De novo design of immunoglobulin-like domains

Antibodies, and antibody derivatives such as nanobodies, contain immunoglobulin-like (Ig) β-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here, we develop design rules for the central feature of the Ig fold architecture—the non-local cross-β structure connecting the two β-sheets—and use these to design highly stable Ig domains de novo, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of antibody-like scaffolds with tailored structures and superior biophysical properties.This research was supported by grants from the Spanish Ministry of Science and Innovation (RYC2018-025295-I, EUR2020-112164, and PID2020-120098GA-I00). This study was also supported in part by grants from Spanish and Catalan public and private bodies (grant/fellowship references MCIN/AEI/10.13039/501100011033/PID2019-107725RG-I00, 2017SGR3 and Fundació “La Marató de TV3” 201815). S.R.M. acknowledges grant BES2016-076877 from the Spanish State Agency for Research (MCIN/AEI/10.13039/501100011033) and the European Social Fund “ESF invests in your future”. U.E. was funded by a Beatriu de Pinós post-doctoral fellowship (AGAUR-MSCA COFUND 2018BP00163. J.R.T. was supported by an EMBO postdoctoral fellowship (under grant agreement ALTF 145-2021). J.C.K. was supported by a National Science Foundation Graduate Research Fellowship (grant DGE-1256082). D.B. and T.M.C. acknowledge the Howard Hughes Medical Institute. We thank the Princess Margaret Cancer Centre for funding of the NMR facility. The Structural Genomics Consortium is a registered charity (no: 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute [OGI-196], EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN grant 875510], Janssen, Merck KGaA (aka EMD in Canada and US), Pfizer and Takeda

Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV’s Envelope Protein on Viral Replication in Cell Culture

<div><p>HIV is notorious for its capacity to evade immunity and anti-viral drugs through rapid sequence evolution. Knowledge of the functional effects of mutations to HIV is critical for understanding this evolution. HIV’s most rapidly evolving protein is its envelope (Env). Here we use deep mutational scanning to experimentally estimate the effects of all amino-acid mutations to Env on viral replication in cell culture. Most mutations are under purifying selection in our experiments, although a few sites experience strong selection for mutations that enhance HIV’s replication in cell culture. We compare our experimental measurements of each site’s preference for each amino acid to the actual frequencies of these amino acids in naturally occurring HIV sequences. Our measured amino-acid preferences correlate with amino-acid frequencies in natural sequences for most sites. However, our measured preferences are less concordant with natural amino-acid frequencies at surface-exposed sites that are subject to pressures absent from our experiments such as antibody selection. Our data enable us to quantify the inherent mutational tolerance of each site in Env. We show that the epitopes of broadly neutralizing antibodies have a significantly reduced inherent capacity to tolerate mutations, rigorously validating a pervasive idea in the field. Overall, our results help disentangle the role of inherent functional constraints and external selection pressures in shaping Env’s evolution.</p></div

Sites of recurrent cell-culture mutations mapped on Env’s structure.

<p>The 25 sites from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.g002" target="_blank">Fig 2B</a> where the mutation frequency increased >3-fold in at least two replicates after cell-culture passage. <b>(A)</b> Trimeric Env (PDB 5FYK [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref073" target="_blank">73</a>]) with one monomer in grey and the others in white, oriented so the membrane-proximal region is at the bottom. Sites of cell-culture mutations are shown as spheres, colored red-to-blue according to primary sequence. Most of these sites fall in one of three clusters. Mutations in the first cluster disrupt potential glycosylation sites at Env’s apex. The second cluster includes or is adjacent to sites where mutations are known to affect Env’s conformational dynamics [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref074" target="_blank">74</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref075" target="_blank">75</a>]. <b>(B)</b> The third cluster is near the co-receptor binding surface. This panel shows an apex-down view of monomeric gp120 (grey) in complex with CD4 (green) (PDB 3JWD [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref048" target="_blank">48</a>]). Sites of recurrent cell-culture mutations are shown as spheres colored according to primary sequence as in panel A. The black bar indicates cropping of CD4. <b>(C)</b> The same view as panel B, but the spheres now show sites known to affect binding to CXCR4 [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref010" target="_blank">10</a>] or CCR5 [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref076" target="_blank">76</a>]. Note the extensive overlap between the spheres in this panel and panel B.</p

Correlation of amino-acid preferences with amino-acid frequencies in nature.

<p>Correlation of amino-acid preferences with amino-acid frequencies in nature.</p

Our experimental estimates are mostly concordant with existing knowledge about the effects of mutations to functionally or structurally important parts of Env.

<p>Our experimental estimates are mostly concordant with existing knowledge about the effects of mutations to functionally or structurally important parts of Env.</p

Selection purged mutations in most of <i>env</i>, but favored mutations at a few sites.

<p><b>(A)</b> For each replicate, we deep sequenced the initial plasmids (DNA) and the viruses after two rounds of passaging (P2). Bars show the per-codon mutation frequency averaged across sites after subtracting error rates determined from the wildtype controls (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.s005" target="_blank">S2 Fig</a>). When mutation frequencies are averaged across all sites, selection purged stop codons to <1% of their frequency in the initial DNA. Selection only slightly reduced the average frequency of nonsynonymous mutations; however, this average results from two distinct trends. For ≈4% of sites, the frequency of nonsynonymous mutations in the twice-passaged viruses (<i>f</i>^<i>P</i>2) increased >3-fold relative to the frequency in the initial plasmid DNA (<i>f</i>^<i>DNA</i>). For all other sites, the frequency of nonsynonymous mutations decreased substantially after selection. <b>(B)</b> The sites at which the error-corrected mutation frequency increased >3-fold are similar between replicates, indicating consistent selection for tissue-culture adaptation at a few positions. The left Venn diagram shows the overlap among replicates in the sites with a >3-fold increase. The right Venn diagram shows the expected overlap if the same number of sites per replicate are randomly drawn from Env’s primary sequence. This difference is statistically significant, with <i>P</i> < 10⁻⁴ when comparing the actual overlap among all three replicates to the random expectation. Another summary view of selection on <i>env</i> is provided by <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.s007" target="_blank">S4</a> and <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.s008" target="_blank">S5</a> Figs.</p

Broadly neutralizing antibody epitopes have significantly lower mutational tolerance than other sites in Env.

<p>Broadly neutralizing antibody epitopes have significantly lower mutational tolerance than other sites in Env.</p

Selection depleted multi-nucleotide codon mutations in the Rev-response element (RRE).

<p>This plot shows a 51-codon sliding-window average of the fold change in per-codon multi-nucleotide mutation frequency after two rounds of viral passage, with data plotted for the center point in each window. The strongest depletion of both synonymous and nonsynonymous mutations occurred in the RRE, which is an RNA secondary structure important for viral replication.</p

The amino-acid preferences are modestly correlated among experimental replicates, but the sites tolerate similar numbers of amino acids and prefer similar amino acids across replicates.

<p><b>(A)</b> Correlations between the site-specific amino-acid preferences from each replicate. <b>(B)</b> Correlations between the effective number of amino acids tolerated per site. For each site <i>r</i>, the effective number of tolerated amino acids is , where <i>H</i>_<i>r</i> is the Shannon entropy of that site’s amino-acid preferences. This number ranges between 1 and 20, with 20 indicating all amino acids are preferred equally and 1 indicating only a single amino acid is preferred. <b>(C)</b> Correlations between the preference-weighted hydrophobicities. For each site <i>r</i>, the preference-weighted hydrophobicity is ∑_<i>a</i> <i>π</i>_{<i>r</i>,<i>a</i>} × <i>X</i>_<i>a</i> where <i>π</i>_{<i>r</i>,<i>a</i>} is the preference of <i>r</i> for amino acid <i>a</i>, and <i>X</i>_<i>a</i> is the Kyte-Doolittle hydropathy [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.ref090" target="_blank">90</a>] of <i>a</i>. The fact that both the effective number of tolerated amino acids and the hydrophobicities are more correlated than the amino-acid preferences means that when different amino acids are preferred at a site in different experimental replicates, the number and chemical properties of the preferred amino acids are similar. Each plot shows the Pearson correlation coefficient and associated P-value. Similar data for replicates 3b-1 and 3b-2 are in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006114#ppat.1006114.s009" target="_blank">S6 Fig</a>. The plots in this and subsequent figures show all 20 amino-acid preferences for each site; although only 19 of these preferences are independent parameters, all 20 values are shown because otherwise the correlation will depend on which value is excluded.</p

Deep mutational scanning workflow.

<p><b>(A)</b> We created libraries of HIV proviral plasmids with random codon mutations in <i>env</i>, and generated mutant viruses by transfecting these plasmid libraries into 293T cells. Since cells receive multiple plasmids, there may not be a link between viral genotype and phenotype at this stage. To establish this link and select for functional variants, we passaged the viruses twice at low multiplicity of infection (MOI) in SupT1 cells. We deep sequenced <i>env</i> before and after selection to quantify the enrichment or depletion of each mutation, and used these data to estimate the preference of each site for each amino acid. Each mutant library was paired with a control in which cells were transfected with a wildtype HIV proviral plasmid to generate initially wildtype viruses that were passaged in parallel with the mutant viruses. Deep sequencing of these wildtype controls enabled estimation of the rates of apparent mutations arising from deep sequencing and viral replication. <b>(B)</b> We performed the entire experiment in triplicate. Additionally, we passaged the replicate-3 transfection supernatant in duplicate (replicate 3b). We also performed the second passage of replicate 3b in duplicate (replicates 3b-1 and 3b-2).</p