A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

Rationally seeded computational protein design of ɑ-helical barrels

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

A suite of designed protein cages using machine learning and protein fragment-based protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.

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/.

Opportunities and challenges in design and optimization of protein function

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

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

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

AbFlex: designing antibody complementarity determining regions with flexible CDR definition

MOTIVATION

Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

RESULTS

We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

AVAILABILITY AND IMPLEMENTATION

The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

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.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

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.

2B4: A potential target in Staphylococcus aureus associated allergic inflammation

Staphylococcus aureus (SA) and its exotoxins activate eosinophils (Eos) and mast cells (MCs) via CD48, a GPI-anchored receptor belonging to the signaling lymphocytes activation molecules (SLAM) family. 2B4 (CD244), an immuno-regulatory transmembrane receptor also belonging to the SLAM family, is the high-affinity ligand for CD48. 2B4 is expressed on several leukocytes including NK cells, T cells, basophils, monocytes, dendritic cells (DCs), and Eos. In the Eos and MCs crosstalk carried out by physical and soluble interactions (named the 'allergic effector unit', AEU), 2B4-CD48 binding plays a central role. As CD48 and 2B4 share some structural characteristics and SA colonization accompanies most of the allergic diseases, we hypothesized that SA exotoxins (e.g. Staphylococcus enterotoxin B, SEB) can also bind and activate 2B4 and thereby possibly further aggravate inflammation. To check our hypothesis, we used in vitro, in silico, and in vivo methods. By enzyme-linked immunosorbent assay (ELISA), flow cytometry (FC), fluorescence microscopy, and microscale thermophoresis, we have shown that SEB can bind specifically to 2B4. By Eos short- and long-term activation assays, we confirmed the functionality of the SEB-2B4 interaction. Using computational modeling, we identified possible SEB-binding sites on human and mouse 2B4. Finally, in vivo, in an SEB-induced peritonitis model, 2B4-KO mice showed a significant reduction of inflammatory features compared with WT mice. Altogether, the results of this study confirm that 2B4 is an important receptor in SEB-mediated inflammation, and therefore a role is suggested for 2B4 in SA associated inflammatory conditions.

Assembly Requirements for the Construction of Large-Scale Binary Protein Structures

The precise assembly of multiple biomacromolecules into well-defined structures and materials is of great importance for various biomedical and nanobiotechnological applications. In this study, we investigate the assembly requirements for two-component materials using charged protein nanocages as building blocks. To achieve this, we designed several variants of ferritin nanocages to determine the surface characteristics necessary for the formation of large-scale binary three-dimensional (3D) assemblies. These nanocage variants were employed in protein crystallization experiments and macromolecular crystallography analyses, complemented by computational methods. Through the screening of nanocage variant combinations at various ionic strengths, we identified three essential features for successful assembly: (1) the presence of a favored crystal contact region, (2) the presence of a charged patch not involved in crystal contacts, and (3) sufficient distinctiveness between the nanocages. Surprisingly, the absence of noncrystal contact mediating patches had a detrimental effect on the assemblies, highlighting their unexpected importance. Intriguingly, we observed the formation of not only binary structures but also both negatively and positively charged unitary structures under previously exclusively binary conditions. Overall, our findings will inform future design strategies by providing some design rules, showcasing the utility of supercharging symmetric building blocks in facilitating the assembly of biomacromolecules into large-scale binary 3D assemblies.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high-affinity protein binders.

Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site

The HIV-1 fusion peptide (FP) represents a promising vaccine target, but global FP sequence diversity among circulating strains has limited anti-FP antibodies to ~60% neutralization breadth. Here we evolve the FP-targeting antibody VRC34.01 in vitro to enhance FP-neutralization using site saturation mutagenesis and yeast display. Successive rounds of directed evolution by iterative selection of antibodies for binding to resistant HIV-1 strains establish a variant, VRC34.01_mm28, as a best-in-class antibody with 10-fold enhanced potency compared to the template antibody and ~80% breadth on a cross-clade 208-strain neutralization panel. Structural analyses demonstrate that the improved paratope expands the FP binding groove to accommodate diverse FP sequences of different lengths while also recognizing the HIV-1 Env backbone. These data reveal critical antibody features for enhanced neutralization breadth and potency against the FP site of vulnerability and accelerate clinical development of broad HIV-1 FP-targeting vaccines and therapeutics.

Designing peptides predicted to bind to the omicron variant better than ACE2 via computational protein design and molecular dynamics

Brought about by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease (COVID-19) pandemic has resulted in large numbers of worldwide deaths and cases. Several SARS-CoV-2 variants have evolved, and Omicron (B.1.1.529) was one of the important variants of concern. It gets inside human cells by using its S1 subunit's receptor-binding domain (SARS-CoV-2-RBD) to bind to Angiotensin-converting enzyme 2 receptor's peptidase domain (ACE2-PD). Using peptides to inhibit binding interactions (BIs) between ACE2-PD and SARS-CoV-2-RBD is one of promising COVID-19 therapies. Employing computational protein design (CPD) as well as molecular dynamics (MD), this study used ACE2-PD's α1 helix to generate novel 25-mer peptide binders (SPB25) of Omicron RBD that have predicted binding affinities (ΔGbind (MM‑GBSA)) better than ACE2 by increasing favorable BIs between SPB25 and the conserved residues of RBD. Results from MD and the MM-GBSA method identified two best designed peptides (SPB25T7L/K11A and SPB25T7L/K11L with ΔGbind (MM‑GBSA) of -92.4 ± 0.4 and -95.7 ± 0.5 kcal/mol, respectively) that have better ΔGbind (MM‑GBSA) to Omicron RBD than ACE2 (-87.9 ± 0.5 kcal/mol) and SPB25 (-71.6 ± 0.5 kcal/mol). Additionally, they were predicted to have slightly higher stabilities, based on their percent helicities in water, than SBP1 (the experimentally proven inhibitor of SARS-CoV-2-RBD). Our two best designed SPB25s are promising candidates as omicron variant inhibitors.

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling

CC and CXC-chemokines are the primary drivers of chemotaxis in inflammation, but chemokine network redundancy thwarts pharmacological intervention. Tick evasins promiscuously bind CC and CXC-chemokines, overcoming redundancy. Here we show that short peptides that promiscuously bind both chemokine classes can be identified from evasins by phage-display screening performed with multiple chemokines in parallel. We identify two conserved motifs within these peptides and show using saturation-mutagenesis phage-display and chemotaxis studies of an exemplar peptide that an anionic patch in the first motif and hydrophobic, aromatic and cysteine residues in the second are functionally necessary. AlphaFold2-Multimer modelling suggests that the peptide occludes distinct receptor-binding regions in CC and in CXC-chemokines, with the first and second motifs contributing ionic and hydrophobic interactions respectively. Our results indicate that peptides with broad-spectrum anti-chemokine activity and therapeutic potential may be identified from evasins, and the pharmacophore characterised by phage display, saturation mutagenesis and computational modelling.

Interface Engineering of Carrier-Protein-Dependent Metabolic Pathways

Carrier-protein-dependent metabolic pathways biosynthesize fatty acids, polyketides, and non-ribosomal peptides, producing metabolites with important pharmaceutical, environmental, and industrial properties. Recent findings demonstrate that these pathways rely on selective communication mechanisms involving protein-protein interactions (PPIs) that guide enzyme reactivity and timing. While rational design of these PPIs could enable pathway design and modification, this goal remains a challenge due to the complex nature of protein interfaces. Computational methods offer an encouraging avenue, though many score functions fail to predict experimental observables, leading to low success rates. Here, we improve upon the Rosetta score function, leveraging experimental data through iterative rounds of computational prediction and mutagenesis, to design a hybrid fatty acid-non-ribosomal peptide initiation pathway. By increasing the weight of the electrostatic score term, the computational protocol proved to be more predictive, requiring fewer rounds of iteration to identify mutants with high in vitro activity. This allowed efficient design of new PPIs between a non-ribosomal peptide synthetase adenylation domain, PltF, and a fatty acid synthase acyl carrier protein, AcpP, as validated by activity and structural studies. This method provides a promising platform for customized pathway design, establishing a standard for carrier-protein-dependent pathway engineering through PPI optimization.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Pyruvate dehydrogenase operates as an intramolecular nitroxyl generator during macrophage metabolic reprogramming

M1 macrophages enter a glycolytic state when endogenous nitric oxide (NO) reprograms mitochondrial metabolism by limiting aconitase 2 and pyruvate dehydrogenase (PDH) activity. Here, we provide evidence that NO targets the PDH complex by using lipoate to generate nitroxyl (HNO). PDH E2-associated lipoate is modified in NO-rich macrophages while the PDH E3 enzyme, also known as dihydrolipoamide dehydrogenase (DLD), is irreversibly inhibited. Mechanistically, we show that lipoate facilitates NO-mediated production of HNO, which interacts with thiols forming irreversible modifications including sulfinamide. In addition, we reveal a macrophage signature of proteins with reduction-resistant modifications, including in DLD, and identify potential HNO targets. Consistently, DLD enzyme is modified in an HNO-dependent manner at Cys477 and Cys484, and molecular modeling and mutagenesis show these modifications impair the formation of DLD homodimers. In conclusion, our work demonstrates that HNO is produced physiologically. Moreover, the production of HNO is dependent on the lipoate-rich PDH complex facilitating irreversible modifications that are critical to NO-dependent metabolic rewiring.

Development of an interfering peptide M1-20 with potent anti-cancer effects by targeting FOXM1

Disrupting protein-protein interactions (PPIs) has emerged as a promising strategy for cancer drug development. Interfering peptides disrupting PPIs can be rationally designed based on the structures of natural sequences mediating these interactions. Transcription factor FOXM1 overexpresses in multiple cancers and is considered an effective target for cancer therapeutic drug development. Using a rational design approach, we have generated a peptide library from the FOXM1 C-terminal sequence and screened FOXM1-binding peptides. Combining FOXM1 binding and cell inhibitory results, we have obtained a FOXM1-targeting interfering peptide M1-20 that is optimized from the natural parent peptide to the D-retro-inverso peptide. With improved stability characteristics, M1-20 inhibits proliferation and migration, and induces apoptosis of cancer cells. Mechanistically, M1-20 inhibits FOXM1 transcriptional activities by disrupting its interaction between the MuvB complex and the transcriptional co-activator CBP. These are consistent with the results that M1-20 suppresses cancer progression and metastasis without noticeable toxic and side effects in wild-type mice. These findings reveal that M1-20 has the potential to be developed as an anti-cancer drug candidate targeting FOXM1.

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

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.

GPCRome-wide analysis of G-protein-coupling diversity using a computational biology approach

GPCRs are master regulators of cell signaling by transducing extracellular stimuli into the cell via selective coupling to intracellular G-proteins. Here we present a computational analysis of the structural determinants of G-protein-coupling repertoire of experimental and predicted 3D GPCR-G-protein complexes. Interface contact analysis recapitulates structural hallmarks associated with G-protein-coupling specificity, including TM5, TM6 and ICLs. We employ interface contacts as fingerprints to cluster G_s vs G_i complexes in an unsupervised fashion, suggesting that interface residues contribute to selective coupling. We experimentally confirm on a promiscuous receptor (CCKAR) that mutations of some of these specificity-determining positions bias the coupling selectivity. Interestingly, G_s-GPCR complexes have more conserved interfaces, while G_i/o proteins adopt a wider number of alternative docking poses, as assessed via structural alignments of representative 3D complexes. Binding energy calculations demonstrate that distinct structural properties of the complexes are associated to higher stability of G_s than G_i/o complexes. AlphaFold2 predictions of experimental binary complexes confirm several of these structural features and allow us to augment the structural coverage of poorly characterized complexes such as G_12/13.

The development of an anti-cancer peptide M1-21 targeting transcription factor FOXM1

BACKGROUND

Transcription factor FOXM1 is a potential target for anti-cancer drug development. An interfering peptide M1-21, targeting FOXM1 and FOXM1-interacting proteins, is developed and its anti-cancer efficacy is evaluated.

METHODS

FOXM1 C-terminus-binding peptides are screened by in silico protocols from the peptide library of FOXM1 (1-138aa) and confirmed by cellular experiments. The selected peptide is synthesized into its D-retro-inverso (DRI) form by fusing a TAT cell-penetrating sequence. Anti-cancer activities are evaluated in vitro and in vivo with tumor-grafted nude mice, spontaneous breast cancer mice, and wild-type metastasis-tracing mice. Anti-cancer mechanisms are analyzed. Distribution and safety profiles in mice are evaluated.

RESULTS

With improved stability and cell inhibitory activity compared to the parent peptide, M1-21 binds to multiple regions of FOXM1 and interferes with protein-protein interactions between FOXM1 and its various known partner proteins, including PLK1, LIN9 and B-MYB of the MuvB complex, and β-catenin. Consequently, M1-21 inhibits FOXM1-related transcriptional activities and FOXM1-mediated nuclear importation of β-catenin and β-catenin transcriptional activities. M1-21 inhibits multiple types of cancer (20 µM in vitro or 30 mg/kg in vivo) by preventing proliferation, migration, and WNT signaling. Distribution and safety profiles of M1-21 are favorable (broad distribution and > 15 h stability in mice) and the tested non-severely toxic dose reaches 200 mg/kg in mice. M1-21 also has low hemolytic toxicity and immunogenicity in mice.

CONCLUSIONS

M1-21 is a promising interfering peptide targeting FOXM1 for the development of anti-cancer drugs.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Little Brown Bats (Myotis lucifugus) Support the Binding of SARS-CoV-2 Spike and Are Likely Susceptible to SARS-CoV-2 Infection

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), believed to have originated from a bat species, can infect a wide range of non-human hosts. Bats are known to harbor hundreds of coronaviruses capable of spillover into human populations. Recent studies have shown a significant variation in the susceptibility among bat species to SARS-CoV-2 infection. We show that little brown bats (LBB) express angiotensin-converting enzyme 2 receptor and the transmembrane serine protease 2, which are accessible to and support SARS-CoV-2 binding. All-atom molecular dynamics (MD) simulations revealed that LBB ACE2 formed strong electrostatic interactions with the RBD similar to human and cat ACE2 proteins. In summary, LBBs, a widely distributed North American bat species, could be at risk of SARS-CoV-2 infection and potentially serve as a natural reservoir. Finally, our framework, combining in vitro and in silico methods, is a useful tool to assess the SARS-CoV-2 susceptibility of bats and other animal species.

Non-covalent interactions that tune the reactivities of the flavins in bifurcating electron transferring flavoprotein

Bifurcating electron transferring flavoproteins (Bf-ETFs) tune chemically identical flavins to two contrasting roles. To understand how, we used hybrid quantum mechanical molecular mechanical calculations to characterize non-covalent interactions applied to each flavin by the protein. Our computations replicated the differences between the reactivities of the flavins: the electron transferring flavin (^ETflavin) was calculated to stabilize anionic semiquinone (ASQ) as needed to execute its single-electron transfers, whereas the Bf flavin (^Bfflavin) was found to disfavor the ASQ state more than does free flavin and to be less susceptible to reduction. The stability of ^ETflavin ASQ was attributed in part to H-bond donation to the flavin O2 from a nearby His side chain, via comparison of models employing different tautomers of His. This H-bond between O2 and the ET site was uniquely strong in the ASQ state, whereas reduction of ^ETflavin to the anionic hydroquinone (AHQ) was associated with side chain reorientation, backbone displacement and reorganization of its H-bond network including a Tyr from the other domain and subunit of the ETF. The Bf site was less responsive overall, but formation of the ^Bfflavin AHQ allowed a nearby Arg side chain to adopt an alternative rotamer that can H-bond to the ^Bfflavin O4. This would stabilize the anionic ^Bfflavin and rationalize effects of mutation at this position. Thus, our computations provide insights on states and conformations that have not been possible to characterize experimentally, offering explanations for observed residue conservation and raising possibilities that can now be tested.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD)

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Residues E53, L55, H59, and G70 of the cellular receptor protein Tva mediate cell binding and entry of the novel subgroup K avian leukosis virus

Subgroup K avian leukosis virus (ALV-K) is a novel subgroup of ALV isolated from Chinese native chickens. As for a retrovirus, the interaction between its envelope protein and cellular receptor is a crucial step in ALV-K infection. Tva, a protein previously determined to be associated with vitamin B₁₂/cobalamin uptake, has been identified as the receptor of ALV-K. However, the molecular mechanism underlying the interaction between Tva and the envelope protein of ALV-K remains unclear. In this study, we identified the C-terminal loop of the LDL-A module of Tva as the minimal functional domain that directly interacts with gp85, the surface component of the ALV-K envelope protein. Further point-mutation analysis revealed that E53, L55, H59, and G70, which are exposed on the surface of Tva and are spatially adjacent, are key residues for the binding of Tva and gp85 and facilitate the entry of ALV-K. Homology modeling analysis indicated that the substitution of these four residues did not significantly impact the Tva structure but impaired the interaction between Tva and gp85 of ALV-K. Importantly, the gene-edited DF-1 cell line with precisely substituted E53, L55, H59, and G70 was completely resistant to ALV-K infection and did not affect vitamin B₁₂/cobalamin uptake. Collectively, these findings not only contribute to a better understanding of the mechanism of ALV-K entry into host cells but also provide an ideal gene-editing target for antiviral study.

Deep learning of protein sequence design of protein-protein interactions

MOTIVATION

As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protein-protein interactions is still a rather challenging subproblem with very low success rates-yet, it is central to most biological processes.

RESULTS

We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.

AVAILABILITY AND IMPLEMENTATION

The source code of the method is available at https://github.com/strauchlab/iNNterfaceDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improved interface packing and design opportunities revealed by CryoEM analysis of a designed protein nanocage

Symmetric protein assemblies play important roles in nature which makes them an attractive target for engineering. De novo symmetric protein complexes can be created through computational protein design to tailor their properties from first principles, and recently several protein nanocages have been created by bringing together protein components through hydrophobic interactions. Accurate experimental structures of newly-developed proteins are essential to validate their design, improve assembly stability, and tailor downstream applications. We describe the CryoEM structure of the nanocage I3-01, at an overall resolution of 3.5 Å. I3-01, comprising 60 aldolase subunits arranged with icosahedral symmetry, has resisted high-resolution characterization. Some key differences between the refined structure and the original design are identified, such as improved packing of hydrophobic sidechains, providing insight to the resistance of I3-01 to high-resolution averaging. Based on our analysis, we suggest factors important in the design and structural processing of new assemblies.

A redox switch regulates the assembly and anti-CRISPR activity of AcrIIC1

Anti-CRISPRs (Acrs) are natural inhibitors of bacteria's CRISPR-Cas systems, and have been developed as a safeguard to reduce the off-target effects of CRISPR gene-editing technology. Acrs can directly bind to CRISPR-Cas complexes and inhibit their activities. However, whether this process is under regulation in diverse eukaryotic cellular environments is poorly understood. In this work, we report the discovery of a redox switch for NmeAcrIIC1, which regulates NmeAcrIIC1's monomer-dimer interconversion and inhibitory activity on Cas9. Further structural studies reveal that a pair of conserved cysteines mediates the formation of inactive NmeAcrIIC1 dimer and directs the redox cycle. The redox switch also applies to the other two AcrIIC1 orthologs. Moreover, by replacing the redox-sensitive cysteines, we generated a robust AcrIIC1 variant that maintains potent inhibitory activity under various redox conditions. Our results reveal a redox-dependent regulation mechanism of Acr, and shed light on the design of superior Acr for CRISPR-Cas systems.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

An inhibitor of interaction between the transcription factor NRF2 and the E3 ubiquitin ligase adapter β-TrCP delivers anti-inflammatory responses in mouse liver

It is widely accepted that activating the transcription factor NRF2 will blast the physiological anti-inflammatory mechanisms, which will help combat pathologic inflammation. Much effort is being put in inhibiting the main NRF2 repressor, KEAP1, with either electrophilic small molecules or disrupters of the KEAP1/NRF2 interaction. However, targeting β-TrCP, the non-canonical repressor of NRF2, has not been considered yet. After in silico screening of ∼1 million compounds, we now describe a novel small molecule, PHAR, that selectively inhibits the interaction between β-TrCP and the phosphodegron in transcription factor NRF2. PHAR upregulates NRF2-target genes such as Hmox1, Nqo1, Gclc, Gclm and Aox1, in a KEAP1-independent, but β-TrCP dependent manner, breaks the β-TrCP/NRF2 interaction in the cell nucleus, and inhibits the β-TrCP-mediated in vitro ubiquitination of NRF2. PHAR attenuates hydrogen peroxide induced oxidative stress and, in lipopolysaccharide-treated macrophages, it downregulates the expression of inflammatory genes Il1b, Il6, Cox2, Nos2. In mice, PHAR selectively targets the liver and greatly attenuates LPS-induced liver inflammation as indicated by a reduction in the gene expression of the inflammatory cytokines Il1b, TNf, and Il6, and in F4/80-stained liver resident macrophages. Thus, PHAR offers a still unexplored alternative to current NRF2 activators by acting as a β-TrCP/NRF2 interaction inhibitor that may have a therapeutic value against undesirable inflammation.

MutDock: A computational docking approach for fixed-backbone protein scaffold design

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC₅₀ ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC₅₀ of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Structure-guided and phage-assisted evolution of a therapeutic anti-EGFR antibody to reverse acquired resistance

Acquired resistance to cetuximab in colorectal cancers is partially mediated by the acquisition of mutations located in the cetuximab epitope in the epidermal growth factor receptor (EGFR) ectodomain and hinders the clinical application of cetuximab. We develop a structure-guided and phage-assisted evolution approach for cetuximab evolution to reverse EGFR^S492R- or EGFR^G465R-driven resistance without altering the binding epitope or undermining antibody efficacy. Two evolved cetuximab variants, Ctx-VY and Ctx-Y104D, exhibit a restored binding ability with EGFR^S492R, which harbors the most common resistance substitution, S492R. Ctx-W52D exhibits restored binding with EGFR harboring another common cetuximab resistance substitution, G465R (EGFR^G465R). All the evolved cetuximab variants effectively inhibit EGFR activation and downstream signaling and induce the internalization and degradation of EGFR^S492R and EGFR^G465R as well as EGFR^WT. The evolved cetuximab variants (Ctx-VY, Ctx-Y104D and Ctx-W52D) with one or two amino acid substitutions in the complementarity-determining region inherit the optimized physical and chemical properties of cetuximab to a great extent, thus ensuring their druggability. Our data collectively show that structure-guided and phage-assisted evolution is an efficient and general approach for reversing receptor mutation-mediated resistance to therapeutic antibody drugs.

Acquired resistance to cetuximab can be mediated by generation of mutations in the EGFR ectodomain. Here the authors report a structure-guided and phage-assisted evolution approach for cetuximab evolution to reverse resistance without altering the binding epitope or undermining antibody efficacy.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Structure of an influenza group 2-neutralizing antibody targeting the hemagglutinin stem supersite

Several influenza antibodies with broad group 2 neutralization have recently been isolated. Here, we analyze the structure, class, and binding of one of these antibodies from an H7N9 vaccine trial, 315-19-1D12. The cryo-EM structure of 315-19-1D12 Fab in complex with the hemagglutinin (HA) trimer revealed the antibody to recognize the helix A region of the HA stem, at the supersite of vulnerability recognized by group 1-specific and by cross-group-neutralizing antibodies. 315-19-1D12 was derived from HV1-2 and KV2-28 genes and appeared to form a new antibody class. Bioinformatic analysis indicated its group 2 neutralization specificity to be a consequence of four key residue positions. We specifically tested the impact of the group 1-specific N33 glycan, which decreased but did not abolish group 2 binding of 315-19-1D12. Overall, this study highlights the recognition of a broad group 2-neutralizing antibody, revealing unexpected diversity in neutralization specificity for antibodies that recognize the HA stem supersite.

A short helix regulates conversion of dimeric and 24-meric ferritin architectures

The inter-subunit interaction at the protein interfaces plays a key role in protein self-assembly, through which enabling protein self-assembly controllable is of great importance for preparing the novel nanoscale protein materials with unexplored properties. Different from normal 24-meric ferritin, archaeal ferritin, Thermotoga maritima ferritin (TmFtn) naturally occurs as a dimer, which can assemble into a 24-mer nanocage induced by salts. However, the regulation mechanism of protein self-assembly underlying this phenomenon remains unclear. Here, a combination of the computational energy simulation and key interface reconstruction revealed that a short helix involved interactions at the C₄ interface are mainly responsible for the existence of such dimer. Agreeing with this idea, deletion of such short helix of each subunit triggers it to be a stable dimer, which losses the ability to reassemble into 24-meric ferritin in the presence of salts in solution. Further support for this idea comes from the observation that grafting a small helix from human H ferritin onto archaeal subunit resulted in a stable 24-mer protein nanocage even in the absence of salts. Thus, these findings demonstrate that adjusting the interactions at the protein interfaces appears to be a facile, effective approach to control subunit assembly into different protein architectures.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

Protein-protein interactions enhance the thermal resilience of SpyRing-cyclized enzymes: A molecular dynamic simulation study

Recently a technique based on the interaction between adhesion proteins extracted from Streptococcus pyogenes, known as SpyRing, has been widely used to improve the thermal resilience of enzymes, the assembly of biostructures, cancer cell recognition and other fields. It was believed that the covalent cyclization of protein skeleton caused by SpyRing reduces the conformational entropy of biological structure and improves its rigidity, thus improving the thermal resilience of the target enzyme. However, the effects of SpyTag/ SpyCatcher interaction with this enzyme are poorly understood, and their regulation of enzyme properties remains unclear. Here, for simplicity, we took the single domain enzyme lichenase from Bacillus subtilis 168 as an example, studied the interface interactions in the SpyRing by molecular dynamics simulations, and examined the effects of the changes of electrostatic interaction and van der Waals interaction on the thermal resilience of target enzyme. The simulations showed that the interface between SpyTag/SpyCatcher and the target enzyme is different from that found by geometric matching method and highlighted key mutations at the interface that might have effect on the thermal resilience of the enzyme. Our calculations highlighted interfacial interactions between enzyme and SpyTag/SpyCatcher, which might be useful in rational designs of the SpyRing.

Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

The animal reservoir of SARS-CoV-2 is unknown despite reports of various SARS-CoV-2-related viruses in Asian Rhinolophus bats^1-4, including the closest virus from R. affinis, RaTG13^5,6 and in pangolins^7-9. SARS-CoV-2 presents a mosaic genome, to which different progenitors contribute. The spike sequence determines the binding affinity and accessibility of its receptor-binding domain (RBD) to the cellular angiotensin-converting enzyme 2 (ACE2) receptor and is responsible for host range^10-12. SARS-CoV-2 progenitor bat viruses genetically close to SARS-CoV-2 and able to enter human cells through a human ACE2 pathway have not yet been identified, though they would be key in understanding the origin of the epidemics. Here we show that such viruses indeed circulate in cave bats living in the limestone karstic terrain in North Laos, within the Indochinese peninsula. We found that the RBDs of these viruses differ from that of SARS-CoV-2 by only one or two residues at the interface with ACE2, bind more efficiently to the hACE2 protein than the SARS-CoV-2 Wuhan strain isolated in early human cases, and mediate hACE2-dependent entry and replication in human cells, which is inhibited by antibodies neutralizing SARS-CoV-2. None of these bat viruses harbors a furin cleavage site in the spike. Our findings therefore indicate that bat-borne SARS-CoV-2-like viruses potentially infectious for humans circulate in Rhinolophus spp. in the Indochinese peninsula.

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.