Novo Apk
Prisc Chandola
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Many peptide hormones form an α-helix on binding their receptors1,2,3,4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.
We set out to develop general methods for designing proteins that bind peptides in helical conformations. To fully leverage recent advances in protein design, we explored both parametric and deep learning-based approaches. For parametric generation, we reasoned that helical bundle scaffolds with an open groove for a helical peptide could provide a general solution to the helical peptide-binding problem: the extended interaction surface between the full length of the helical peptide target and the contacting helices on the designed scaffold could enable high-affinity and specific binding, and the helices flanking the groove could limit self-association of the recessed hydrophobic surfaces. In parallel, we reasoned that deep learning methods, which do not pre-specify scaffold geometries, could permit the exploration of different potential solutions to peptide binding.
We next explored using the RoseTTAFold-based denoising diffusion method RFdiffusion8. RFdiffusion directly generates protein structures with diverse topologies, and is much more compute efficient than Hallucination. We first extended RFdiffusion to enable optimization of existing helical peptide-binders.
Antibodies have served as the industry standard for affinity reagents for many years, but their use is often hampered by variable specificity and stability10,11. For binding helical peptides, the computationally designed helical scaffolds described in this paper have a number of structural and biochemical advantages. First, the extensive burial of the full length of an extended helix is difficult to accomplish with antibody loops15, but very natural with matching extended α-helices in groove-shaped scaffolds. Second, designed scaffolds are more amenable to incorporation into sensors as illustrated by the lucCagePTH sensor. Third, they are more stable than antibodies, can be produced much less expensively, and can be easily incorporated into affinity matrices for enrichment of peptide hormones from human serum (the striking difference in the robustness of antibody-conjugated versus binder-conjugated beads to repeated use (Fig. 4d, right) highlights the differences in stability of the two modalities). Fourth, computational design avoids the need to immunize animals, which often mount weak responses to highly conserved bioactive molecules42. MS-based detection of peptides following enrichment using designed binders could provide a general route forwards for serological detection of a wide range of disease-associated peptide biomarkers.
Our results highlight the emergence of powerful new deep learning methods for protein design. The RFjoint and RFdiffusion methods were both able to improve on initial Rosetta designs, and the Hallucination approach generated high-affinity binders without requiring pre-specification of the bound structures. Moreover, the RFdiffusion method rapidly generated very tight (picomolar Kd values) affinity and specific binders to several helical peptides. RFdiffusion was previously shown to be able to design binders to folded targets8; here we demonstrate further that it can be used to improve starting designs by partial noising and denoising, and can generate binders to peptides starting from no information other than the target sequence. To our knowledge, the Bim- and PTH-binding proteins diffused starting from random noise are the highest-affinity binders to any target (protein, peptide or small molecule) achieved directly by computational design with no experimental optimization. We expect both the RFdiffusion de novo peptide binder design capability and the ability to resample around initial designs (before or after experimental characterization) to be broadly applicable.
To sample around an initial putative binder, and to extend the binding interface to make additional contacts with the bound peptide, the RFjoint Inpainting network was used23, in conjunction with ProteinMPNN24. Rosetta-designed binders to PTH, GCG and NPY were used as input to RFjoint. RFjoint is deterministic, and hence, to generate diversity, additional length was added (randomly and independently sampled) at the loop junctions between the binder helices. Additionally, one whole helix was completely rebuilt by RFjoint, to further permit diversification. RFjoint designs were subsequently sequence redesigned with ProteinMPNN, validated and filtered in silico by AF2 with initial guess6,28, and subsequently tested experimentally.
Radius of gyration: the radius of gyration was calculated as the mean squared distance of residues from the centre of mass of the protein. To approximately standardize the scaling with length of the protein, this was empirically normalized by dividing the radius of gyration by the radius of a sphere of volume related to the length of the Hallucinated protein.
To optimize the designed binder, Monte Carlo simulated annealing was carried out, with a starting temperature of 0.01, and the half-life of the exponential decay set to 500 steps. Alterations were accepted or rejected using the Metropolis criterion. A total of 5,000 steps were carried out during design.
Previous work has demonstrated that AF2 Hallucination yields adversarial sequences that do not work experimentally31. However, designs can be rescued with ProteinMPNN redesign of the sequences. Sixty-four sequences were designed per backbone, and were subsequently filtered on the basis of AF2 pLDDT, pTM, RMSD to the design model, RMSD of the monomer to the binder model (without the peptide), and Rosetta ddG. The precise values used for filtering were chosen to reduce the set down to 46 designs.
A modified version of RFdiffusion was trained to permit the design of protein binders to targets, for which only the sequence of the target was specified. The training strategy largely followed the training strategy used for the original RFdiffusion model, with some modifications. A summary is provided below.
Atomic models of the GCG binders designed with Inpainting and partial diffusion (Fig. 2c), the Bim binder (Fig. 3d) and PTH peptide have been uploaded to the PDB with the accession codes 8GJG, 8GJI, 8T5E and 8T5F, respectively. Sequences of the binders described in this paper are in Extended Data Table 1.
Code for the parametric design pipeline can be found at _paper/tree/main/projects/parametric_groove_design. Code to run RFjoint Inpainting can be found at Computational notebooks for the sequence-threading pipeline can be found at _paper/tree/main/projects/threading. Partial-diffusion code explanation and examples can be found at -diffusion. Code explanation and examples of binder design using RFdiffusion can be found at -design. An explanation of how to implement potentials, including the radius of gyration can be found at -auxiliary-potentials. Code to run AF2 Hallucination for peptide design is available at _peptide_hallucination.
D.B., S.V.T., P.J.Y.L., P.V., I.D.L., A.N.H., D.J., E.H., A.H.-W.Y., H.-H.H., J.L.W., M.J.M., N.R.B. and G.R.L. are inventors on a provisional patent application submitted by the University of Washington for the design and composition of the proteins created in this study.
Recent changes to the Food and Drug Administration Modernization Act of 1997 have facilitated the review and approval of novel devices. The process of classification of the de novo mechanism is one such change.
As such, device manufacturers should understand what a de novo classification is, the circumstances under which its use is appropriate, and scenarios under which device manufacturers can obtain a de novo classification.
As part of the Food and Drug Administration Modernization Act of 1997, the de novo classification pathway functions as an alternative means of classifying low- to moderate-risk devices. Traditionally, these devices were automatically classified as class III devices after the Food and Drug Administration determined that they are not substantially equivalent during review of a 510(k) application.
The Food and Drug Administration considers the de novo classification to be appropriate for devices that have not been classified under section 513(a)(1) of the Federal Food, Drug, and Cosmetic Act. These devices do not fit into any particular class, have no equivalent device that is currently marketed, or have not been determined to be substantially equivalent as the result of a 510(k) application.
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