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.

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.

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.

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.

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.

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/.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach.

Phage-assisted evolution of highly active cytosine base editors with enhanced selectivity and minimal sequence context preference

TadA-derived cytosine base editors (TadCBEs) enable programmable C•G-to-T•A editing while retaining the small size, high on-target activity, and low off-target activity of TadA deaminases. Existing TadCBEs, however, exhibit residual A•T-to-G•C editing at certain positions and lower editing efficiencies at some sequence contexts and with non-SpCas9 targeting domains. To address these limitations, we use phage-assisted evolution to evolve CBE6s from a TadA-mediated dual cytosine and adenine base editor, discovering mutations at N46 and Y73 in TadA that prevent A•T-to-G•C editing and improve C•G-to-T•A editing with expanded sequence-context compatibility, respectively. In E. coli, CBE6 variants offer high C•G-to-T•A editing and no detected A•T-to-G•C editing in any sequence context. In human cells, CBE6 variants exhibit broad Cas domain compatibility and retain low off-target editing despite exceeding BE4max and previous TadCBEs in on-target editing efficiency. Finally, we show that the high selectivity of CBE6 variants is well-suited for therapeutically relevant stop codon installation without creating unwanted missense mutations from residual A•T-to-G•C editing.

A novel major facilitator superfamily-type tripartite efflux system CprABC mediates resistance to polymyxins in Chryseobacterium sp. PL22-22A

Background

Polymyxin B (PMB) and polymyxin E (colistin, CST) are polymyxin antibiotics, which are considered last-line therapeutic options against multidrug-resistant Gram-negative bacteria in serious infections. However, there is increasing risk of resistance to antimicrobial drugs. Effective efflux pump inhibitors (EPIs) should be developed to help combat efflux pump-mediated antibiotic resistance.

Methods

Chryseobacterium sp. PL22-22A was isolated from aquaculture sewage under selection with 8 mg/L PMB, and then its genome was sequenced using Oxford Nanopore and BGISEQ-500 platforms. Cpr (Chryseobacterium Polymyxins Resistance) genes encoding a major facilitator superfamily-type tripartite efflux system, were found in the genome. These genes, and the gene encoding a truncation mutant of CprB from which sequence called CprBc was deleted, were amplified and expressed/co-expressed in Escherichia coli DH5α. Minimum inhibitory concentrations (MICs) of polymyxins toward the various E. coli heterologous expression strains were tested in the presence of 2-128 mg/L PMB or CST. The pumping activity of CprABC was assessed via structural modeling using Discovery Studio 2.0 software. Moreover, the influence on MICs of baicalin, a novel MFS EPI, was determined, and the effect was analyzed based on homology modeling.

Results

Multidrug-resistant bacterial strain Chryseobacterium sp. PL22-22A was isolated in this work; it has notable resistance to polymyxin, with MICs for PMB and CST of 96 and 128 mg/L, respectively. A novel MFS-type tripartite efflux system, named CprABC, was identified in the genome of Chryseobacterium sp. PL22-22A. Heterologous expression and EPI assays indicated that the CprABC system is responsible for the polymyxin resistance of Chryseobacterium sp. PL22-22A. Structural modeling suggested that this efflux system provides a continuous conduit that runs from the CprB funnel through the CprC porin domain to pump polymyxins out of the cell. A specific C-terminal α-helix, CprBc, has an activation function on polymyxin excretion by CprB. The flavonoid compound baicalin was found to affect the allostery of CprB and/or obstruct the substrate conduit, and thus to inhibit extracellular polymyxin transport by CprABC.

Conclusion

Novel MFS-type tripartite efflux system CprABC in Chryseobacterium sp. PL22-22A mediates resistance to polymyxins, and baicalin is a promising EPI.

Disordered regions and folded modules in CAF-1 promote histone deposition in Schizosaccharomyces pombe

Genome and epigenome integrity in eukaryotes depends on the proper coupling of histone deposition with DNA synthesis. This process relies on the evolutionary conserved histone chaperone CAF-1 for which the links between structure and functions are still a puzzle. While studies of the Saccharomyces cerevisiae CAF-1 complex enabled to propose a model for the histone deposition mechanism, we still lack a framework to demonstrate its generality and in particular, how its interaction with the polymerase accessory factor PCNA is operating. Here, we reconstituted a complete SpCAF-1 from fission yeast. We characterized its dynamic structure using NMR, SAXS and molecular modeling together with in vitro and in vivo functional studies on rationally designed interaction mutants. Importantly, we identify the unfolded nature of the acidic domain which folds up when binding to histones. We also show how the long KER helix mediates DNA binding and stimulates SpCAF-1 association with PCNA. Our study highlights how the organization of CAF-1 comprising both disordered regions and folded modules enables the dynamics of multiple interactions to promote synthesis-coupled histone deposition essential for its DNA replication, heterochromatin maintenance, and genome stability functions.

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure

Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.

Computational Design of Peptides for Biomaterials Applications

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.

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

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

DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

A-kinase anchoring proteins are enriched in the central pair microtubules of motile cilia in Chlamydomonas reinhardtii

Cilia are microtubule-based sensory organelles present in a number of eukaryotic cells. Mutations in the genes encoding ciliary proteins cause ciliopathies in humans. A-kinase anchoring proteins (AKAPs) tether ciliary signaling proteins such as protein kinase A (PKA). The dimerization and docking domain (D/D) on the RIIα subunit of PKA interacts with AKAPs. Here, we show that AKAP240 from the central-pair microtubules of Chlamydomonas reinhardtii cilia uses two C-terminal amphipathic helices to bind to its partner FAP174, an RIIα-like protein with a D/D domain at the N-terminus. Co-immunoprecipitation using anti-FAP174 antibody with an enriched central-pair microtubule fraction isolated seven interactors whose mass spectrometry analysis revealed proteins from the C2a (FAP65, FAP70, and FAP147) and C1b (CPC1, HSP70A, and FAP42) microtubule projections and FAP75, a protein whose sub-ciliary localization is unknown. Using RII D/D and FAP174 as baits, we identified two additional AKAPs (CPC1 and FAP297) in the central-pair microtubules.

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

An efflux pump in genomic island GI-M202a mediates the transfer of polymyxin B resistance in Pandoraea pnomenusa M202

BACKGROUND

Polymyxin B is considered a last-line therapeutic option against multidrug-resistant gram-negative bacteria, especially in COVID-19 coinfections or other serious infections. However, the risk of antimicrobial resistance and its spread to the environment should be brought to the forefront.

METHODS

Pandoraea pnomenusa M202 was isolated under selection with 8 mg/L polymyxin B from hospital sewage and then was sequenced by the PacBio RS II and Illumina HiSeq 4000 platforms. Mating experiments were performed to evaluate the transfer of the major facilitator superfamily (MFS) transporter in genomic islands (GIs) to Escherichia coli 25DN. The recombinant E. coli strain Mrc-3 harboring MFS transporter encoding gene FKQ53_RS21695 was also constructed. The influence of efflux pump inhibitors (EPIs) on MICs was determined. The mechanism of polymyxin B excretion mediated by FKQ53_RS21695 was investigated by Discovery Studio 2.0 based on homology modeling.

RESULTS

The MIC of polymyxin B for the multidrug-resistant bacterial strain P. pnomenusa M202, isolated from hospital sewage, was 96 mg/L. GI-M202a, harboring an MFS transporter-encoding gene and conjugative transfer protein-encoding genes of the type IV secretion system, was identified in P. pnomenusa M202. The mating experiment between M202 and E. coli 25DN reflected the transferability of polymyxin B resistance via GI-M202a. EPI and heterogeneous expression assays also suggested that the MFS transporter gene FKQ53_RS21695 in GI-M202a was responsible for polymyxin B resistance. Molecular docking revealed that the polymyxin B fatty acyl group inserts into the hydrophobic region of the transmembrane core with Pi-alkyl and unfavorable bump interactions, and then polymyxin B rotates around Tyr43 to externally display the peptide group during the efflux process, accompanied by an inward-to-outward conformational change in the MFS transporter. Additionally, verapamil and CCCP exhibited significant inhibition via competition for binding sites.

CONCLUSIONS

These findings demonstrated that GI-M202a along with the MFS transporter FKQ53_RS21695 in P. pnomenusa M202 could mediate the transmission of polymyxin B resistance.

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Harnessing AlphaFold to reveal state secrets: Prediction of hERG closed and inactivated states

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, IKr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with myriad available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions aligning well with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as a novel method to predict discrete protein conformations and novel linkages from structure to function.

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.

Computational Design of Peptide Assemblies

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

[Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]

OBJECTIVE

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results.

METHODS

Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages.

RESULTS

When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group.

CONCLUSION

PML protein can interact strongly with TAB1 protein.