A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

De novo design of ATPase based on a blueprint optimized for harboring the P-loop motif

De novo design of proteins has seen remarkable recent progress and has provided understanding of folding and functional expression. However, rationally creating enzymes with high activity comparable to most naturally occurring enzymes remains challenging. Here, we attempted to design an ATPase de novo, through the exploration of an optimal backbone blueprint to incorporate a conserved phosphate binding motif, the P-loop, into designed structures. The designed protein, based on the identified blueprint, was found to be a monomer with high thermal stability and exhibited ATPase ability. The crystal structure was closely matched to the design model, both at the overall structure level and within the P-loop motif. Interestingly, AlphaFold 2 was not able to predict the designed structure accurately, indicating the difficulties of predicting folded structures for novel amino acid sequences. Remarkably, the designed protein exhibited ATPase ability even at temperatures around 100°C, with significantly increased activity. However, the ATPase activity was still not comparable to those of naturally occurring enzymes. This suggests that the P-loop motif alone is insufficient to achieve the high ATPase activity seen in naturally occurring enzymes, indicating that other structural components-such as a binding pocket optimized for the adenine or ribose moieties of ATP, additional catalytic residues, or structural dynamics that facilitate hydrolysis-are necessary to reach such activity levels.

Construction and cytotoxicity evaluation of peptide nanocarriers based on coiled-coil structures with a cyclic β-amino acid at the knob-into-hole interaction site

Peptides are highly attractive as nanocarriers for drug delivery and other biomedical applications due to their unique combination of biocompatibility, efficacy, safety, and versatility-qualities that are difficult to achieve with other nanocarrier types. Particularly promising in this context are peptide foldamers containing non-canonical residues, which can yield nanostructures with diverse physicochemical properties. Additionally, the introduction of non-proteinogenic amino acids into the sequence enhances conformational stability and resistance to proteolysis, critical features for bioapplications. In this article, we report the development of novel foldameric bundles based on a coiled-coil structure incorporating trans-(1S,2S)-2-aminocyclopentanecarboxylic acid (trans-ACPC) at the key interacting site. We also provide both theoretical and experimental analyses of how this cyclic β-residue affects the thermodynamic and proteolytic stability, oligomerization state, and encapsulation properties of the resulting foldamers compared to standard coiled-coils. Additionally, we assessed the cytotoxicity of these foldamers using the MTT assay on 3T3 cells. The results demonstrate that neither the foldamers nor trans-ACPC exhibit toxic effects on the 3T3 cell line, highlighting their potential as safe and effective nanocarriers.

De Novo Design of Parallel and Antiparallel A3B3 Heterohexameric α-Helical Barrels

The de novo design of α-helical coiled-coil peptides is advanced. Using established sequence-to-structure relationships, it is possible to generate various coiled-coil assemblies with predictable numbers and orientations of helices. Here, we target new assemblies, namely, A3B3 heterohexamer α-helical barrels. These designs are based on pairs of sequences with three heptad repeats (abcdefg), programmed with a = Leu, d = Ile, e = Ala, and g = Ser, and b = c = Glu to make the acidic (A) chains and b = c = Lys in the basic (B) chains. These design rules ensure that the desired oligomeric state and stoichiometry are readily achieved. However, controlling the orientation of neighboring helices (parallel or antiparallel) is less straightforward. Surprisingly, we find that assembly and helix orientation are sensitive to the length of the overhang between helices. To study this, cyclically permutated peptide sequences with three heptad repeats (the register) in the peptide sequences were analyzed. Peptides starting at g (g-register) form a parallel 6-helix barrel in solution and in an X-ray crystal structure, whereas the b- and c-register peptides form an antiparallel complex. In lieu of experimental X-ray structures for b- and c-register peptides, AlphaFold-Multimer is used to predict atomistic models. However, considerably more sampling than the default value is required to match the models and the experimental data, as many confidently predicted and plausible models are generated with incorrect helix orientations. This work reveals the previously unknown influence of the heptad register on helical overhang and the orientation of α-helical coiled-coil peptides and provides insights for the modeling of oligopeptide coiled-coil complexes with AlphaFold.

Bottom-up design of peptide shapes in water using oligomers of N-methyl-l/d-alanine

De novo design of peptide shapes is of great interest in biomolecular science since the local peptide shapes formed by a short peptide chain in the proteins are often key to biological activities. Here, we show that the de novo design of peptide shapes with sub-nanometer conformational control can be realized using peptides consisting of N-methyl-l-alanine and N-methyl-d-alanine residues. The conformation of N-methyl-l/d-alanine residue is largely fixed because of the restricted bond rotation and hence can serve as a scaffold on which we can build a peptide into a designed shape. The local shape control by per-residue conformational restriction by torsional strains starkly contrasts with the global shape stabilization of proteins based on many remote interactions. The oligomers allow the bottom-up design of diverse peptide shapes with a small number of amino acid residues and would offer unique opportunities to realize the de novo design of biofunctional molecules.

Rational Design Principles for De Novo α-Helical Peptide Barrels with Dynamic Conductive Channels

Despite advances in peptide and protein design, the rational design of membrane-spanning peptides that form conducting channels remains challenging due to our imperfect understanding of the sequence-to-structure relationships that drive membrane insertion, assembly, and conductance. Here, we describe the design and computational and experimental characterization of a series of coiled coil-based peptides that form transmembrane α-helical barrels with conductive channels. Through a combination of rational and computational design, we obtain barrels with 5 to 7 helices, as characterized in detergent micelles. In lipid bilayers, these peptide assemblies exhibit two conductance states with relative populations dependent on the applied potential: (i) low-conductance states that correlate with variations in the designed amino-acid sequences and modeled coiled-coil barrel geometries, indicating stable transmembrane α-helical barrels; and (ii) high-conductance states in which single channels change size in discrete steps. Notably, the high-conductance states are similar for all peptides in contrast to the low-conductance states. This indicates the formation of large, dynamic channels, as observed in natural barrel-stave peptide channels. These findings establish rational routes to design and tune functional membrane-spanning peptide channels with specific conductance and geometry.

Molecular modification and preliminary application of a novel mannanase from Alkalihalobacillus hemicellulosilyticus

Mannooligosaccharides (MOS) are high-quality prebiotic components, and enzymatic production of MOS is the most efficient, simple, and environmentally friendly method. Mannanase is the key enzyme involved in the decomposition of mannan for MOS production. However, there are currently challenges in obtaining thermally stable Mannanase. In this study, a novel thermo-alkaline mannanase, ManB085, was discovered from Alkalihalobacillus hemicellulosilyticus and heterologously expressed. The optimal temperature and pH of this enzyme are 75 °C and 10, respectively, and it remains stable at pH 7-12 and temperatures below 50 °C. Through rational design, a mutant ManB085M, with the non-catalytic domain truncated, was obtained, showing significantly improved thermal stability. The half-life of the mutant at 75 °C is 12.3 times that of the wild type, and the T50 increased by 23 °C. Notably, the enzyme can also produce MOS by hydrolyzing hemicellulose in coffee grounds. To enhance the MOS production capability, a Carbohydrate-Binding Module (CBM) was added to the C-terminus of ManB085M, significantly improving its ability to extract MOS from coffee grounds. This study proposes a novel mannanase with significant potential in MOS production.

Advances in designed bionanomolecular assemblies for biotechnological and biomedical applications

Recent advances in protein engineering have revolutionized the design of bionanomolecular assemblies for functional therapeutic and biotechnological applications. This review highlights the progress in creating complex protein architectures, encompassing both finite and extended assemblies. AI tools, including AlphaFold, RFDiffusion, and ProteinMPNN, have significantly enhanced the scalability and success of de novo designs. Finite assemblies, like nanocages and coiled-coil-based structures, enable precise molecular encapsulation or functional protein domain presentation. Extended assemblies, including filaments and 2D/3D lattices, offer unparalleled structural versatility for applications such as vaccine development, responsive biomaterials, and engineered cellular scaffolds. The convergence of artificial intelligence-driven design and experimental validation promises strong acceleration of the development of tailored protein assemblies, offering new opportunities in synthetic biology, materials science, biotechnology, and biomedicine.

Controlling outer-sphere solvent reorganization energy to turn on or off the function of artificial metalloenzymes

Metalloenzymes play essential roles in biology. However, unraveling how outer-sphere interactions can be predictably controlled to influence their functions remains a significant challenge. Inspired by Cu enzymes, we demonstrate how variations in the primary, secondary, and outer coordination-sphere interactions of de novo designed artificial copper proteins (ArCuPs) within trimeric (3SCC) and tetrameric (4SCC) self-assemblies-featuring a trigonal Cu(His)3 and a square pyramidal Cu(His)4(OH2) coordination-influence their catalytic and electron transfer properties. While 3SCC electrocatalyzes C-H oxidation, 4SCC does not. CuI-3SCC reacts more rapidly with H2O2 than O2, whereas 4SCC is less active. Electron transfer, reorganization energies, and extended H2O-mediated hydrogen bonding patterns provide insights into the observed reactivity differences. The inactivity of 4SCC is attributed to a significant solvent reorganization energy barrier mediated by a specific His---Glu hydrogen bond. When this hydrogen bond is disrupted, the solvent reorganization energy is reduced, and C-H peroxidation activity is restored.

Engineering microbial cell factories by multiplexed spatiotemporal control of cellular metabolism: Advances, challenges, and future perspectives

Generally, the metabolism in microbial organism is an intricate, spatiotemporal process that emerges from gene regulatory networks, which affects the efficiency of product biosynthesis. With the coming age of synthetic biology, spatiotemporal control systems have been explored as versatile strategies to promote product biosynthesis at both spatial and temporal levels. Meanwhile, the designer synthetic compartments provide new and promising approaches to engineerable spatiotemporal control systems to construct high-performance microbial cell factories. In this article, we comprehensively summarize recent developments in spatiotemporal control systems for tailoring advanced cell factories, and illustrate how to apply spatiotemporal control systems in different microbial species with desired applications. Future challenges of spatiotemporal control systems and perspectives are also discussed.

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

Protein design accelerates the development and application of optogenetic tools

Optogenetics has substantially enhanced our understanding of biological processes by enabling high-precision tracking and manipulation of individual cells. It relies on photosensitive proteins to monitor and control cellular activities, thereby paving the way for significant advancements in complex system research. Photosensitive proteins play a vital role in the development of optogenetics, facilitating the establishment of cutting-edge methods. Recent breakthroughs in protein design have opened up opportunities to develop protein-based tools that can precisely manipulate and monitor cellular activities. These advancements will significantly accelerate the development and application of optogenetic tools. This article emphasizes the pivotal role of protein design in the development of optogenetic tools, offering insights into potential future directions. We begin by providing an introduction to the historical development and fundamental principles of optogenetics, followed by an exploration of the operational mechanisms of key photosensitive domains, which includes clarifying the conformational changes they undergo in response to light, such as allosteric modulation and dimerization processes. Building on this foundation, we reveal the development of protein design tools that will enable the creation of even more sophisticated optogenetic techniques.

Confinement and Catalysis within De Novo Designed Peptide Barrels

De novo protein design has advanced such that many peptide assemblies and protein structures can be generated predictably and quickly. The drive now is to bring functions to these structures, for example, small-molecule binding and catalysis. The formidable challenge of binding and orienting multiple small molecules to direct chemistry is particularly important for paving the way to new functionalities. To address this, here we describe the design, characterization, and application of small-molecule:peptide ternary complexes in aqueous solution. This uses α-helical barrel (αHB) peptide assemblies, which comprise 5 or more α helices arranged around central channels. These channels are solvent accessible, and their internal dimensions and chemistries can be altered predictably. Thus, αHBs are analogous to "molecular flasks" made in supramolecular, polymer, and materials chemistry. Using Förster resonance energy transfer as a readout, we demonstrate that specific αHBs can accept two different organic dyes, 1,6-diphenyl-1,3,5-hexatriene and Nile red, in close proximity. In addition, two anthracene molecules can be accommodated within an αHB to promote anthracene photodimerization. However, not all ternary complexes are productive, either in energy transfer or photodimerization, illustrating the control that can be exerted by judicious choice and design of the αHB.

Exchange, promiscuity, and orthogonality in de novo designed coiled-coil peptide assemblies

De novo protein design is delivering new peptide and protein structures at a rapid pace. Many of these synthetic polypeptides form well-defined and hyperthermal-stable structures. Generally, however, less is known about the dynamic properties of the de novo designed structures. Here, we explore one aspect of dynamics in a series of de novo coiled-coil peptide assemblies: namely, peptide exchange within and between different oligomers from dimers through to heptamers. First, we develop a fluorescence-based reporter assay for peptide exchange that is straightforward to implement, and, thus, would be useful to others examining similar systems. We apply this assay to explore both homotypic exchange within single species, and heterotypic exchange between coiled coils of different oligomeric states. For the former, we provide a detailed study for a dimeric coiled coil, CC-Di, finding a half-life for exchange of 4.2 ± 0.3 minutes at a peptide concentration of 200 μM. Interestingly, more broadly when assessing exchange across all of the oligomeric states, we find that some of the designs are faithful and only undergo homotypic strand exchange, whereas others are promiscuous and exchange to form unexpected hetero-oligomers. Finally, we develop two design strategies to improve the orthogonality of the different oligomers: (i) using alternate positioning of salt bridge interactions; and (ii) incorporating non-canonical repeats into the designed sequences. In so doing, we reconcile the promiscuity and deliver a set of faithful homo-oligomeric de novo coiled-coil peptides. Our findings have implications for the application of these and other coiled coils as modules in chemical and synthetic biology.

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.

Protein mimics of fusion core from SARS-CoV-1 can inhibit SARS-CoV-2 entry

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the genus Betacoronavirus (subgenus Sarbecovirus) and shares significant genomic and phylogenetic similarities with severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1). SARS-CoV-2 infection occurs through membrane fusion between the virus and host cell membranes, which is facilitated by the spike glycoprotein subunit 2 (S2). The folding of three heptad-repeat regions 1 (HR1) into a central trimeric core structure, along with the binding of three heptad-repeat regions 2 (HR2) in an antiparallel manner within the groove formed between the HR1 regions, which provides the driving force for membrane fusion. In this study, trimeric and monomeric six-helix bundles (6HB) were created by combining various truncations of the sequences from SARS-CoV-2 HR1 and HR2. In addition, monomeric five-helix bundles (5HB) were constructed using a similar method. Finally, we demonstrated a protein mimic, 5HB_V1 (from SARS-CoV-1), that exhibits activity in inhibiting SARS-CoV-2. These findings suggest a strategy to design monomeric 6HB and 5HB based on the SARS-CoV-2 fusion core: maintain the flanking sequences outside the α-helix region in HR2 and introduce point mutations to enhance hydrogen bonding between the helix bundles. The 5HB could be a target for designing new inhibitors against SARS-CoV-1 and SARS-CoV-2.

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

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.

Advancing sustainable biotechnology through protein engineering

The push for industrial sustainability benefits from the use of enzymes as a replacement for traditional chemistry. Biological catalysts, especially those that have been engineered for increased activity, stability, or novel function, and are often greener than alternative chemical approaches. This Review highlights the role of engineered enzymes (and identifies directions for further engineering efforts) in the application areas of greenhouse gas sequestration, fuel production, bioremediation, and degradation of plastic wastes.

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.

Continuous Flow Microreactors for the High-efficiency Enzymatic Synthesis of 10-Hydroxystearic Acid from Oleic Acid

Converting fatty acids into specialty chemicals is sustainable but hindered by the low efficiency and thermal instability of current oleic acid hydratases, along with mass transfer limitations in emulsion reactions. This study introduces an optimized continuous flow micro-reactor (CFMR) that efficiently transforms oleic acid at low (15 g L-1) and high (50 g L-1) concentrations, improving reaction efficiency and overcoming key conversion barriers. The first CFMR model showed reaction speeds surpassing traditional batch stirred tank reactors (BSTR). Optimizations were performed on three key components: liquid storage, mixer, and reaction section of the CFMR, with each round's best conditions carried into the next. This achieved a space-time yield of 597 g L-1 d-1 at a 15 g L-1 oleic acid load. To further enhance the yield, we optimized the emulsifier system to solve incomplete emulsification and developed a two-component feed microreactor (TCFMR) that addressed mass transfer limitations caused by the product at high substrate loads, reaching a 91 % conversion of 50 g L-1 oleic acid in 30 minutes, with a space-time yield of 2312 g L-1 d-1. These advancements represent significant progress in utilizing fatty acids and advancing sustainable chemical synthesis.

An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8-140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a broad range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Computational design of IDPs with specific conformational properties has, however, been difficult because of their substantial dynamics and structural complexity. We describe a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs that differ in compaction, long-range contacts, and propensity to phase separate. We experimentally tested and validated our designs and analyzed the sequence features that determine conformations. We show how our results are captured by a machine learning model, enabling us to speed up the algorithm. Our work expands the toolbox for computational protein design and will facilitate the design of proteins whose functions exploit the many properties afforded by protein disorder.

The art of designed coiled-coils for the regulation of mammalian cells

Synthetic biology aims to engineer complex biological systems using modular elements, with coiled-coil (CC) dimer-forming modules are emerging as highly useful building blocks in the regulation of protein assemblies and biological processes. Those small modules facilitate highly specific and orthogonal protein-protein interactions, offering versatility for the regulation of diverse biological functions. Additionally, their design rules enable precise control and tunability over these interactions, which are crucial for specific applications. Recent advancements showcase their potential for use in innovative therapeutic interventions and biomedical applications. In this review, we discuss the potential of CCs, exploring their diverse applications in mammalian cells, such as synthetic biological circuit design, transcriptional and allosteric regulation, cellular assemblies, chimeric antigen receptor (CAR) T cell regulation, and genome editing and their role in advancing the understanding and regulation of cellular processes.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

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.

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.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Functionally Active Synthetic α-Helical Pores

Transmembrane pores are currently at the forefront of nanobiotechnology, nanopore chemistry, and synthetic chemical biology research. Over the past few decades, significant studies in protein engineering have paved the way for redesigning membrane protein pores tailored for specific applications in nanobiotechnology. Most previous efforts predominantly centered on natural β-barrel pores designed with atomic precision for nucleic acid sequencing and sensing of biomacromolecules, including protein fragments. The requirement for a more efficient single-molecule detection system has driven the development of synthetic nanopores. For example, engineering channels to conduct ions and biomolecules selectively could lead to sophisticated nanopore sensors. Also, there has been an increased interest in synthetic pores, which can be fabricated to provide more control in designing architecture and diameter for single-molecule sensing of complex biomacromolecules. There have been impressive advancements in developing synthetic DNA-based pores, although their application in nanopore technology is limited. This has prompted a significant shift toward building synthetic transmembrane α-helical pores, a relatively underexplored field offering novel opportunities. Recently, computational tools have been employed to design and construct α-helical barrels of defined structure and functionality. We focus on building synthetic α-helical pores using naturally occurring transmembrane motifs of membrane protein pores. Our laboratory has developed synthetic α-helical transmembrane pores based on the natural porin PorACj (Porin A derived from Corynebacterium jeikeium) that function as nanopore sensors for single-molecule sensing of cationic cyclodextrins and polypeptides. Our breakthrough lies in being the first to create a functional and large stable synthetic transmembrane pore composed of short synthetic α-helical peptides. The key highlight of our work is that these pores can be synthesized using easy chemical synthesis, which permits its easy modification to include a variety of functional groups to build charge-selective sophisticated pores. Additionally, we have demonstrated that stable functional pores can be constructed from D-amino acid peptides. The analysis of pores composed of D- and L-amino acids in the presence of protease showed that only the D pores are highly functional and stable. The structural models of these pores revealed distinct surface charge conformation and geometry. These new classes of synthetic α-helical pores are highly original systems of general interest due to their unique architecture, functionality, and potential applications in nanopore technology and chemical biology. We emphasize that these simplified transmembrane pores have the potential to be components of functional nanodevices and therapeutic tools. We also suggest that such designed peptides might be valuable as antimicrobial agents and can be targeted to cancer cells. This article will focus on the evolutions in assembling α-helical transmembrane pores and highlight their advantages, including structural and functional versatility.

Computational design of soluble and functional membrane protein analogues

De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

A de novo designed coiled coil-based switch regulates the microtubule motor kinesin-1

Many enzymes are allosterically regulated via conformational change; however, our ability to manipulate these structural changes and control function is limited. Here we install a conformational switch for allosteric activation into the kinesin-1 microtubule motor in vitro and in cells. Kinesin-1 is a heterotetramer that accesses open active and closed autoinhibited states. The equilibrium between these states centers on a flexible elbow within a complex coiled-coil architecture. We target the elbow to engineer a closed state that can be opened with a de novo designed peptide. The alternative states are modeled computationally and confirmed by biophysical measurements and electron microscopy. In cells, peptide-driven activation increases kinesin transport, demonstrating a primary role for conformational switching in regulating motor activity. The designs are enabled by our understanding of ubiquitous coiled-coil structures, opening possibilities for controlling other protein activities.

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.

Design of Functional Globular β-Sheet Miniproteins

The design of discrete β-sheet peptides is far less advanced than e. g. the design of α-helical peptides. The reputation of β-sheet peptides as being poorly soluble and aggregation-prone often hinders active design efforts. Here, we show that this reputation is unfounded. We demonstrate this by looking at the β-hairpin and WW domain. Their structure and folding have been extensively studied and they have long served as model systems to investigate protein folding and folding kinetics. The resulting fundamental understanding has led to the development of hyperstable β-sheet scaffolds that fold at temperatures of 100 °C or high concentrations of denaturants. These have been used to design functional miniproteins with protein or nucleic acid binding properties, in some cases with such success that medical applications are conceivable. The β-sheet scaffolds are not always completely rigid, but can be specifically designed to respond to changes in pH, redox potential or presence of metal ions. Some engineered β-sheet peptides also exhibit catalytic properties, although not comparable to those of natural proteins. Previous reviews have focused on the design of stably folded and non-aggregating β-sheet sequences. In our review, we now also address design strategies to obtain functional miniproteins from β-sheet folding motifs.

Reengineering of a flavin-binding fluorescent protein using ProteinMPNN

Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.

Computational design of soluble functional analogues of integral membrane proteins

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

A Bibliometric Analysis: Current Perspectives and Potential Trends of Enzyme Thermostability from 1991-2022

Thermostability is considered a crucial parameter to evaluate the viability of enzymes in industrial applications. Over the past 31 years, many studies have been reported on the thermostability of enzymes. However, there is no systematic bibliometric analysis of publications on the thermostability of enzymes. In this study, 16,035 publications related to the thermostability of enzymes were searched and collected, showing an increasing annual trend. China contributed the most publications, while the United States had the highest citation count. International Journal of Biological Macromolecules is the most productive journal in the research field. Moreover, Chinese acad sci and Khosro Khajeh are the most active institutions and prolific authors in the field, respectively. Analysis of references with the strongest citation bursts and keyword co-occurrences, magnetic nanoparticles, metal-organic frameworks, molecular dynamics, and rational design are current hot spots and significant future research directions. This study is the first comprehensive bibliometric analysis summarizing trends and developments in enzyme thermostability research. Our findings could provide scholars with an understanding of the fundamental knowledge framework of the field and identify recent potential hotspots and research trends that could facilitate the discovery of collaboration opportunities.

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.

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.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods

MOTIVATION

Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness.

RESULTS

To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Assembling membraneless organelles from de novo designed proteins

Recent advances in de novo protein design have delivered a diversity of discrete de novo protein structures and complexes. A new challenge for the field is to use these designs directly in cells to intervene in biological processes and augment natural systems. The bottom-up design of self-assembled objects such as microcompartments and membraneless organelles is one such challenge. Here we describe the design of genetically encoded polypeptides that form membraneless organelles in Escherichia coli. To do this, we combine de novo α-helical sequences, intrinsically disordered linkers and client proteins in single-polypeptide constructs. We tailor the properties of the helical regions to shift protein assembly from arrested assemblies to dynamic condensates. The designs are characterized in cells and in vitro using biophysical methods and soft-matter physics. Finally, we use the designed polypeptide to co-compartmentalize a functional enzyme pair in E. coli, improving product formation close to the theoretical limit.

Engineering status of protein for improving microbial cell factories

With the development of metabolic engineering and synthetic biology, microbial cell factories (MCFs) have provided an efficient and sustainable method to synthesize a series of chemicals from renewable feedstocks. However, the efficiency of MCFs is usually limited by the inappropriate status of protein. Thus, engineering status of protein is essential to achieve efficient bioproduction with high titer, yield and productivity. In this review, we summarize the engineering strategies for metabolic protein status, including protein engineering for boosting microbial catalytic efficiency, protein modification for regulating microbial metabolic capacity, and protein assembly for enhancing microbial synthetic capacity. Finally, we highlight future challenges and prospects of improving microbial cell factories by engineering status of protein.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Xeno Amino Acids: A Look into Biochemistry as We Do Not Know It

Would another origin of life resemble Earth's biochemical use of amino acids? Here, we review current knowledge at three levels: (1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we do not know it. Amino acids thus remain important and tractable targets for astrobiological research. (2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. (3) Would we expect a similar set of side chains to those within the genetic code? Many plausible alternatives exist. Furthermore, evidence exists for both evolutionary advantage and physicochemical constraint as explanatory factors for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: (a) expanding evidence about amino acids as attractors within chemical evolution; (b) extending characterization of other backbones relative to biological proteins; and (c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides.

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a wide range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Designing IDPs with specific structural or functional properties has, however, been difficult, in part because determining accurate conformational ensembles of IDPs generally requires a combination of computational modelling and experiments. Motivated by recent advancements in efficient physics-based models for simulations of IDPs, we have developed a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs with different levels of compaction and that vary more than 100 fold in their propensity to undergo phase separation, even while keeping a fixed amino acid composition. We experimentally tested designs of variants of the low-complexity domain of hnRNPA1 and find high accuracy in our computational predictions, both in terms of single-chain compaction and propensity to undergo phase separation. We analyze the sequence features that determine changes in compaction and propensity to phase separate and find an overall good agreement with previous findings for naturally occurring sequences. Our general, physics-based method enables the design of disordered sequences with specified conformational properties. Our algorithm thus expands the toolbox for protein design to include also the most flexible proteins and will enable the design of proteins whose functions exploit the many properties afforded by protein disorder.

De novo design of knotted tandem repeat proteins

De novo protein design methods can create proteins with folds not yet seen in nature. These methods largely focus on optimizing the compatibility between the designed sequence and the intended conformation, without explicit consideration of protein folding pathways. Deeply knotted proteins, whose topologies may introduce substantial barriers to folding, thus represent an interesting test case for protein design. Here we report our attempts to design proteins with trefoil (31) and pentafoil (51) knotted topologies. We extended previously described algorithms for tandem repeat protein design in order to construct deeply knotted backbones and matching designed repeat sequences (N = 3 repeats for the trefoil and N = 5 for the pentafoil). We confirmed the intended conformation for the trefoil design by X ray crystallography, and we report here on this protein's structure, stability, and folding behaviour. The pentafoil design misfolded into an asymmetric structure (despite a 5-fold symmetric sequence); two of the four repeat-repeat units matched the designed backbone while the other two diverged to form local contacts, leading to a trefoil rather than pentafoil knotted topology. Our results also provide insights into the folding of knotted proteins.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.