Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups t...
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| Published in | Science (American Association for the Advancement of Science) Vol. 384; no. 6693; p. eadl2528 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
The American Association for the Advancement of Science
19.04.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
Advances in machine learning have made protein structure prediction and design much more accurate and accessible in recent years, but these tools have generally been limited to polypeptide chains. However, ligands such as small molecules, metal ions, and nucleic acids are crucial components of most proteins, both in terms of structure and biological function. Krishna
et al
. present a next-generation protein structure prediction and design tool, RoseTTAFold All-Atom, that can accept a wide range of ligands and covalent amino acid modifications. The authors demonstrate superior performance on protein-ligand structure prediction relative to other tools, even in the absence of an input experimental structure. They also perform de novo design of proteins to bind cofactors and small molecules and experimentally validate these designs. —Michael A. Funk |
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| AbstractList | Editor’s summaryAdvances in machine learning have made protein structure prediction and design much more accurate and accessible in recent years, but these tools have generally been limited to polypeptide chains. However, ligands such as small molecules, metal ions, and nucleic acids are crucial components of most proteins, both in terms of structure and biological function. Krishna et al. present a next-generation protein structure prediction and design tool, RoseTTAFold All-Atom, that can accept a wide range of ligands and covalent amino acid modifications. The authors demonstrate superior performance on protein-ligand structure prediction relative to other tools, even in the absence of an input experimental structure. They also perform de novo design of proteins to bind cofactors and small molecules and experimentally validate these designs. —Michael A. FunkINTRODUCTIONProteins rarely act alone; they form complexes with other proteins in cell signaling, interact with DNA and RNA during transcription and translation, and interact with small molecules both covalently and noncovalently during metabolism and signaling. Despite substantial recent progress in protein-only structure prediction, modeling such general biomolecular assemblies remains an outstanding challenge.RATIONALEWe set out to develop a unified structure prediction and design approach for assemblies containing proteins, nucleic acids, small molecules, metals, and chemical modifications. We sought to combine a sequence-based description of proteins and nucleic acids with an atomic graph representation of small molecules and protein covalent modifications. We started with the RoseTTAFold2 (RF2) network, which takes as input one-dimensional (1D) sequence information, 2D pairwise distance information from homologous templates, and 3D coordinate information and iteratively improves predicted structures through many hidden layers.RESULTSFor our biomolecular structure prediction network RoseTTAFold All-Atom (RFAA) (see the figure, top), we retained the representations of protein and nucleic acid chains from RF2 and represented arbitrary small molecules as atom-bond graphs. To the 1D track, we input the chemical element type of each nonpolymer atom; to the 2D track, the chemical bonds between atoms; and to the 3D track, information on chirality. Immediately after input, the full system was represented as a disconnected gas of amino acid residues, nucleic acid bases, and freely moving atoms, which was successively transformed through the many blocks of the network into physically plausible assembly structures. We trained RFAA on protein–small molecule, protein-metal, and covalently modified protein complexes that are found in the Protein Data Bank (PDB), filtering out common solvents and crystallization additives.In the CAMEO blind ligand-docking challenge, RFAA outperforms baseline automated pipelines. Although not all predictions are accurate, the network outputs a confidence estimate that correlates with accuracy. The network generalizes beyond the training set: Accurate predictions are made for proteins with low sequence homology (BLAST e-value >1) and ligands with low similarity (Tanimoto similarity <0.5) to those in the training set. Prediction accuracy is higher for protein–small molecule complexes with more-favorable computed interaction energies using a molecular mechanics force field, which suggests that RFAA has learned aspects of the physical chemistry of protein–small molecule interactions. Nearly half (46%) of covalent modifications are predicted accurately [<2.5-Å root mean square deviation (RMSD)]. These additional prediction capabilities do not come at the expense of the protein structure–prediction task because RFAA has a prediction accuracy on protein monomer structures comparable to that AlphaFold2.For small-molecule binder design, we developed RFdiffusion All-Atom (RFdiffusionAA) by fine-tuning RFAA on diffusion denoising tasks. Starting from random residue distributions, RFdiffusionAA generates folded protein structures that surround the small molecule. In contrast to previous approaches that rely on native or preexisting designed scaffolds, the binding pockets are custom generated for each ligand of interest. We generated small-molecule binding designs for the cardiac disease drug digoxigenin, the enzyme cofactor heme, and optically active bilin molecules. In each case, experimental characterization showed that a subset of the designs had the designed binding activity. The crystal structure of a heme binding design matched the RFdiffusionAA model very closely (0.86-Å Cα RMSD).CONCLUSIONRFAA demonstrates that a single neural network can be trained to accurately model general biomolecular assemblies containing a wide diversity of nonprotein components. Although there is still room for improvement in prediction accuracy, we anticipate that RFAA should be broadly useful for modeling full biological assemblies and RFdiffusionAA for designing small molecule–binding proteins and sensors. Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin. Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin. Advances in machine learning have made protein structure prediction and design much more accurate and accessible in recent years, but these tools have generally been limited to polypeptide chains. However, ligands such as small molecules, metal ions, and nucleic acids are crucial components of most proteins, both in terms of structure and biological function. Krishna et al . present a next-generation protein structure prediction and design tool, RoseTTAFold All-Atom, that can accept a wide range of ligands and covalent amino acid modifications. The authors demonstrate superior performance on protein-ligand structure prediction relative to other tools, even in the absence of an input experimental structure. They also perform de novo design of proteins to bind cofactors and small molecules and experimentally validate these designs. —Michael A. Funk Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin. |
| Author | Krishna, Rohith McHugh, Ryan Baker, David Wang, Jue Anishchenko, Ivan Hunter, C. Neil DiMaio, Frank Venkatesh, Preetham Hitchcock, Andrew Kang, Alex Vafeados, Dionne Baek, Minkyung Li, Xinting Bera, Asim K. Morey-Burrows, Felix S. Sutherland, George A. Ahern, Woody Sturmfels, Pascal Kalvet, Indrek Lee, Gyu Rie Humphreys, Ian R. Brackenbrough, Evans |
| Author_xml | – sequence: 1 givenname: Rohith orcidid: 0009-0003-6387-9622 surname: Krishna fullname: Krishna, Rohith organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 2 givenname: Jue orcidid: 0000-0002-1139-6640 surname: Wang fullname: Wang, Jue organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 3 givenname: Woody orcidid: 0009-0006-1247-8847 surname: Ahern fullname: Ahern, Woody organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA – sequence: 4 givenname: Pascal surname: Sturmfels fullname: Sturmfels, Pascal organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA – sequence: 5 givenname: Preetham orcidid: 0000-0002-0089-9365 surname: Venkatesh fullname: Venkatesh, Preetham organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA – sequence: 6 givenname: Indrek orcidid: 0000-0002-6610-2857 surname: Kalvet fullname: Kalvet, Indrek organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA – sequence: 7 givenname: Gyu Rie orcidid: 0000-0002-9119-5303 surname: Lee fullname: Lee, Gyu Rie organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA – sequence: 8 givenname: Felix S. orcidid: 0009-0005-2309-6062 surname: Morey-Burrows fullname: Morey-Burrows, Felix S. organization: School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK – sequence: 9 givenname: Ivan surname: Anishchenko fullname: Anishchenko, Ivan organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 10 givenname: Ian R. orcidid: 0000-0002-3058-6714 surname: Humphreys fullname: Humphreys, Ian R. organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 11 givenname: Ryan surname: McHugh fullname: McHugh, Ryan organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA – sequence: 12 givenname: Dionne orcidid: 0000-0002-2560-8907 surname: Vafeados fullname: Vafeados, Dionne organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 13 givenname: Xinting orcidid: 0000-0001-5559-7578 surname: Li fullname: Li, Xinting organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 14 givenname: George A. orcidid: 0000-0002-6319-4637 surname: Sutherland fullname: Sutherland, George A. organization: School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK – sequence: 15 givenname: Andrew orcidid: 0000-0001-6572-434X surname: Hitchcock fullname: Hitchcock, Andrew organization: School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK – sequence: 16 givenname: C. Neil orcidid: 0000-0003-2533-9783 surname: Hunter fullname: Hunter, C. Neil organization: School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK – sequence: 17 givenname: Alex orcidid: 0000-0001-5487-0499 surname: Kang fullname: Kang, Alex organization: Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 18 givenname: Evans orcidid: 0009-0004-1476-0219 surname: Brackenbrough fullname: Brackenbrough, Evans organization: Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 19 givenname: Asim K. orcidid: 0000-0001-9473-2912 surname: Bera fullname: Bera, Asim K. organization: Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 20 givenname: Minkyung orcidid: 0000-0003-3414-9404 surname: Baek fullname: Baek, Minkyung organization: School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea – sequence: 21 givenname: Frank orcidid: 0000-0002-7524-8938 surname: DiMaio fullname: DiMaio, Frank organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA – sequence: 22 givenname: David orcidid: 0000-0001-7896-6217 surname: Baker fullname: Baker, David organization: Department of Biochemistry, University of Washington, Seattle, WA 98105, USA., Institute for Protein Design, University of Washington, Seattle, WA 98105, USA., Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38452047$$D View this record in MEDLINE/PubMed |
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| Snippet | Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold... Editor’s summaryAdvances in machine learning have made protein structure prediction and design much more accurate and accessible in recent years, but these... |
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| SubjectTerms | Accuracy Additives Amino acids Amino Acids - chemistry Assemblies Binding Bond graphs Cell signaling Chemical bonds Chemical elements Chemistry Chirality Coronary artery disease Covalence Crystal structure Crystallization Crystallography Deep Learning Design Digoxigenin DNA - chemistry Gas pipelines Graph representations Graphical representations Heart diseases Heme Homology Ligands Machine learning Metal ions Metals Modelling Models, Molecular Networks Neural networks Nucleic acids Optical activity Organic Chemistry Physical chemistry Polypeptides Predictions Protein Engineering - methods Protein structure Proteins Proteins - chemistry Residues Similarity Structure-function relationships |
| Title | Generalized biomolecular modeling and design with RoseTTAFold All-Atom |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/38452047 https://www.proquest.com/docview/3040564452 https://www.proquest.com/docview/2954769956 |
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