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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.

There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10 , 11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications. Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges.

Many peptide hormones form an α-helix on binding their receptors 1 – 4 , and sensitive methods for their detection could contribute to better clinical management of disease 5 . De novo protein design can now generate binders with high affinity and specificity to structured proteins 6 , 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 RFdiffusion 8 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. A study describes a direct computational approach without experimental optimization to design high-affinity proteins that bind small helical peptides.

Despite the central role that antibodies play in modern medicine, there is currently no method to design novel antibodies that bind a specific epitope entirely . Instead, antibody discovery currently relies on animal immunization or random library screening approaches. Here, we demonstrate that combining computational protein design using a fine-tuned RFdiffusion network alongside yeast display screening enables the generation of antibody variable heavy chains (VHHs) and single chain variable fragments (scFvs) that bind user-specified epitopes with atomic-level precision. To verify this, we experimentally characterized VHH binders to four disease-relevant epitopes using multiple orthogonal biophysical methods, including cryo-EM, which confirmed the proper Ig fold and binding pose of designed VHHs targeting influenza hemagglutinin and toxin B (TcdB). For the influenza-targeting VHH, high-resolution structural data further confirmed the accuracy of CDR loop conformations. While initial computational designs exhibit modest affinity, affinity maturation using OrthoRep enables production of single-digit nanomolar binders that maintain the intended epitope selectivity. We further demonstrate the de novo design of single-chain variable fragments (scFvs), creating binders to TcdB and a Phox2b peptide-MHC complex by combining designed heavy and light chain CDRs. Cryo-EM structural data confirmed the proper Ig fold and binding pose for two distinct TcdB scFvs, with high-resolution data for one design additionally verifying the atomically accurate conformations of all six CDR loops. Our approach establishes a framework for the rational computational design, screening, isolation, and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.

Many peptide hormones form an alpha-helix upon binding their receptors, and sensitive detection methods for them could contribute to better clinical management. De novo protein design can now generate binders with high affinity and specificity to structured proteins. However, the design of interactions between proteins and short helical peptides is an unmet challenge. Here, we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that with the RFdiffusion generative model, picomolar affinity binders can be generated to helical peptide targets either by noising and then denoising lower affinity designs generated with other methods, or completely de novo starting from random noise distributions; to our knowledge these are the highest affinity designed binding proteins against any protein or small molecule target generated directly by computation without any experimental optimization. The RFdiffusion designs enable the enrichment of parathyroid hormone or other bioactive peptides in human plasma and subsequent detection by mass spectrometry, and bioluminescence-based protein biosensors. Capture reagents for bioactive helical peptides generated using the methods described here could aid in the improved diagnosis and therapeutic management of human diseases.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Fixed misspelled author name* https://www.bakerlab.org/wp-content/uploads/2022/11/diffusion_animation_PTHbinder.gif

There has been considerable recent progress in designing new proteins using deep learning methods. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.Competing Interest StatementThe authors have declared no competing interest.

Feeding a growing world sustainably In the coming years, continued population growth, rising incomes, increasing meat and dairy consumption and expanding biofuel use will place unprecedented demands on the world's agriculture and natural resources. Can we meet society's growing food needs while reducing agriculture's environmental harm? Here, an international team of environmental and agricultural scientists uses new geospatial data and models to identify four strategies that could double food production while reducing environmental impacts. First, halt agricultural expansion. Second, close 'yield gaps' on underperforming lands. Third, increase cropping efficiency. And finally, we need to change our diets and shift crop production away from livestock feed, bioenergy crops and other non-food applications. Increasing population and consumption are placing unprecedented demands on agriculture and natural resources. Today, approximately a billion people are chronically malnourished while our agricultural systems are concurrently degrading land, water, biodiversity and climate on a global scale. To meet the world’s future food security and sustainability needs, food production must grow substantially while, at the same time, agriculture’s environmental footprint must shrink dramatically. Here we analyse solutions to this dilemma, showing that tremendous progress could be made by halting agricultural expansion, closing ‘yield gaps’ on underperforming lands, increasing cropping efficiency, shifting diets and reducing waste. Together, these strategies could double food production while greatly reducing the environmental impacts of agriculture.

INTRODUCTION While there may be microbial contributions to Alzheimer's disease (AD), findings have been inconclusive. We recently reported an AD‐associated CD83(+) microglia subtype associated with increased immunoglobulin G4 (IgG4) in the transverse colon (TC). METHODS We used immunohistochemistry (IHC), IgG4 repertoire profiling, and brain organoid experiments to explore this association. RESULTS CD83(+) microglia in the superior frontal gyrus (SFG) are associated with elevated IgG4 and human cytomegalovirus (HCMV) in the TC, anti‐HCMV IgG4 in cerebrospinal fluid, and both HCMV and IgG4 in the SFG and vagal nerve. This association was replicated in an independent AD cohort. HCMV‐infected cerebral organoids showed accelerated AD pathophysiological features (Aβ42 and pTau‐212) and neuronal death. DISCUSSION Findings indicate complex, cross‐tissue interactions between HCMV and the adaptive immune response associated with CD83(+) microglia in persons with AD. This may indicate an opportunity for antiviral therapy in persons with AD and biomarker evidence of HCMV, IgG4, or CD83(+) microglia. Highlights Cross‐tissue interaction between HCMV and the adaptive immune response in a subset of persons with AD. Presence of CD83(+) microglial associated with IgG4 and HCMV in the gut. CD83(+) microglia are also associated presence of HCMV and IgG4 in the cortex and vagal nerve. Replication of key association in an independent cohort of AD subjects. HCMV infection of cerebral organoids accelerates the production of AD neuropathological features.

Allogeneic hematopoietic cell transplantation (alloHCT), also known as stem cell or bone marrow transplantation, is a cellular therapy performed to treat a variety of malignant and non-malignant hematologic diseases. Chronic graft-versus-host disease (cGVHD) is a common immune-mediated complication of alloHCT that can affect various organs of the body, with approximately 70% of affected patients presenting with oral features. Oral manifestations of cGVHD include lichenoid lesions (diagnostic feature), erythema, pseudomembranous ulcerations, superficial mucoceles, salivary gland hypofunction, xerostomia, orofacial sclerosis, trismus, and increased sensitivity to spicy, acidic, hard, and crunchy foods. Patients with oral cGVHD are also at increased risk for developing secondary conditions, such as oral candidiasis, dental caries, and oral squamous cell carcinoma. Given these complex oral health challenges, the dental hygienist can play a key role in optimizing patients' oral health care from pre-stem cell transplantation through survivorship. Optimal care includes a comprehensive health history assessment, thorough extraoral and intraoral examinations, detailed hard and soft tissue evaluations, oral hygiene, and dietary assessment, along with the delivery of patient-centered, oral health instruction and preventive therapies. Appropriate monitoring and management of oral cGVHD require a collaborative care approach between dental, oncology, and oral medicine providers. As part of a multidisciplinary care team, dental hygienists play an important role in the management of patients with oral cGVHD. The purpose of this review is to provide an overview of alloHCT and its oral health considerations, with a focus on oral cGVHD etiology, signs and symptoms, and management considerations for the dental team.