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

Abstract

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.

Principles for computational design of binding antibodies

“…Antibody CDR backbones are stabilized by irregular interactions of backbone and amino acid side chains comprising both short-and long-range contacts, including buried polar networks. To overcome the challenges in designing such nonideal features, we developed an algorithm called AbDesign (14), which operates in three stages (Movie S1): First, natural antibody Fv backbones are segmented into constituent parts, and new backbones are designed by recombining segments from Significance Antibodies are the most versatile class of binding molecule known, and have numerous applications in biomedicine. Computational design of antibodies, however, poses unusual difficulties relative to previously designed proteins, as antibodies comprise multiple nonideal features, such as long and unstructured loops and buried charges and polar interaction networks.…”

Computational protein design for given backbone: recent progresses in general method-related aspects

“…In a repeat-protein design protocol, Parmeggiani et al have considered protein family-specific statistical restraints on structure and sequences [18]. For antibody design, Lapidoth et al proposed a protocol to construct antibody models of combinatorial complementarity-determining (CDR) regions by explicitly using CDRs (and respective sequence profiles) observed in a large number of experimental antibody structures [19]. The overall problem of protein design split into two sub-problems.…”

Protein structural motifs in prediction and design

“…This is because sequence statistics emergent from motif instances can report on important determinants of the targeted structure. Whereas contiguous fragments have been widely used for deriving such empirical sequence constraints in design [22,53,57], applications of multi-segment motifs towards this end sparser [52,56] [34]. Still, this represents a promising direction, especially as more structural data accumulate, with the prospect of providing quantitative sequence determinants of tertiary and quaternary structure, just as sequence statistics of local backbone fragments enabled the quantification of secondary-structural propensities.…”

“…Relying on the relatively large number of antibody entries in the PDB, their computational method, AbDesign, uses backbone fragments from aligned regions of antibody structures to combinatorially generate new putative templates. The sequence for each combination is optimized with a modified Rosetta design procedure, in which amino acids are constrained to those naturally found in the fragments [57,60]. …”