[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124212-en":3,"doc-seo-124212-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124212,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization","Protein design seeks polypeptide sequences that fold into a specified three-dimensional structure, but exact physics-based solutions require nested searches over sequence and conformational space. Recent machine learning enables fast structure prediction and motivates rethinking sequence search. This work presents a general protein design scheme combining learned physics-based scoring with iterative, quantum-inspired optimization. Proof-of-concept results on a lattice protein model show rapid learning, promising designs, and performance gains over conventional sequence optimization even on classical hardware.","arXiv :2407 .07177v1 [ quant-ph] 9 Jul 2024  \nProtein Design by Integrating Machine Learning with Quantum Annealing and  \nQuantum-inspired Optimization  \nVeronica Panizza, 1, 2 Philipp Hauke, 1, 2, ∗ Cristian Micheletti,3 and Pietro Faccioli4  \n1 Pitaevskii BEC Center, Physics Department, Trento University, Via Sommarive 14, 38123 Povo (Trento), Italy.  \n2 INFN-TIFPA, Via Sommarive 14, 38123 Povo (Trento), Italy.  \n3 Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, I-34136 Trieste, Italy.†  \n4 Department of Physics University of Milano-Bicocca and INFN, Piazza della Scienza 3, I-20126 Milan, Italy.‡  \nThe protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure searches can now be bypassed thanks to recent machine learning breakthroughs, which have enabled accurate and rapid structure predictions. Similarly, sequence searches might be entirely transformed by the advent of quantum annealing machines and by the required new encodings of the search problem, which could be performative even on classical machines. In this work, we introduce a general protein design scheme where algorithmic and technological advancements in machine learning and quantum-inspired algorithms can be integrated, and an optimal physics-based scoring function is iteratively learned. In this first proof-of-concept application, we apply the iterative method to a lattice protein model amenable to exhaustive benchmarks, finding that it can rapidly learn a physics-based scoring function and achieve promising design performances. Strikingly, our quantum-inspired reformulation outperforms conventional sequence optimization even when adopted on classical machines. The scheme is general and can easily extended, e.g., to encompass off-lattice models, and it can integrate progress on various computational platforms, thus representing a new paradigm approach for protein design.  \nI. INTRODUCTION  \nIn contrast to random polypeptide chains, most naturally occurring proteins fold rapidly and reversibly into a unique conformation that is solely determined by the sequence of amino acids, called the native state. [1–3] . This property is consistent with the interpretation that the native state typically corresponds to the free-energy minimum of the peptide chain [4, 5] and is kinetically accessible from generic conformers of the unfolded ensemble [2, 3, 6, 7] . The unique thermodynamic properties of proteins and protein-like systems promoted by natural or artificial selection [2–5, 8–18], have long posed two fundamental challenges: (i) predicting protein structures given the chemical sequence and (ii) finding sequences that can fold onto a given target structure. These are known as protein folding and protein design problems, respectively. Because of their close connection, they are also called the direct and the inverse protein folding problems.  \nFrom a thermodynamic perspective, both challenges can be fully specified by defining the (effective) energy E(Γ, S) of a polypeptide chain as a function of its sequence, S, and its conformational state, Γ . By effective energy, we intend that E includes contributions from the thermodynamic integration of the solvent degrees of freedom. Solving the direct protein folding problem for a given polypeptide sequence S involves finding the conformer(s) Γ with the largest occupation probability in  \n∗ philipp.hauke@unitn.it † [cristian.micheletti@sissa.it](cristian.micheletti@sissa.it)[ ](cristian.micheletti@sissa.it)‡ [pietro.faccioli@unimib.it](pietro.faccioli@unimib.it)  \ncanonical equilibrium,  \ne −βE(Γ,S)  \nP(Γ|S) = ~~ ~~′~~ ~~ , (1)  \nPΓ′ e −βE(Γ ,S)  \nwhere β is the inverse thermal energy in physiological conditions and the sum in the denominator runs over the possible conformational states. Foldable polypeptide chains,","cbCaidolKwAlSRBb","https://ap.wps.com/l/cbCaidolKwAlSRBb","pdf",2313042,1,16,"English","en",105,"# Introduction\n## Protein folding and inverse folding\n## Physics-based formulations of direct and inverse problems\n## Computational challenges and motivation for ML and quantum-inspired methods\n## Proposed integrated scheme and proof-of-concept results","[{\"question\":\"What is the protein design problem addressed in this work?\",\"answer\":\"It is the inverse problem of finding polypeptide sequences that fold into a target three-dimensional structure.\"},{\"question\":\"Why is solving the protein design problem computationally demanding?\",\"answer\":\"Rigorous formulations require nested searches over both sequence space and conformational structure space.\"},{\"question\":\"How does the proposed scheme combine machine learning with quantum-inspired optimization?\",\"answer\":\"It iteratively learns an optimal physics-based scoring function and integrates it with quantum-inspired reformulation for sequence optimization.\"}]","Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization | 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is the protein design problem addressed in this work?","Question",{"text":75,"@type":76},"It is the inverse problem of finding polypeptide sequences that fold into a target three-dimensional structure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is solving the protein design problem computationally demanding?",{"text":80,"@type":76},"Rigorous formulations require nested searches over both sequence space and conformational structure space.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed scheme combine machine learning with quantum-inspired optimization?",{"text":84,"@type":76},"It iteratively learns an optimal physics-based scoring function and integrates it with quantum-inspired reformulation for sequence 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