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A Perspective on Protein Structure Prediction Using Quantum Computers.
Doga, Hakan; Raubenolt, Bryan; Cumbo, Fabio; Joshi, Jayadev; DiFilippo, Frank P; Qin, Jun; Blankenberg, Daniel; Shehab, Omar.
Afiliación
  • Doga H; IBM Quantum, Almaden Research Center, San Jose, California 95120, United States.
  • Raubenolt B; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • Cumbo F; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • Joshi J; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • DiFilippo FP; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • Qin J; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • Blankenberg D; Center for Computational Life Sciences, Lerner Research Institute, The Cleveland Clinic, Cleveland, Ohio 44106, United States.
  • Shehab O; IBM Quantum, IBM Thomas J Watson Research Center, Yorktown Heights, New York 10598, United States.
J Chem Theory Comput ; 20(9): 3359-3378, 2024 May 14.
Article en En | MEDLINE | ID: mdl-38703105
ABSTRACT
Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Teoría Cuántica Idioma: En Revista: J Chem Theory Comput Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Teoría Cuántica Idioma: En Revista: J Chem Theory Comput Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos