AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences

By Gideon Lapidoth, Dror Baran, G. Pszolla, Christoffer H Norn, A. Alon, Michael D. Tyka, Sarel Jacob Fleishman. 2015

118 citations recorded by the source corpus.

Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function—essential to exert control over all polypeptide degrees of freedom—remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high‐affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root‐mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti‐lysozyme antibodies, complementarity‐determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity. Proteins 2015; 83:1385–1406. © 2015 Wiley Periodicals, Inc.

Subfields: Molecular Biology, Oncology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022, methodology): “2 Related Work Computational Antibody Design Conventional computational approaches are mostly based on sampling algorithms over hand-crafted and statistical energy functions and iteratively modify protein sequences and structures [1, 29, 49, 36, 38].”
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “Lapidoth et al. have presented AbDesign [47] that follows a similar methodology to Opt-MAVEn, breaking up antibodies into V regions and CDR3 by analogy to V(D)J recombination.”
Assessing and enhancing foldability in designed proteins (2021, methodology): “Our strategy is based on recent methods developed in our lab to design new backbones through the modular assembly of large (60–150 amino acid) backbone fragments of natural enzymes.(25,30,31) Modular assembly and design was successful in generating accurate antibodies(27) and ultrahigh specificity b”
AbPredict 2: A server for accurate and unstrained structure prediction of antibody variable domains (2018, methodology): “In each conformational move, the method randomly samples from precomputed databases a backbone conformation belonging to the Vl, Vh, LCDR3, HCDR3 or the RBO, followed by combinatorial sidechain packing and sidechain and backbone minimization to reduce stereochemical strain(Lapidoth et al., 2015).”