-immuno interactions, antigen identification and optimization by the deployment of high-dimensional multiomics analysis approaches, encouraging close collaborations among scientists from different scientific disciplines and aggressive exploration of novel nanotechnologies.

Current progress, challenges, and future perspectives of language models for protein representation and protein design

The sequence-structure-function paradigm of protein is the basis of molecular biology. What is the underlying mechanism of such sequence and structure/function corresponding relationship? We reviewed the methods for protein representation and protein design. With these protein representation models, we can accurately predict many properties of proteins, such as stability and binding affinity. Progen, Chroma, RF Diffusion, SCUBA, and other protein design models have demonstrated how human-designed artificial proteins can have desired biological functions. The protein design will revolutionize drug development. And more efficient artificial enzymes that break down industrial waste or plastics will contribute to carbon neutrality. We also discussed the three greatest challenges of protein design in future and possible solutions.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination

Immunogen design approaches aim to control the specificity and quality of antibody responses to enable the creation of next-generation vaccines with improved potency and breadth. However, our understanding of the relationship between immunogen structure and immunogenicity is limited. Here we use computational protein design to generate a self-assembling nanoparticle vaccine platform based on the head domain of influenza hemagglutinin (HA) that enables precise control of antigen conformation, flexibility, and spacing on the nanoparticle exterior. Domain-based HA head antigens were presented either as monomers or in a native-like closed trimeric conformation that prevents exposure of trimer interface epitopes. These antigens were connected to the underlying nanoparticle by a rigid linker that was modularly extended to precisely control antigen spacing. We found that nanoparticle immunogens with decreased spacing between closed trimeric head antigens elicited antibodies with improved hemagglutination inhibition (HAI) and neutralization potency as well as binding breadth across diverse HAs within a subtype. Our "trihead" nanoparticle immunogen platform thus enables new insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used to generate next-generation vaccines against influenza and other viruses.