Abstract:We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broad… Show more
“…In Stage 4, like some previous DMS efforts (Doud & Bloom, 2016), we directly sequenced the coding region from the clone population to determine variant frequency before and after selection. Use of tiled amplicons enables individual template molecules to be sequenced on both strands, allowing elimination of most base‐calling errors (Fowler et al , 2010; Whitehead et al , 2012; Zhang et al , 2016) (see Materials and Methods for details). This reduction in base‐calling error allows us to more accurately measure lower allele frequencies in mutagenized libraries.…”
Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.
“…In Stage 4, like some previous DMS efforts (Doud & Bloom, 2016), we directly sequenced the coding region from the clone population to determine variant frequency before and after selection. Use of tiled amplicons enables individual template molecules to be sequenced on both strands, allowing elimination of most base‐calling errors (Fowler et al , 2010; Whitehead et al , 2012; Zhang et al , 2016) (see Materials and Methods for details). This reduction in base‐calling error allows us to more accurately measure lower allele frequencies in mutagenized libraries.…”
Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.
“…Of special interest, it has been demonstrated that mutations of non-contacting residues near the interface of a protein-protein interaction can enhance the electrostatic charge complementarity resulting in improved affinity, specifically by reducing the k off rate. 29,30 10.1080/19420862.2018.1490119-F0002Figure 2.Molecular interaction of key residues of the designed Fc variants. An interaction map was calculated using the Rosetta REF2015 score function to define the energy of interaction between residues.…”
Engineering of antibodies for improved pharmacokinetics through enhanced binding to the neonatal Fc receptor (FcRn) has been demonstrated in transgenic mice, non-human primates and humans. Traditionally, such approaches have largely relied on random mutagenesis and display formats, which fail to address related critical attributes of the antibody, such as effector functions or biophysical stability. We have developed a structure- and network-based framework to interrogate the engagement of IgG with multiple Fc receptors (FcRn, C1q, TRIM21, FcγRI, FcγRIIa/b, FcγRIIIa) simultaneously. Using this framework, we identified features that govern Fc-FcRn interactions and identified multiple distinct pathways for enhancing FcRn binding in a pH-specific manner. Network analysis provided a novel lens to study the allosteric impact of half-life-enhancing Fc mutations on FcγR engagement, which occurs distal to the FcRn binding site. Applying these principles, we engineered a panel of unique Fc variants that enhance FcRn binding while maintaining robust biophysical properties and wild type-like binding to activating receptors. An antibody harboring representative Fc designs demonstrates a half-life improvement of > 9 fold in transgenic mice and > 3.5 fold in cynomolgus monkeys, and maintains robust effector functions such as antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity.
“…To confirm the designed binding mode and identify possible avenues for improving the computational design, we used PCR mutagenesis in concert with one round of fluorescent-activated cell sorting (FACS) and next-generation sequencing, resulting in a fine-resolution map of the sequencefunction landscape (18,19). Sequencing of the C-terminal 51 positions, which contain the designed binding site, was carried out before and after selection for IgG binding.…”
Computational design provides the opportunity to program protein-protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (IgG) His-433. Using next-generation sequencing of naïve and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds IgG with a K d of ∼4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for IgG affinity purification and diagnostic devices.computational interface design | pH-sensitivity | antibody purification
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