Macromolecular modeling and design in Rosetta: recent methods and frameworks

Nuclear pore dysfunction and disease: a complex opportunity

The separation of genetic material from bulk cytoplasm has enabled the evolution of increasingly complex organisms, allowing for the development of sophisticated forms of life. However, this complexity has created new categories of dysfunction, including those related to the movement of material between cellular compartments. In eukaryotic cells, nucleocytoplasmic trafficking is a fundamental biological process, and cumulative disruptions to nuclear integrity and nucleocytoplasmic transport are detrimental to cell survival. This is particularly true in post-mitotic neurons, where nuclear pore injury and errors to nucleocytoplasmic trafficking are strongly associated with neurodegenerative disease. In this review, we summarize the current understanding of nuclear pore biology in physiological and pathological contexts and discuss potential therapeutic approaches for addressing nuclear pore injury and dysfunctional nucleocytoplasmic transport.

Exploring a Hirudin variant from nonhematophagous leeches: Unraveling full-length sequence, alternative splicing, function, and potential as a novel anticoagulant polypeptide

ETHNOPHARMACOLOGICAL RELEVANCE

Leeches exhibit robust anticoagulant activity, making them useful for treating cardiovascular diseases in traditional Chinese medicine. Whitmania pigra, the primary source species of leech-derived medicinal compounds in China, has been demonstrated to possess formidable anticoagulant properties. Hirudin-like peptides, recognized as potent thrombin inhibitors, are prevalent in hematophagous leeches. Considering that W. pigra is a nonhematophagic leech, the following question arises: does a hirudin variant exist in this species?

AIM OF THE STUDY

In this study we identified the hirudin-encoding gene (WP_HV1) in the W. pigra genome. The goal of this study was to assess its anticoagulant activity and analyze the related mechanisms.

MATERIALS AND METHODS

In this study, a hirudin-encoding gene, WP_HV1, was identified from the W. pigra genome, and its accurate coding sequence (CDS) was validated through cloning from cDNA extracted from fresh W. pigra specimens. The structure of WP_HV1 and the amino acids associated with its anticoagulant activity were determined by sequence and structural analysis and prediction of its binding energy to thrombin. E. coli was used for the expression of WP_HV1 and recombinant proteins with various structures and mutants. The anticoagulant activity of the synthesized recombinant proteins was then confirmed using thrombin time (TT).

RESULTS

Validation of the WP_HV1 gene was accomplished, and three alternative splices were discovered. The TT of the blank sample exceeded that of the recombinant WP_HV1 sample by 1.74 times (0.05 mg/ml), indicating positive anticoagulant activity. The anticoagulant activity of WP_HV1 was found to be associated with its C-terminal tyrosine, along with the presence of 9 acidic amino acids on both the left and right sides. A significant reduction in the corresponding TT was observed for the mutated amino acids compared to those of the wild type, with decreases of 4.8, 6.6, and 3.9 s, respectively. In addition, the anticoagulant activity of WP_HV1 was enhanced and prolonged for 2.7 s when the lysine-67 residue was mutated to tryptophan.

CONCLUSION

Only one hirudin-encoding variant was identified in W. pigra. The active amino acids associated with anticoagulation in WP_HV1 were resolved and validated, revealing a novel source for screening and developing new anticoagulant drugs.

Delineating redox cooperativity in water-soluble and membrane multiheme cytochromes through protein design

Nature has evolved diverse electron transport proteins and multiprotein assemblies essential to the generation and transduction of biological energy. However, substantially modifying or adapting these proteins for user-defined applications or to gain fundamental mechanistic insight can be hindered by their inherent complexity. De novo protein design offers an attractive route to stripping away this confounding complexity, enabling us to probe the fundamental workings of these bioenergetic proteins and systems, while providing robust, modular platforms for constructing completely artificial electron-conducting circuitry. Here, we use a set of de novo designed mono-heme and di-heme soluble and membrane proteins to delineate the contributions of electrostatic micro-environments and dielectric properties of the surrounding protein medium on the inter-heme redox cooperativity that we have previously reported. Experimentally, we find that the two heme sites in both the water-soluble and membrane constructs have broadly equivalent redox potentials in isolation, in agreement with Poisson-Boltzmann Continuum Electrostatics calculations. BioDC, a Python program for the estimation of electron transfer energetics and kinetics within multiheme cytochromes, also predicts equivalent heme sites, and reports that burial within the low dielectric environment of the membrane strengthens heme-heme electrostatic coupling. We conclude that redox cooperativity in our diheme cytochromes is largely driven by heme electrostatic coupling and confirm that this effect is greatly strengthened by burial in the membrane. These results demonstrate that while our de novo proteins present minimalist, new-to-nature constructs, they enable the dissection and microscopic examination of processes fundamental to the function of vital, yet complex, bioenergetic assemblies.

δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches

The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep-learning-based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ-conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide-rich peptides by these complementary methods.

Sculpting conducting nanopore size and shape through de novo protein design

Transmembrane β-barrels have considerable potential for a broad range of sensing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels, which provide suboptimal starting points. By contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to designing transmembrane β-barrel pores with different diameters and pore geometries. Nuclear magnetic resonance and crystallographic characterization show that the designs are stably folded with structures resembling those of the design models. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 picosiemens (~0.5 nanometer pore diameter) to 430 picosiemens (~1.1 nanometer pore diameter). Our approach opens the door to the custom design of transmembrane nanopores for sensing and sequencing applications.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies

Many growth factors and cytokines signal by binding to the extracellular domains of their receptors and driving association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affect signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo-designed fibroblast growth factor receptor (FGFR)-binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and mitogen-activated protein kinase (MAPK) pathway activation. The high specificity of the designed agonists reveals distinct roles for two FGFR splice variants in driving arterial endothelium and perivascular cell fates during early vascular development. Our designed modular assemblies should be broadly useful for unraveling the complexities of signaling in key developmental transitions and for developing future therapeutic applications.

Polymerization of ZBTB transcription factors regulates chromatin occupancy

BCL6, an oncogenic transcription factor (TF), forms polymers in the presence of a small-molecule molecular glue that stabilizes a complementary interface between homodimers of BCL6's broad-complex, tramtrack, and bric-à-brac (BTB) domain. The BTB domains of other proteins, including a large class of TFs, have similar architectures and symmetries, raising the possibility that additional BTB proteins self-assemble into higher-order structures. Here, we surveyed 189 human BTB proteins with a cellular fluorescent reporter assay and identified 18 ZBTB TFs that show evidence of polymerization. Through biochemical and cryoelectron microscopy (cryo-EM) studies, we demonstrate that these ZBTB TFs polymerize into filaments. We found that BTB-domain-mediated polymerization of ZBTB TFs enhances chromatin occupancy within regions containing homotypic clusters of TF binding sites, leading to repression of target genes. Our results reveal a role of higher-order structures in regulating ZBTB TFs and suggest an underappreciated role for TF polymerization in modulating gene expression.

MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impacts

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorer strives for efficiency in user experience. For example, we have prepared 45 000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/.

Computational design of de novo bioenergetic membrane proteins

The major energy-producing reactions of biochemistry occur at biological membranes. Computational protein design now provides the opportunity to elucidate the underlying principles of these processes and to construct bioenergetic pathways on our own terms. Here, we review recent achievements in this endeavour of 'synthetic bioenergetics', with a particular focus on new enabling tools that facilitate the computational design of biocompatible de novo integral membrane proteins. We use recent examples to showcase some of the key computational approaches in current use and highlight that the overall philosophy of 'surface-swapping' - the replacement of solvent-facing residues with amino acids bearing lipid-soluble hydrophobic sidechains - is a promising avenue in membrane protein design. We conclude by highlighting outstanding design challenges and the emerging role of AI in sequence design and structure ideation.

Functionally Active Synthetic α-Helical Pores

ConspectusTransmembrane 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.

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

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.

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

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

Computational Design of Phosphotriesterase Improves V-Agent Degradation Efficiency

Organophosphates (OPs) are a class of neurotoxic acetylcholinesterase inhibitors including widely used pesticides as well as nerve agents such as VX and VR. Current treatment of these toxins relies on reactivating acetylcholinesterase, which remains ineffective. Enzymatic scavengers are of interest for their ability to degrade OPs systemically before they reach their target. Here we describe a library of computationally designed variants of phosphotriesterase (PTE), an enzyme that is known to break down OPs. The mutations G208D, F104A, K77A, A80V, H254G, and I274N broadly improve catalytic efficiency of VX and VR hydrolysis without impacting the structure of the enzyme. The mutation I106 A improves catalysis of VR and L271E abolishes activity, likely due to disruptions of PTE's structure. This study elucidates the importance of these residues and contributes to the design of enzymatic OP scavengers with improved efficiency.

DDAffinity: predicting the changes in binding affinity of multiple point mutations using protein 3D structure

MOTIVATION

Mutations are the crucial driving force for biological evolution as they can disrupt protein stability and protein-protein interactions which have notable impacts on protein structure, function, and expression. However, existing computational methods for protein mutation effects prediction are generally limited to single point mutations with global dependencies, and do not systematically take into account the local and global synergistic epistasis inherent in multiple point mutations.

RESULTS

To this end, we propose a novel spatial and sequential message passing neural network, named DDAffinity, to predict the changes in binding affinity caused by multiple point mutations based on protein 3D structures. Specifically, instead of being on the whole protein, we perform message passing on the k-nearest neighbor residue graphs to extract pocket features of the protein 3D structures. Furthermore, to learn global topological features, a two-step additive Gaussian noising strategy during training is applied to blur out local details of protein geometry. We evaluate DDAffinity on benchmark datasets and external validation datasets. Overall, the predictive performance of DDAffinity is significantly improved compared with state-of-the-art baselines on multiple point mutations, including end-to-end and pre-training based methods. The ablation studies indicate the reasonable design of all components of DDAffinity. In addition, applications in nonredundant blind testing, predicting mutation effects of SARS-CoV-2 RBD variants, and optimizing human antibody against SARS-CoV-2 illustrate the effectiveness of DDAffinity.

AVAILABILITY AND IMPLEMENTATION

DDAffinity is available at https://github.com/ak422/DDAffinity.

Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement.

Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP

In Saccharomyces cerevisiae (S. cerevisiae), Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homolog with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known about how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling of biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, supporting the structural model. Finally, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN.

Deep learning-based characterization and redesign of major potato tuber storage protein

Potato is one of the most important crops worldwide, to feed a fast-growing population. In addition to providing energy, fiber, vitamins, and minerals, potato storage proteins are considered as one of the most valuable sources of non-animal proteins due to their high essential amino acid (EAA) index. However, low tuber protein content and limited knowledge about potato storage proteins restrict their widespread utilization in the food industry. Here, we report a proof-of-concept study, using deep learning-based protein design tools, to characterize the biological and chemical characteristics of patatins, the major potato storage proteins. This knowledge was then employed to design multiple cysteines on the patatin surface to build polymers linked by disulfide bonds, which significantly improved viscidity and nutrient of potato flour dough. Our study shows that deep learning-based protein design strategies are efficient to characterize and to create novel proteins for future food sources.

De novo design of peptides that bind specific conformers of α-synuclein

Insoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloids in a conformationally specific manner are lacking. We introduce a novel computational method for de novo design of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a "target backbone" for the design of a peptide binder. Next, we interrogate structures in the PDB with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Vaccination induces broadly neutralizing antibody precursors to HIV gp41

A key barrier to the development of vaccines that induce broadly neutralizing antibodies (bnAbs) against human immunodeficiency virus (HIV) and other viruses of high antigenic diversity is the design of priming immunogens that induce rare bnAb-precursor B cells. The high neutralization breadth of the HIV bnAb 10E8 makes elicitation of 10E8-class bnAbs desirable; however, the recessed epitope within gp41 makes envelope trimers poor priming immunogens and requires that 10E8-class bnAbs possess a long heavy chain complementarity determining region 3 (HCDR3) with a specific binding motif. We developed germline-targeting epitope scaffolds with affinity for 10E8-class precursors and engineered nanoparticles for multivalent display. Scaffolds exhibited epitope structural mimicry and bound bnAb-precursor human naive B cells in ex vivo screens, protein nanoparticles induced bnAb-precursor responses in stringent mouse models and rhesus macaques, and mRNA-encoded nanoparticles triggered similar responses in mice. Thus, germline-targeting epitope scaffold nanoparticles can elicit rare bnAb-precursor B cells with predefined binding specificities and HCDR3 features.

Reshaping Phosphatase Substrate Preference for Controlled Biosynthesis Using a "Design-Build-Test-Learn" Framework

Biosynthesis is the application of enzymes in microbial cell factories and has emerged as a promising alternative to chemical synthesis. However, natural enzymes with limited catalytic performance often need to be engineered to meet specific needs through a time-consuming trial-and-error process. This study presents a quantum mechanics (QM)-incorporated design-build-test-learn (DBTL) framework to rationally design phosphatase BT4131, an enzyme with an ambiguous substrate spectrum involved in N-acetylglucosamine (GlcNAc) biosynthesis. First, mutant M1 (L129Q) is designed using force field-based methods, resulting in a 1.4-fold increase in substrate preference (kcat/Km) toward GlcNAc-6-phosphate (GlcNAc6P). QM calculations indicate that the shift in substrate preference is caused by a 13.59 kcal mol-1 reduction in activation energy. Furthermore, an iterative computer-aided design is conducted to stabilize the transition state. As a result, mutant M4 (I49Q/L129Q/G172L) with a 9.5-fold increase in kcat-GlcNAc6P/Km-GlcNAc6P and a 59% decrease in kcat-Glc6P/Km-Glc6P is highly desirable compared to the wild type in the GlcNAc-producing chassis. The GlcNAc titer increases to 217.3 g L-1 with a yield of 0.597 g (g glucose)-1 in a 50-L bioreactor, representing the highest reported level. Collectively, this DBTL framework provides an easy yet fascinating approach to the rational design of enzymes for industrially viable biocatalysts.

Phage defence system CBASS is regulated by a prokaryotic E2 enzyme that imitates the ubiquitin pathway

The cyclic-oligonucleotide-based anti-phage signalling system (CBASS) is a type of innate prokaryotic immune system. Composed of a cyclic GMP-AMP synthase (cGAS) and CBASS-associated proteins, CBASS uses cyclic oligonucleotides to activate antiviral immunity. One major class of CBASS contains a homologue of eukaryotic ubiquitin-conjugating enzymes, which is either an E1-E2 fusion or a single E2. However, the functions of single E2s in CBASS remain elusive. Here, using biochemical, genetic, cryo-electron microscopy and mass spectrometry investigations, we discover that the E2 enzyme from Serratia marcescens regulates cGAS by imitating the ubiquitination cascade. This includes the processing of the cGAS C terminus, conjugation of cGAS to a cysteine residue, ligation of cGAS to a lysine residue, cleavage of the isopeptide bond and poly-cGASylation. The poly-cGASylation activates cGAS to produce cGAMP, which acts as an antiviral signal and leads to cell death. Thus, our findings reveal a unique regulatory role of E2 in CBASS.

Engineering of Zinc Finger Nucleases Through Structural Modeling Improves Genome Editing Efficiency in Cells

Genome Editing is widely used in biomedical research and medicine. Zinc finger nucleases (ZFNs) are smaller in size than transcription activator-like effector (TALE) nucleases (TALENs) and CRISPR-Cas9. Therefore, ZFN-encoding DNAs can be easily packaged into a viral vector with limited cargo space, such as adeno-associated virus (AAV) vectors, for in vivo and clinical applications. ZFNs have great potential for translational research and clinical use. However, constructing functional ZFNs and improving their genome editing efficiency is extremely difficult. Here, the efficient construction of functional ZFNs and the improvement of their genome editing efficiency using AlphaFold, Coot, and Rosetta are described. Plasmids encoding ZFNs consisting of six fingers using publicly available zinc-finger resources are assembled. Two functional ZFNs from the ten ZFNs tested are successfully obtained. Furthermore, the engineering of ZFNs using AlphaFold, Coot, or Rosetta increases the efficiency of genome editing by 5%, demonstrating the effectiveness of engineering ZFNs based on structural modeling.

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9-14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Antibody design using deep learning: from sequence and structure design to affinity maturation

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

The LexA-RecA* structure reveals a cryptic lock-and-key mechanism for SOS activation

The bacterial SOS response plays a key role in adaptation to DNA damage, including genomic stress caused by antibiotics. SOS induction begins when activated RecA*, an oligomeric nucleoprotein filament that forms on single-stranded DNA, binds to and stimulates autoproteolysis of the repressor LexA. Here, we present the structure of the complete Escherichia coli SOS signal complex, constituting full-length LexA bound to RecA*. We uncover an extensive interface unexpectedly including the LexA DNA-binding domain, providing a new molecular rationale for ordered SOS gene induction. We further find that the interface involves three RecA subunits, with a single residue in the central engaged subunit acting as a molecular key, inserting into an allosteric binding pocket to induce LexA cleavage. Given the pro-mutagenic nature of SOS activation, our structural and mechanistic insights provide a foundation for developing new therapeutics to slow the evolution of antibiotic resistance.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Computational design of non-porous pH-responsive antibody nanoparticles

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding

Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.

Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?

T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

Mechanism of DNA unwinding by MCM8-9 in complex with HROB

HROB promotes the MCM8-9 helicase in DNA damage response. To understand how HROB activates MCM8-9, we defined their interaction interface. We showed that HROB makes important yet transient contacts with both MCM8 and MCM9, and binds the MCM8-9 heterodimer with the highest affinity. MCM8-9-HROB prefer branched DNA structures, and display low DNA unwinding processivity. MCM8-9 unwinds DNA as a hexamer that assembles from dimers on DNA in the presence of ATP. The hexamer involves two repeating protein-protein interfaces between the alternating MCM8 and MCM9 subunits. One of these interfaces is quite stable and forms an obligate heterodimer across which HROB binds. The other interface is labile and mediates hexamer assembly, independently of HROB. The ATPase site formed at the labile interface contributes disproportionally more to DNA unwinding than that at the stable interface. Here, we show that HROB promotes DNA unwinding downstream of MCM8-9 loading and ring formation on ssDNA.

The hinge-engineered IgG1-IgG3 hybrid subclass IgGh47 potently enhances Fc-mediated function of anti-streptococcal and SARS-CoV-2 antibodies

Streptococcus pyogenes can cause invasive disease with high mortality despite adequate antibiotic treatments. To address this unmet need, we have previously generated an opsonic IgG1 monoclonal antibody, Ab25, targeting the bacterial M protein. Here, we engineer the IgG2-4 subclasses of Ab25. Despite having reduced binding, the IgG3 version promotes stronger phagocytosis of bacteria. Using atomic simulations, we show that IgG3's Fc tail has extensive movement in 3D space due to its extended hinge region, possibly facilitating interactions with immune cells. We replaced the hinge of IgG1 with four different IgG3-hinge segment subclasses, IgGhxx. Hinge-engineering does not diminish binding as with IgG3 but enhances opsonic function, where a 47 amino acid hinge is comparable to IgG3 in function. IgGh47 shows improved protection against S. pyogenes in a systemic infection mouse model, suggesting that IgGh47 has promise as a preclinical therapeutic candidate. Importantly, the enhanced opsonic function of IgGh47 is generalizable to diverse S. pyogenes strains from clinical isolates. We generated IgGh47 versions of anti-SARS-CoV-2 mAbs to broaden the biological applicability, and these also exhibit strongly enhanced opsonic function compared to the IgG1 subclass. The improved function of the IgGh47 subclass in two distant biological systems provides new insights into antibody function.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

High-Temperature Tolerance Protein Engineering through Deep Evolution

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

The structural landscape of the immunoglobulin fold by large-scale de novo design

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on β-sheet-β-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

Methyl rosmarinate is an allosteric inhibitor of SARS-CoV-2 3 CL protease as a potential candidate against SARS-cov-2 infection

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been ongoing for more than three years and urgently needs to be addressed. Traditional Chinese medicine (TCM) prescriptions have played an important role in the clinical treatment of patients with COVID-19 in China. However, it is difficult to uncover the potential molecular mechanisms of the active ingredients in these TCM prescriptions. In this paper, we developed a new approach by integrating the experimental assay, virtual screening, and the experimental verification, exploring the rapid discovery of active ingredients from TCM prescriptions. To achieve this goal, 4 TCM prescriptions in clinical use for different indications were selected to find the antiviral active ingredients in TCMs. The 3-chymotrypsin-like protease (3CLpro), an important target for fighting COVID-19, was utilized to determine the inhibitory activity of the TCM prescriptions and single herb. It was found that 10 single herbs had better inhibitory activity than other herbs by using a fluorescence resonance energy transfer (FRET) - based enzymatic assay of SARS-CoV-2 3CLpro. The ingredients contained in 10 herbs were thus virtually screened and the predicted active ingredients were experimentally validated. Thus, such a research strategy firstly removed many single herbs with no inhibitory activity against SARS-CoV-2 3CLpro at the very beginning by FRET-based assay, making our subsequent virtual screening more effective. Finally, 4 active components were found to have stronger inhibitory effects on SARS-CoV-2 3CLpro, and their inhibitory mechanism was subsequently investigated. Among of them, methyl rosmarinate as an allosteric inhibitor of SARS-CoV-2 3CLpro was confirmed and its ability to inhibit viral replication was demonstrated by the SARS-CoV-2 replicon system. To validate the binding mode via docking, the mutation experiment, circular dichroism (CD), enzymatic inhibition and surface plasmon resonance (SPR) assay were performed, demonstrating that methyl rosmarinate bound to the allosteric site of SARS-CoV-2 3CLpro. In conclusion, this paper provides the new ideas for the rapid discovery of active ingredients in TCM prescriptions based on a specific target, and methyl rosmarinate has the potential to be developed as an antiviral therapeutic candidate against SARS-CoV-2 infection.

Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF

Whereas oncogenes can potentially be inhibited with small molecules, the loss of tumour suppressors is more common and is problematic because the tumour-suppressor proteins are no longer present to be targeted. Notable examples include SMARCB1-mutant cancers, which are highly lethal malignancies driven by the inactivation of a subunit of SWI/SNF (also known as BAF) chromatin-remodelling complexes. Here, to generate mechanistic insights into the consequences of SMARCB1 mutation and to identify vulnerabilities, we contributed 14 SMARCB1-mutant cell lines to a near genome-wide CRISPR screen as part of the Cancer Dependency Map Project1-3. We report that the little-studied gene DDB1-CUL4-associated factor 5 (DCAF5) is required for the survival of SMARCB1-mutant cancers. We show that DCAF5 has a quality-control function for SWI/SNF complexes and promotes the degradation of incompletely assembled SWI/SNF complexes in the absence of SMARCB1. After depletion of DCAF5, SMARCB1-deficient SWI/SNF complexes reaccumulate, bind to target loci and restore SWI/SNF-mediated gene expression to levels that are sufficient to reverse the cancer state, including in vivo. Consequently, cancer results not from the loss of SMARCB1 function per se, but rather from DCAF5-mediated degradation of SWI/SNF complexes. These data indicate that therapeutic targeting of ubiquitin-mediated quality-control factors may effectively reverse the malignant state of some cancers driven by disruption of tumour suppressor complexes.

Design of amyloidogenic peptide traps

Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1-42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.

Strategies for designing biocatalysts with new functions

The engineering of natural enzymes has led to the availability of a broad range of biocatalysts that can be used for the sustainable manufacturing of a variety of chemicals and pharmaceuticals. However, for many important chemical transformations there are no known enzymes that can serve as starting templates for biocatalyst development. These limitations have fuelled efforts to build entirely new catalytic sites into proteins in order to generate enzymes with functions beyond those found in Nature. This bottom-up approach to enzyme development can also reveal new fundamental insights into the molecular origins of efficient protein catalysis. In this tutorial review, we will survey the different strategies that have been explored for designing new protein catalysts. These methods will be illustrated through key selected examples, which demonstrate how highly proficient and selective biocatalysts can be developed through experimental protein engineering and/or computational design. Given the rapid pace of development in the field, we are optimistic that designer enzymes will begin to play an increasingly prominent role as industrial biocatalysts in the coming years.

Continuous evolution of compact protein degradation tags regulated by selective molecular glues

Conditional protein degradation tags (degrons) are usually >100 amino acids long or are triggered by small molecules with substantial off-target effects, thwarting their use as specific modulators of endogenous protein levels. We developed a phage-assisted continuous evolution platform for molecular glue complexes (MG-PACE) and evolved a 36-amino acid zinc finger (ZF) degron (SD40) that binds the ubiquitin ligase substrate receptor cereblon in complex with PT-179, an orthogonal thalidomide derivative. Endogenous proteins tagged in-frame with SD40 using prime editing are degraded by otherwise inert PT-179. Cryo-electron microscopy structures of SD40 in complex with ligand-bound cereblon revealed mechanistic insights into the molecular basis of SD40's activity and specificity. Our efforts establish a system for continuous evolution of molecular glue complexes and provide ZF tags that overcome shortcomings associated with existing degrons.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes

The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternative approach involves expanding natural enzyme capabilities for new substrates and parameters. Here, we introduce CoSaNN (Conformation Sampling using Neural Network), an enzyme design strategy using deep learning for structure prediction and sequence optimization. CoSaNN controls enzyme conformations to expand chemical space beyond simple mutagenesis. It employs a context-dependent approach for generating enzyme designs, considering non-linear relationships in sequence and structure space. We also developed SolvIT, a graph NN predicting protein solubility in Escherichia coli, optimizing enzyme expression selection from larger design sets. Using this method, we engineered enzymes with superior expression levels, with 54% expressed in E. coli, and increased thermal stability, with over 30% having higher Tm than the template, with no high-throughput screening. Our research underscores AI's transformative role in protein design, capturing high-order interactions and preserving allosteric mechanisms in extensively modified enzymes, and notably enhancing expression success rates. This method's ease of use and efficiency streamlines enzyme design, opening broad avenues for biotechnological applications and broadening field accessibility.

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.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

A green pathway for lignin valorization: Enzymatic lignin depolymerization in biocompatible ionic liquids and deep eutectic solvents

Lignin depolymerization, which enables the breakdown of a complex and heterogeneous aromatic polymer into relatively uniform derivatives, serves as a critical process in valorization of lignin. Enzymatic lignin depolymerization has become a promising biological strategy to overcome the heterogeneity of lignin, due to its mild reaction conditions and high specificity. However, the low solubility of lignin compounds in aqueous environments prevents efficient lignin depolymerization by lignin-degrading enzymes. The employment of biocompatible ionic liquids (ILs) and deep eutectic solvents (DESs) in lignin fractionation has created a promising pathway to enzymatically depolymerize lignin within these green solvents to increase lignin solubility. In this review, recent research progress on enzymatic lignin depolymerization, particularly in a consolidated process involving ILs/DESs is summarized. In addition, the interactions between lignin-degrading enzymes and solvent systems are explored, and potential protein engineering methodology to improve the performance of lignin-degrading enzymes is discussed. Consolidation of enzymatic lignin depolymerization and biocompatible ILs/DESs paves a sustainable, efficient, and synergistic way to convert lignin into value-added products.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

MUTATIONEXPLORER- A WEBSERVER FOR MUTATION OF PROTEINS AND 3D VISUALIZATION OF ENERGETIC IMPACTS

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format.