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Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency. Recently, a pipeline for the design of protein-binding proteins using only the structure of the target protein was reported. Here, the authors report that the incorporation of deep learning methods into the original pipeline increases experimental success rate by ten-fold.

Two amino acids (lysine at position 627 or asparagine at position 701) in the polymerase subunit PB2 protein are considered critical for the adaptation of avian influenza A viruses to mammals. However, the recently emerged pandemic H1N1 viruses lack these amino acids. Here, we report that a basic amino acid at position 591 of PB2 can compensate for the lack of lysine at position 627 and confers efficient viral replication to pandemic H1N1 viruses in mammals. Moreover, a basic amino acid at position 591 of PB2 substantially increased the lethality of an avian H5N1 virus in mice. We also present the X-ray crystallographic structure of the C-terminus of a pandemic H1N1 virus PB2 protein. Arginine at position 591 fills the cleft found in H5N1 PB2 proteins in this area, resulting in differences in surface shape and charge for H1N1 PB2 proteins. These differences may affect the protein's interaction with viral and/or cellular factors, and hence its ability to support virus replication in mammals.

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na V 1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na V 1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery. The authors in this work introduce RosettaVS, an AI-accelerated open-source drug discovery platform. They apply this tool to multi-billion compound libraries, where it was able to identify compounds that bind important targets KLHDC2 and Na V 1.7.

The development of native-like HIV-1 envelope (Env) trimer antigens has enabled the induction of neutralizing antibody (NAb) responses against neutralization-resistant HIV-1 strains in animal models. However, NAb responses are relatively weak and narrow in specificity. Displaying antigens in a multivalent fashion on nanoparticles (NPs) is an established strategy to increase their immunogenicity. Here we present the design and characterization of two-component protein NPs displaying 20 stabilized SOSIP trimers from various HIV-1 strains. The two-component nature permits the incorporation of exclusively well-folded, native-like Env trimers into NPs that self-assemble in vitro with high efficiency. Immunization studies show that the NPs are particularly efficacious as priming immunogens, improve the quality of the Ab response over a conventional one-component nanoparticle system, and are most effective when SOSIP trimers with an apex-proximate neutralizing epitope are displayed. Their ability to enhance and shape the immunogenicity of SOSIP trimers make these NPs a promising immunogen platform. Nanoparticles are a promising approach to increase immunogenicity of protein antigens for vaccines. Here, Brouwer et al . design self-assembling, two-component protein NPs that present native-like SOSIP trimers of HIV envelope protein and determine immunogenicity in a small animal model.

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational “anchor extension” methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC 50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain. Cyclic peptides are of particular interest due to their pharmacological properties, but their design for binding to a target protein is challenging. Here, the authors present a computational “anchor extension” methodology for de novo design of cyclic peptides that bind to the target protein with high affinity, and validate the approach by developing cyclic peptides that inhibit histone deacetylases 2 and 6.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model—despite weaknesses including a noisy data set—can be used to substantially increase the stability of both expert-designed and model-generated proteins.

The RGD (Arg-Gly-Asp)-binding integrins αvβ6 and αvβ8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between homologous αvβ6 and αvβ8 and other RGD integrins, stabilize specific conformational states, and have high thermal stability could have considerable therapeutic utility. Existing small molecule and antibody inhibitors do not have all these properties, and hence new approaches are needed. Here we describe a generalized method for computationally designing RGD-containing miniproteins selective for a single RGD integrin heterodimer and conformational state. We design hyperstable, selective αvβ6 and αvβ8 inhibitors that bind with picomolar affinity. CryoEM structures of the designed inhibitor-integrin complexes are very close to the computational design models, and show that the inhibitors stabilize specific conformational states of the αvβ6 and the αvβ8 integrins. In a lung fibrosis mouse model, the αvβ6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity. Roy et al. describe a generalized method for computationally designing miniproteins selective for a single integrin heterodimer and conformational state. The designed αvβ6 inhibitor remains monomeric and maintains biological activity following aerosolization and shows excellent efficacy in bleomycin induced lung fibrosis.

Selection of aptamers from nucleic acid libraries by in vitro evolution represents a powerful method of identifying high-affinity ligands for a broad range of molecular targets. Nevertheless, a sizeable fraction of proteins remain difficult targets due to inherently limited chemical diversity of nucleic acids. We have exploited synthetic nucleotide modifications that confer protein-like diversity on a nucleic acid scaffold, resulting in a new generation of binding reagents called SOMAmers (Slow Off-rate Modified Aptamers). Here we report a unique crystal structure of a SOMAmer bound to its target, platelet-derived growth factor B (PDGF-BB). The SOMAmer folds into a compact structure and exhibits a hydrophobic binding surface that mimics the interface between PDGF-BB and its receptor, contrasting sharply with mainly polar interactions seen in traditional protein-binding aptamers. The modified nucleotides circumvent the intrinsic diversity constraints of natural nucleic acids, thereby greatly expanding the structural vocabulary of nucleic acid ligands and considerably broadening the range of accessible protein targets.

There has been some success in designing stable peptide filaments; however, mimicking the reversible assembly of many natural protein filaments is challenging. Dynamic filaments usually comprise independently folded and asymmetric proteins and using such building blocks requires the design of multiple intermonomer interfaces. Shen et al. report the design of self-assembling helical filaments based on previously designed stable repeat proteins. The filaments are micron scale, and their diameter can be tuned by varying the number of repeats in the monomer. Anchor and capping units, built from monomers that lack an interaction interface, can be used to control assembly and disassembly. Science , this issue p. 705 A general computational approach allows design of self-assembling helical filaments from monomeric proteins. We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo–electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units. The assembly and disassembly of the filaments can be controlled by engineered anchor and capping units built from monomers lacking one of the interaction surfaces. The ability to generate dynamic, highly ordered structures that span micrometers from protein monomers opens up possibilities for the fabrication of new multiscale metamaterials.

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone 1 – 3 . Here, inspired by natural and re-engineered protein–peptide systems 4 – 11 , we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone 12 . The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins. A computational approach is used to design modular proteins that bind to synthetic peptides and disordered regions of human proteins with high affinity and specificity.