title: Computational de novo design of antibodies binding to a peptide with high affinity.
abstract: Antibody drugs play a critical role in infectious diseases, cancer, autoimmune diseases, and inflammation. However, experimental methods for the generation of therapeutic antibodies such as using immunized mice or directed evolution remain time consuming and cannot target a specific antigen epitope. Here, we describe the application of a computational framework called OptMAVEn combined with molecular dynamics to de novo design antibodies. Our reference system is antibody 2D10, a single-chain antibody (scFv) that recognizes the dodecapeptide DVFYPYPYASGS, a peptide mimic of mannose-containing carbohydrates. Five de novo designed scFvs sharing less than 75% sequence similarity to all existing natural antibody sequences were generated using OptMAVEn and their binding to the dodecapeptide was experimentally characterized by biolayer interferometry and isothermal titration calorimetry. Among them, three scFvs show binding affinity to the dodecapeptide at the nM level. Critically, these de novo designed scFvs exhibit considerably diverse modeled binding modes with the dodecapeptide. The results demonstrate the potential of OptMAVEn for the de novo design of thermally and conformationally stable antibodies with high binding affinity to antigens and encourage the targeting of other antigen targets in the future. Biotechnol. Bioeng. 2017;114: 1331-1342. © 2017 Wiley Periodicals, Inc.
authors.affiliation: Department of Chemical Engineering, University Park, Pennsylvania, 16802.|Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, 16802.
authors.forename: Venkata Giridhar
authors.initials: VG
authors.lastname: Poosarla
authors.affiliation: Department of Chemical Engineering, University Park, Pennsylvania, 16802.
authors.forename: Tong
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Physics and Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801.
authors.forename: Boon Chong
authors.initials: BC
authors.lastname: Goh
authors.affiliation: Department of Physics and Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801.
authors.forename: Klaus
authors.initials: K
authors.lastname: Schulten
authors.affiliation: Department of Chemical Engineering, University Park, Pennsylvania, 16802.|Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, 16802.
authors.forename: Thomas K
authors.initials: TK
authors.lastname: Wood
authors.affiliation: Department of Chemical Engineering, University Park, Pennsylvania, 16802.
authors.forename: Costas D
authors.initials: CD
authors.lastname: Maranas
mesh_terms: D000911:Antibodies, Monoclonal / Q000737:chemistry* / Q000276:immunology / Q000648:ultrastructure; D001665:Binding Sites; D015195:Drug Design; D018604:Epitope Mapping / Q000379:methods*; D008956:Models, Chemical; D018448:Models, Immunological; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry* / Q000276:immunology; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*
chemical_list: D000911:Antibodies, Monoclonal; D010455:Peptides
keywords: OptMAVEn; antibody structure prediction; biolayer interferometry; computational antibody design; de novo design; single-chain antibodies