5NBI

Principles for computational design of antibodies


Experimental Data Snapshot

  • Method: X-RAY DIFFRACTION
  • Resolution: 2.1 Å
  • R-Value Free: 0.258 
  • R-Value Work: 0.218 

wwPDB Validation 3D Report Full Report


This is version 1.4 of the entry. See complete history

Literature

Principles for computational design of binding antibodies.

Baran, D.Pszolla, M.G.Lapidoth, G.D.Norn, C.Dym, O.Unger, T.Albeck, S.Tyka, M.D.Fleishman, S.J.

(2017) Proc. Natl. Acad. Sci. U.S.A. 114: 10900-10905

  • DOI: 10.1073/pnas.1707171114
  • Primary Citation of Related Structures:  

  • PubMed Abstract: 
  • Natural proteins must both fold into a stable conformation and exert their molecular function. To date, computational design has successfully produced stable and atomically accurate proteins by using so-called "ideal" folds rich in regular secondary ...

    Natural proteins must both fold into a stable conformation and exert their molecular function. To date, computational design has successfully produced stable and atomically accurate proteins by using so-called "ideal" folds rich in regular secondary structures and almost devoid of loops and destabilizing elements, such as cavities. Molecular function, such as binding and catalysis, however, often demands nonideal features, including large and irregular loops and buried polar interaction networks, which have remained challenging for fold design. Through five design/experiment cycles, we learned principles for designing stable and functional antibody variable fragments (Fvs). Specifically, we ( i ) used sequence-design constraints derived from antibody multiple-sequence alignments, and ( ii ) during backbone design, maintained stabilizing interactions observed in natural antibodies between the framework and loops of complementarity-determining regions (CDRs) 1 and 2. Designed Fvs bound their ligands with midnanomolar affinities and were as stable as natural antibodies, despite having >30 mutations from mammalian antibody germlines. Furthermore, crystallographic analysis demonstrated atomic accuracy throughout the framework and in four of six CDRs in one design and atomic accuracy in the entire Fv in another. The principles we learned are general, and can be implemented to design other nonideal folds, generating stable, specific, and precise antibodies and enzymes.


    Organizational Affiliation

    Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.,Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel; sarel@weizmann.ac.il.,The Israeli Structural Proteomics Center, Weizmann Institute of Science, Rehovot 76100, Israel.,Google, Inc., Mountain View, CA 94043.




Macromolecules

Find similar proteins by: Sequence  |  Structure

Entity ID: 1
MoleculeChainsSequence LengthOrganismDetails
Design of antibodies
H
233N/AMutation(s): 0 
Protein Feature View is not available: No corresponding UniProt sequence found.
Entity ID: 2
MoleculeChainsSequence LengthOrganismDetails
Design of antibodies
L
218N/AMutation(s): 0 
Protein Feature View is not available: No corresponding UniProt sequence found.
Experimental Data & Validation

Experimental Data

  • Method: X-RAY DIFFRACTION
  • Resolution: 2.1 Å
  • R-Value Free: 0.258 
  • R-Value Work: 0.218 
  • Space Group: P 31 2 1
Unit Cell:
Length (Å)Angle (°)
a = 67.909α = 90.00
b = 67.909β = 90.00
c = 182.181γ = 120.00
Software Package:
Software NamePurpose
iMOSFLMdata reduction
PHASERphasing
SCALEPACKdata scaling
REFMACrefinement

Structure Validation

View Full Validation Report or Ramachandran Plots



Entry History 

Deposition Data

Revision History 

  • Version 1.0: 2017-09-27
    Type: Initial release
  • Version 1.1: 2017-10-18
    Type: Database references
  • Version 1.2: 2017-11-08
    Type: Database references
  • Version 1.3: 2018-09-12
    Type: Data collection, Source and taxonomy
  • Version 1.4: 2019-02-20
    Type: Advisory, Data collection, Derived calculations