Backbone Flexibility of CDR3 and Immune Recognition of Antigens

IMF YJMBI-64354; No. of pages: 17; 4C: 3, 6, 7, 8, 9, 10, 11

Article

Jaafar N. Haida1

0022-2836/$ - see front m

Please cite this article a(2014), http://dx.doi.org/

Backbone Flexibility of CDR3 and ImmuneRecognition of Antigens

r 1, 2, Wei Zhu1, Jacquelin
e Lypowy1, Brian G. Pierce3, 4,Amtul Bari , Kris Persaud1, Xenia Luna1, 1, Marshall Snavely1,Dale Ludwig1 and Zhiping Weng2, 3, 4
1 - ImClone Systems, a wholly subsidiary of Eli Lilly and Company, Alexandria Center for Life Sciences, 450 East 29th Street, NewYork, NY 10016, USA2 - Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA3 - Bioinformatics Program, Boston University, 44 Cummington Street, Boston, MA 02215, USA4 - Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street,Worcester, MA 01605, USA

Correspondence to Jaafar N. Haidar and Zhiping Weng: J. N. Haidar is to be contacted at: ImClone Systems, a whollysubsidiary of Eli Lilly and Company, Alexandria Center for Life Sciences, 450 East 29th Street, New York, NY 10016,USA, Z. Weng to be contacted: Program in Bioinformatics and Integrative Biology, University of Massachusetts MedicalSchool, 368 Pantation Street, Worcester, MA 01605. [email protected]; [email protected]://dx.doi.org/10.1016/j.jmb.2013.12.024Edited by A. Panchenko

Abstract

Conformational entropy is an important component of protein–protein interactions; however, there is noreliable method for computing this parameter. We have developed a statistical measure of residual backboneentropy in folded proteins by using the ϕ–ψ distributions of the 20 amino acids in common secondarystructures. The backbone entropy patterns of amino acids within helix, sheet or coil form clusters thatrecapitulate the branching and hydrogen bonding properties of the side chains in the secondary structure type.The same types of residues in coil and sheet have identical backbone entropies, while helix residues havemuch smaller conformational entropies. We estimated the backbone entropy change for immunoglobulincomplementarity-determining regions (CDRs) from the crystal structures of 34 low-affinity T-cell receptors and40 high-affinity Fabs as a result of the formation of protein complexes. Surprisingly, we discovered that thecomputed backbone entropy loss of only the CDR3, but not all CDRs, correlated significantly with the kineticand affinity constants of the 74 selected complexes. Consequently, we propose a simple algorithm tointroduce proline mutations that restrict the conformational flexibility of CDRs and enhance the kinetics andaffinity of immunoglobulin interactions. Combining the proline mutations with rationally designed mutants froma previous study led to 2400-fold increase in the affinity of the A6 T-cell receptor for Tax-HLAA2. However, thismutational scheme failed to induce significant binding changes in the already-high-affinity C225–Fab/huEGFR interface. Our results will serve as a roadmap to formulate more effective target functions to designimmune complexes with improved biological functions.

© 2013 Elsevier Ltd. All rights reserved.

Introduction

Proteins and protein complexes are the funda-mental components of cellular machinery. Despitedecades of research, however, the thermodynamicprinciples that govern protein folding and binding arenot completely understood. In particular, the loss ofside-chain and backbone conformational entropy

atter © 2013 Published by Elsevier Ltd.

s: Haidar Jaafar N., et al, Backbone Flexib10.1016/j.jmb.2013.12.024

comprises a major free-energy penalty for theseprocesses [1]. Nonetheless, many folding, dockingand design algorithms do not include this componentin their scoring functions due to the lack of anaccurate measure that can be rapidly computed[2–5]. Among the exceptions, three studies includedbackbone entropy for evaluating binding free ener-gies [1,6,7].

J. Mol. Biol. (2014) xx, xxx–xxx

ility of CDR3 and Immune Recognition of Antigens, J Mol Biol

mailto:[email protected]

mailto:[email protected]

http://dx.doi.org/Jaafar N.Haidar12Njaafar.haidar@imclone.comWeiZhu1JacquelineLypowy1Brian G.Pierce34AmtulBari1KrisPersaud1XeniaLuna11MarshallSnavely1DaleLudwig1ZhipingWeng234NZhiping.Weng@umassmed.edu1ImClone Systems, a wholly subsidiary of Eli Lilly and Company, Alexandria Center for Life Sciences, 450 East 29th Street, New York, NY 10016, USAImClone Systems, Eli Lilly and Company, Alexandria Center for Life Sciences450 East 29th StreetNew YorkNY10016USA2Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USADepartment of Biomedical Engineering, Boston University44 Cummington Street,BostonMA02215USA3Bioinformatics Program, Boston University, 44 Cummington Street, Boston, MA 02215, USABioinformatics Program, Boston University44 Cummington StreetBostonMA02215USA4Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA 01605, USAProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School368 Plantation StreetWorcesterMA01605USANJ. N. Haidar is to be contacted at: ImClone Systems, a wholly subsidiary of Eli Lilly and Company, Alexandria Center for Life Sciences, 450 East 29th Street, New York, NY 10016, USA, Z. Weng to be contacted: Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, 368 Pantation Street, Worcester, MA 01605. Present address: X. Luna, Kadmon Corporation, LLC, 450 East 29th Street, New York, NY 10016, USA.

http://dx.doi.org/

http://dx.doi.org/10.1016/j.jmb.2013.12.024

2 Backbone Flexibility of CDR3

Attempts to compute backbone entropy started inthe early 1950s [8,9]. Entropy S is defined as −R∑Pi ln(Pi), where R is the universal gas constant, Piis the probability that the system resides in the ithstate and the summation is performed over allpossible states of the system [10]. Thus, in order tocompute the backbone entropy of a residue, oneneeds to determine the number of conformations theresidue's backbone can adopt and the correspond-ing probabilities. Two dihedral angles, ϕ and ψ,jointly specify a backbone conformation, whichcorresponds to a point on the Ramachandranmap [11]. Three main approaches can rely on suchcalculations to assess backbone conformations fromunfolded proteins: (1) One could exhaustivelyenumerate all sterically allowed conformations [12]or dynamically sample these conformations [13] forshort peptide chains. (2) One may assume that thecollection of backbone conformations observed in alarge set of folded protein structures is representativeof all feasible conformations in the unfolded state [7].(3) One can utilize nuclear magnetic resonance(NMR) to monitor the motion of proteins [14].Compared with the progress made in obtaining

favorable backbone conformations of unfoldedproteins, less attention has been paid to the extentof backbone flexibility in folded proteins. There arewell-known examples of large conformational chang-es upon binding [15,16], indicating that amplebackbone flexibility exists in the folded state. Forexample, Frederick et al. employed NMR to measurethe conformational flexibility of calmodulin and itsrole in molecular recognition [17]. Interestingly, theconformational entropy change is linearly correlatedto the total change of the calmodulin binding entropy[17–19]. In 1999,Willcox et al. also relied on isothermaltitration calorimetry to conclude that T-cell receptor(TCR) complex formation is driven by favorableenthalpic interactions that offset an unfavorableentropic barrier induced by the flexibility of thecomplementarity-determining regions (CDRs) [20].Since then, multiple studies demonstrated that eventhough CDRs undergo conformational rearrange-ments during TCR complex formation, determiningthe impact of such structural changes on the thermo-dynamics of TCR binding is not trivial [21–29].Subsequently, Armstrong et al. examined the collec-tive thermodynamic data of more than 41 TCRinteractions and concluded that there is no enthalpicor entropic signature for TCR complex formation [30].Hence, accurate assessment of the backbone flexibil-ity of the CDRs might be particularly important tofurther understand the thermodynamics of immuno-globulin binding.With the rapid accumulation of structures in the

Protein Data Bank (PDB) [31], the backboneconformational space of folded proteins is becomingincreasingly more comprehensive. In this study, weinvestigate backbone conformations of 20 amino

Please cite this article as: Haidar Jaafar N., et al, Backbone Flexib(2014), http://dx.doi.org/10.1016/j.jmb.2013.12.024

acids in high-resolution PDB structures and computethe corresponding statistical entropies in commonsecondary structures (α-helix, β-strand or neither,referred to as coil). We utilized these entropies todefine and compute the backbone flexibility of theCDRs of immunoglobulin (Fabs and TCRs) struc-tures. Interestingly, only the backbone flexibility ofCDR3 significantly correlated with the bindingkinetics of 74 immune complexes we mined fromthe literature. Hence, our backbone flexibility mea-sure should prove useful when developing struc-ture-based target functions that compute the bindingfree energy of immunoglobulin interactions.Convoluting the impact of CDR structural changes

on the thermodynamics of TCR binding is not a trivialmatter [30] because protein flexibility might redistrib-ute or even increase during the formation of proteincomplexes [32–34]. In spite of this complexity, thereare multiple case studies that relate CDR rigidity tothe low affinity and the multi-specificity of TCRs [35]and antibodies [36–47]. Additionally, Lipovsek et al.demonstrated that constricting the flexibility of CDRswith interloop disulfide bonds enhanced the affinityof immunoglobulin interactions [48]. This collectiveexperimental and computational evidence wasconvincing to explore alternative structure-basedmethods to decrease the conformational flexibility inthe backbone of CDR loops to enhance the bindingaffinities of immunoglobulins. Our novel structure-based method systematically introduces prolinesinto the CDR loops of low-affinity (A6/Tax-HLAA2)and high-affinity (C225/huEGFR) immunoglobulininterfaces. A combination of three proline CDRmutations enhanced the binding kinetics and affinityof the A6 TCR to the pep-MHC by 26 times. Uponcombining these mutations with our previouslyengineered mutants (~100-fold affinity enhance-ment) [49], the stability of the A6/Tax-HLAA2interaction increased by approximately 2400-fold.This result is consistent with multiple case studiesregarding the loss of CDR structural plasticity whileaffinity maturing antibodies [36–47]. On the otherhand, none of the C225 CDR proline mutationssignificantly enhanced the affinity or kinetics of theC225/huEGFR interaction. This suggests that back-bone flexibility does not contribute significantly tohigh-sensitivity/high-specificity immunoglobulinsthat recognize protein targets in a lock-and-key-likemode [41,43,50].

Results

We partitioned all residues in a set of 18,555high-resolution PDB structures into α-helix, β-sheetand coil (see Materials and Methods) and construct-ed a Ramachandran map for each residue in eachstructure type separately. We divided these mapsinto 10° × 10° bins, converted residue counts into



Fig. 1. The computed backbone entropy for 20 amino acids in three structure types, plotted as a function of the probabilitycutoff Pc. In (a) α-helix, (b) β-sheet and (c) coil, one Ramachandran map was constructed for each amino acid type, and thecorresponding backbone entropy was computed. See Materials and Methods for the definition and rationale of Pc.

3Backbone Flexibility of CDR3

probabilities and computed the statistical backboneentropy from the probabilities above a cutoff Pc.

Backbone entropy clusters of 20 amino acid types

Figure 1 illustrates backbone entropies for individ-ual residues in helix, sheet or coil. In SupplementaryFig. S1, we pooled all amino acids to illustrate theimpact of secondary structures. We only used ϕ–ψbins with probabilities above cutoff Pc after renorma-lization of the probabilities (see Materials andMethods). In Fig. 1, we plot entropy as a functionof Pc with each amino acid represented by aseparate curve. This reveals interesting clusteringpatterns, with the curves within a cluster intersectingeach other at some value of Pc and curves betweenclusters disjoint. Helix and sheet have distinctly

Table 1. Backbone flexibility clusters of 20 amino acids inhelix, sheet and coil

Helix Sheet and coil

Clusters Entropy Clusters Entropy

Sheet Coil

1. Gly 3.913 1. Gly 5.299 5.2992. Asn 3.759 2. Asn Asp 5.21 5.2343. Asp His Ser Thr 3.514 3. His 5.090 5.1494. Cys Tyr 3.296 4. Arg Cys Glu

Gln Lys Ser4.932 4.978

5. Phe 3.214 5. Ala Met PheThr Trp Tyr

4.782 4.841

6. Arg Gln Glu LeuLys Met Pro Trp

2.910 6. Leu 4.666 4.722

7. Ile Val 2.676 7. Ile Val 4.259 4.3308. Ala 2.610 8. Pro 3.842 3.862

Backbone flexibility clusters within a structure were determinedbased on Fig. 1. The number next to a cluster is the averagebackbone flexibility of the amino acids included in that cluster, atPc of 6.1 × 10−4.


different clustering patterns, while surprisingly, sheetand coil are very similar. Our clustering results didnot change when we recomputed and re-plotted Sjkagainst Pc at bin sizes 6° × 6° and 14° × 14° for thehelix, sheet and coil secondary structures in Sup-plementary Fig. S2a–c. In light of the redundancy ofthe protein structures in the PDB, we obtained threenon-redundant lists of PDB structures from theASTRAL database [51], where proteins with greaterthan 70%, 40% or 20% sequence identities wereexcluded, respectively. Figure 1 was regeneratedwith these datasets and remained essentially un-changed except for increased noise due to de-creased data points (data not shown). Thus, wedecided to use the full set of PDB structures for allanalysis. Additionally, Supplementary Fig. S1 furtherillustrates that the backbones of amino acids havethe same amount of entropy in sheet and coil butmuch less entropy in helix.Figure 1 indicates that the Gly backbone has the

highest φ–ψ conformational entropy regardless ofthe secondary structure, which is expected. Similar-ly, Pro has the least entropic backbone in sheet andcoil. However, the Ala backbone is the least entropicin helix. The Ramachandran maps of Pro and Ala areshown in Supplementary Fig. S3. They resemble themap of all residues in right-handed α-helices inprevious studies [52]. Indeed, the ϕ–ψ distribution ofAla is mostly restricted to the primary peak while thatof Pro spreads into the secondary peak. Pc was setat the default value of 6.1 × 10−4 in SupplementaryFig. S3. This corresponds to a count threshold of 208for Ala and of 50 for Pro. Total counts aftereliminating bins below the thresholds are 328,035for Ala and 79,835 for Pro. Thus, our finding that Alahas the most restricted backbone in α-helices wasnot an artifact caused by the lack of data. This resultis definitely related to the well-established fact




regarding Ala being the best helix stabilizer amongall 20 amino acids [53–55].We clustered amino acids into eight groups for helix

and sheet/coil, respectively (Table 1). The standarddeviation of entropies within each group is around0.01. These groups recapitulate three known con-cepts in protein chemistry: (1) Cβ-branched residuestend to have less entropy than Cγ-branched residues(e.g., Ile versus Leu), which reflects the sterichindrance of early branching on backbone conforma-tion. (2) Charged and polar residues tend to havemore entropy than hydrophobic residues (e.g., Thrversus Val). The ability of the former in participating inhydrogen bonding and other enthalpic interactionsrenders some backbone conformations more favor-able than otherwise. (3) Smaller residues tend to havemore entropy than larger ones (e.g., Asp versus Glu).Bulkier side chainswould placemore constraint on thebackbone and thus decrease backbone entropy.Two residues appear to be outliers of the above-

mentioned general trends: Ala and His. As mentionedbefore, Ala has the smallest entropy in helix. It belongsto the fourth smallest entropy cluster in sheet and coil.This is surprising as Ala has only onemethyl group asits side chain, small and un-branched. His on the otherhand has an unexpectedly large entropy. It is Cγ

branched with a bulky five-membered ring and hasmore entropy than other Cγ-branched ring-containingresidues Phe, Trp and Tyr, in all structure types. Thismay be due to the fact that His has a smaller ring thanthe residues mentioned above and contains twonitrogen atoms, becoming positively charged atslightly acidic pH. Thus, its unique properties allow itto participate in hydrogen bonding and other enthalpicinteractions, leading to a greater variety of observedbackbone conformations.There are three notable differences between

residue entropies in helix and those in sheet/coil.First, as mentioned above, helix residues havesmaller entropy than sheet/coil residues. The differ-ence is so great that the most flexible residue inhelix, Gly, has almost the same amount of entropy asthe most rigid residue in sheet/coil, Pro. This reflectsthe strong steric constraint helix imposes on thebackbones of its component residues in order toform hydrogen bonds at a regular interval. Second,even though the rankings of the 20 residues withinhelix are similar to those within sheet or coil, onlyeight residues are in the same rank cluster (if werank the clusters in the order of high-to-low entropy),while eight residues jump between neighboringclusters and four residues jump to a further cluster.These four residues are Thr (third in helix and fifth insheet/coil), Arg (sixth in helix and fourth in sheet/coil), Pro (sixth in helix and eighth in sheet/coil) andAla (eighth in helix and fifth in sheet/coil). Theunusual behavior of Thr and Arg might reflect thegreater impact of side-chain size on backboneentropy of helix than that of sheet/coil. Third, the


20 helix residues spread evenly across the range ofentropy (Fig. 1a). In contrast, for sheet/coil, threeresidues (Ile, Val and Pro) have much lowerentropies than the other 17 residues, which spreadout more or less evenly. As Ile, Val and Pro areCβ-branched nonpolar residues, their different be-haviors indicate the greater impact of these twofeatures on restricting backbone φ–ψ conformationsin sheet/coil versus those in helix.

The impact of solvent accessibility onbackbone entropy

The results in Fig. 1 and Supplementary Fig. S1are the output of pooling together the φ–ψ confor-mational data from 18,555 high-quality proteincrystal structures (see Materials and Methods).Hence, our structural data collectively suggest thatthe backbones of sheet and coil in folded proteinshave similar backbone conformational entropy.However, it is counterintuitive to assume that thebackbone of any folded protein can explore allthe possible φ–ψ conformations allowable by theRamachandran maps derived from the collectivestructural data that lead to Fig. 1 (Table 1) andSupplementary Fig. S1. This might be the case in theunfolded state of the protein but not in its folded state.In folded proteins, intrinsic conformational fluctuationshave been shown experimentally and computationallyto correlate significantly with the relative solventaccessibility (Arel), which was defined as the amountof surface area a protein exposes to the solventcompared to what is expected given its molecularweight [56]. Additionally,Arel was also reported to be astrong predictor of the conformational changesinduced by protein binding [57].Because the intrinsic structural flexibility of pro-

teins is strongly influenced by solvent accessibility,we computed the percentage ASA (accessiblesurface area) with NACCESS [58] for each second-ary structure within 520 monomeric protein crystalstructures used as templates in eight rounds of theCASP (Critical Assessment of protein StructurePrediction; CASP 2–9) [59] (no NMR structures).After extracting the secondary structure informationfor this dataset from the S2C database [60], wecomputed in Fig. 2 the average percentage ASA [58](backbone and side chain) with NACCESS program[58] for each of the sheet and coil backbone clusters(Table 1). The percentage ASA (normalized for side-chain size) in Fig. 2 follows a similar pattern whencomparing the coil and sheet entropy clusters.However, it is apparent from Fig. 2 that sheetresidues are more buried (less exposed to thesolvent) than coil residues. Based on these resultsand the conclusions of the study by J. A. Walshregarding solvent accessibility and protein flexibility[56], we can deduce that the sheet residues arestructurally more constrained than coil residues



Cluste

r 1

Cluste

r 2

Cluste

r 3

Cluste

r 4

Cluste

r 5

Cluste

r 6

Cluste

r 7

Cluste

r 80

10

20

30

40

50

The average %ASA for the backbone flexibility clusters within the CASP 2-9 protein structures

%A

SA

SheetCoil

Fig. 2. Comparing the solvent accessibility of coil andsheet secondary structures extracted from the crystalstructures of 520 protein domains that are used astemplates for protein structure predictions in CASP rounds2–9. The secondary structure information was extractedfrom the S2C database [60]. NACCESS was utilized tocompute the percentage ASA for each residue within thesestructures. We reported the average percentage ASA andits standard error for the amino acids within eachsecondary structure backbone flexibility cluster (Table 1).


while sampling their allowable Ramachandran φ–ψconformations in folded proteins. Hence, it is logicalto define structural flexibility in the backbone offolded proteins as the backbone entropy (Table 1)normalized by its corresponding percentage ASA.Even though the collective analysis in Fig. 1 andSupplementary Fig. S1 shows that the coil and sheethave similar backbone entropies, this definition ofstructural flexibility will render the coil backbonemore flexible than that of the sheet backbonebecause a higher fraction of its entropy (Table 1)remains in the folded state of proteins.

The effects of backbone flexibility on immuno-globulin interactions

The binding sites of antibodies and TCRs aremostly coils and thus should contain high backboneentropy. A large portion of this is lost upon complexformation. Accordingly, a previous study indicatedthat antibodies and TCRs undergo conformationalchanges and significant entropic loss upon bindingto their ligands [20]. Thus, immunoglobulin inter-faces are ideal candidates for testing the effects ofbackbone flexibility on the kinetics of proteincomplex formation.To probe the effect of backbone entropy on the

formation of immunoglobulin/protein interfaces, wecollected binding affinity and kinetics data for 40antibody/antigen and 34 TCR/pep-MHC complexeswith solved X-ray structures (Supplementary TableS1). As expected, Fig. 3 shows that the antibody/antigen complexes have stronger affinities than theTCR/pep-MHC due to faster on-rates and slower


off-rates. To probe the impact of CDR backboneflexibility on the affinity of these complexes, wecomputed the loss of backbone entropy (−ΔS) of theCDRs in Supplementary Table S2 as the sum ofCDR entropies, proportional to the extents theirsolvent accessibility varied upon complex formation:

−X

Si � ΔASAi ð1Þ

where Si is the backbone entropy (coil or sheet inTable 1 because the Kabat definition of CDRsincludes residues in both secondary structures) of ithCDR position and ΔASAi is the change of thepercentage solvent accessibility of the ith CDRposition {computed using the NACCESS program[58]: ASA(bound) − ASA(unbound)}. We utilized thepercentage and not absolute ASA to eliminate biasesrelated to the size of the side chain and to stayconsistent with recent evidence that relative solventaccessibility is a strong predictor of conformationalflexibility of folded proteins [56] and of conformationalchanges induced by protein binding [57]. For simplic-ity, we approximated the unbound structure ofantibody or TCR using their bound structures (Sup-plementary Table S1) but without the binding partner.In Fig. 4, we compared the Fab and TCR scores

from Eq. (1) for all CDR pairs (i.e., CDR1 forantibodies corresponds to the sum over the heavyand light CDRs) with their corresponding total sum.Figure 4 indicates that the TCR CDRs (loweraffinities) are losing more unfavorable backboneflexibility than the Fab CDRs (higher affinities) uponbinding to their protein targets. Figure 4 alsoillustrates that the TCR CDR3s significantly(p-value = 1.1 × 10−6 by Wilcoxon rank sum test)lose more unfavorable flexibility than the Fab CDR3sloops and that no significant difference is observedwhen comparing TCR and Fab entropy (flexibility)loss of CDR1s and CDR2s. Hence, the flexibility lossof the CDR3 pair is a classifier that can distinguishbetween the high (Fab) and low (TCR) affinityinterfaces. Among each interface type (Fab orTCR), Fig. 4 also demonstrates that the CDR3slose more backbone flexibility than CDR1s orCDR2s. Hence, we concluded that immunoglobulinbinding to protein targets requires compensation foran entropic barrier specifically enforced by the CDR3loops, rather than by all six CDRs. In Fig. 4, wepooled together the CDR3 data from both chains(heavy, light, α, β and so on) because these loopsare usually responsible for antigen specificity and toincrease our sample size for statistical analysis. Weperformed the same analysis with CDR1–CDR2 forconsistency. If we separate the CDRs by theirchains, these trends do not change significantlyversus the pooled results in Fig. 4 (data not shown).To further expound on the statistical observation

that CDR3s contribute to most of the unfavorable



Comparing the association constants ofTCRs and mAbs

TCRm

Ab103

104

105

106

107

p-value = 1.3 x 10-5

k on (

1/M

s)

Comparing the dissociation constants ofTCRs and mAbs

TCRm

Ab10-6

10-5

10-4

10-3

10-2

10-1

100

p-value = 1.9 x 10-9

k off (

1/s)

Comparing the affinity constants ofTCRs and mAbs

TCRm

Ab10-12

10-11

10-10

10-9

10-8

10-7

10-6

10-5

10-4

p-value = 2.6 x 10-11

KD (

1/M

)

Fig. 3. The distributions of the SPR kinetic and affinitycoefficients kon, koff and KD = koff/kon for the 34 TCR and40 Fab complex structures in Supplementary Table 1. Thekinetic rates were reported in the literature by fitting thebinding response of the SPR (25 °C; Biacore 3000) to asimple Langmuir model.


backbone flexibility (entropy) loss in Fig. 4, wecompared in Fig. 5 our backbone flexibility measure[Eq. (1)] to the binding energetics derived from thesurface plasmon resonance (SPR) binding datasetin Supplementary Table S1. Change in binding freeenergy (ΔG) is dependent on the SPR bindingaffinity constants (KD) in Supplementary Table S1according to the following equation ΔG = RTlnKD(R is the gas constant; T = 298 K) and on the kinetic


coefficients (kon and koff in Supplementary Table S1)according to the following simple derivation:

ΔG ¼ RT � lnKD

¼ RT � ln koff=konð Þ¼ RT � ln koffð Þ þ −RT � ln konð Þ½ �

In Fig. 5, we correlated the computed backboneflexibility loss [Eq. (1)] of the CDR3s to ΔG and its kon[ΔGon = −RT × ln(kon)] and koff [ΔGoff = RT × ln(k-off)] components to understand the impact of CDR3flexibility on the affinity and kinetics of immunoglob-ulin complex formation and to further validate ourbackbone entropy measures in Table 1.The significant positive correlation (p-value =

0.0001) between the computed backbone flexibilityloss (−ΔS) of the CDR3s (third bin in Fig. 4) and theexperimentally determinedΔGon in Fig. 5 implies thatCDR3s with higher backbone flexibilities will slowdown the association phase of their correspondingimmunoglobulin/antigen interaction. Hence, CDR3swith higher backbone flexibility need to sample alarger φ–ψ conformational space before rearranginginto the optimal structure required to recognize aspecific antigen. This is consistent with what hasbeen previously reported in the literature regarding therelationship between the conformational entropy andthe association energy of protein–protein interactions[1,17,18,33,61]. The novelty here is that only thebackbone flexibility of the CDR3s influences theassociation of immunoglobulin interactions. In contrast,the significant positive correlation (p-value b 0.0001)with the experimentally determined ΔGoff implies thatCDR3s with higher backbone flexibility lead to fasterdissociation of the immunoglobulin complex. This arather unexpected observation that needs furtherinvestigation. Consequently, as the flexibility ofCDR3s increases in Fig. 5, the affinity of the Igfragments decreases (ΔG increases with a positivecorrelation; p-value b 0.0001). In spite of the statisticalsignificance of the patterns observed in Fig. 5, thereported correlation coefficients 0.5, 0.5 and 0.55 forΔGon, ΔGoff and ΔG, respectively, are low because weare only relating the backbone entropy to the total freeenergy of binding, thus ignoring other energetic termsthat influence these interactions. When correlating thecomputedΔS of all six CDRs rather than that of CDR3sonly, the observed correlations become statisticallyinsignificant.Because the relative solvent accessible is a strong

predictor of the intrinsic conformational flexibility offolded proteins [56] and of conformational changesinduced by protein binding [57], we sought to verifythat the relationship between backbone flexibilityand the binding energetics in Fig. 5 is not dominatedby the percentage ASA (computed with NACCESS[58]) component of Eq. (1). Hence, we correlated theaveraged relative ASA change (bound − unbound)



Comparing CDR backbone flexibity: TCR vs Fab

TCR

CD

R1

Fab

CD

R1

TCR

CD

R2

Fab

CD

R2

TCR

CD

R3

Fab

CD

R3

TCR

CD

R S

umFa

b C

DR

Sum

0

1000

2000

3000

4000

p = 1.1E-06

p = 4.2E-04

p = 0.1p = 0.7

Com

pute

d-ΔS

Fig. 4. Comparing the binding entropy loss (computedΔS) distributions for CDRs of the 34 low-affinity TCRcomplex structures (green) and the 40 high-affinity Fabcomplex structures (red) of Supplementary Tables S1 andS2. The binding entropy loss was computed according tothe following equation: −∑Si × ΔASAi where Si (Table 1) isthe backbone flexibility of CDR residue i and ΔASAi =ASAi(bound) − ASAi(unbound) is the change in solventaccessibility of CDR residue i due to complex formation.ASA was computed via NACCESS. The distributions werecompared between TCR and Fab complex structures forCDR1s, CDR2s, CDR3s and all CDRs. The statisticalsignificance of these comparisons was reported as thep-value of the one-tailed Wilcoxon ranked sum test.


for the CDR3s of the 74 immunoglobulin complexes(Supplementary Table S1) with their correspondingSPR energetics. The correlation of the averagedASA change of CDR3 with −ΔGon is 0.36 (p-value =0.0023), with ΔGoff is 0.42 (p-value = 0.0005) andwith ΔG is 0.43 (p-value = 0.0002 in SupplementaryFig. S4). These results highlight the influence of theaveraged relative ASA on the conformational flexi-bility of CDR3s and consequently on the kinetics ofimmune recognition of antigens, yet these correla-tions—especially for −ΔGon—are statistically lesssignificant than their counterparts in Fig. 5. Addition-ally, we correlated the sum of the CDR3s backboneentropy loss that corresponds to setting the percent-age ASA in Eq. (1) to unity, with the bindingenergetics of the 74 complexes in SupplementaryTable S1. The correlations of the uncorrected CDR3backbone entropy loss with −ΔGon, ΔGoff and ΔGare 0.36 (p-value = 0.002), 0.36 (p-value = 0.002)and 0.43 (p-value = 0.0002 in SupplementaryFig. S4), respectively. This correlation analysisdemonstrates that the individual terms relative ASAand backbone entropy strongly impact the bindingenergetics of the 72 immunoglobulin complexes inSupplementary Table S1. However, the productof these terms in Fig. 5 is statistically more significantin capturing the kinetics and affinities of the 72immunoglobulins than the individual terms of Eq. (1).No significant correlations can be reported when thisanalysis is repeated for CDR1 and CDR2.


Improving the affinity of A6/Tax-HLAA2 complexby systematically introducing prolines into theCDRs of the A6 TCR

Consistent with our results in Fig. 5, the literaturecontains mounting evidence that the CDRs ofaffinity-matured antibodies are structurally rigid(Mohan et al. [41]; Thorpe et al. [43]) and thathigh-affinity antibodies with known structures recog-nize antigens in a key-and-lock-like mode (Haidar etal. [50]). It has also been suggested that incorporatingintraloop (Wu et al. [62]) and interloop (Lipovsek et al.[48]) disulfide bonds would constrict the flexibility ofCDRs and thus help engineer and select high-affinityantibodies. However, introducing novel cysteines intopeptide sequences can impede the folding of theprotein structure due to the possible formation ofnon-native disulfide bonds. Proline scanning hasbeen proposed as an alternative to further enhancethe thermal stability of proteins by lowering the entropyof the reference (unfolded) state in order to avoid suchcomplications [63–68]. Furthermore, Holler et al.isolated by yeast display 2C TCR clones withproline-rich sequence at the tip of their CDR3αloop that enhanced the affinity of 2C to the pep-MHCtarget—dEV8/Kb—by more than 100-fold [69]. More-over, phage display has isolated higher-affinity clonesthat contained proline substitutions for the Mel5 [70]and 1G4 [71]. These observations, in addition to ourresults fromTable 1 andFigs. 4 and 5, led us to pursuemutational schemes focused on structurally constrict-ing the CDRs of immunoglobulins to further delineatethe impact of CDR flexibility (conformational entropy)on the stability of Ig/protein complexes. We focusedon the A6/Tax-HLAA2 (PDB ID: 1AO7 [72]) complexbecause of our previous experience with modulatingthe low affinity of this interface [49,73].Theoretically, amino acid mutations from upper

backbone flexibility clusters to lower clusters inTable 1 would increase the rigidity of CDRs andresult in decreasing the unfavorable entropy lossduring protein–protein recognition. Specifically, pro-line mutations will minimize entropy loss at eachCDR (coil) position because the proline backbone isthe most rigid in the coil/sheet category of Table 1due to its constricting cyclic side chain. We decidedto utilize structural information to efficiently predictthe A6 CDR positions that will favor and accommo-date proline substitutions by utilizing the graphicaltool in the Ramachandran Plot Server‡ to computeand project the dihedral angles of the A6 CDRstructures onto high-quality proline and pre-prolineRamachandran maps generated from all the PDB[74]. We ignored the helix content in the proline andpre-proline φ–ψ maps of the Ramachandran PlotServer because prolines are rare within the helixsecondary structure. This server can identify sites ina CDR structure that prefer proline and pre-prolinebackbone conformation.



Fig. 5. Correlation between the weighted backboneentropy change and the ΔGon [−RT × ln(kon)], ΔGoff[RT × ln(koff)] and ΔG (RT × lnKD) of the 34 TCRcomplexes and 40 Fab complexes in SupplementaryTable S1. The TCR/pep-MHC and the Fab/antigencomplexes are labeled in the scatter plots with greencircles and red squares, respectively.


In addition to the graphical representation of data,the Ramachandran Plot Server can quantify theproline or pre-proline backbone preference at anyposition of the CDR as being “Preferred”, “Accessi-ble” or “Un-preferred”. The coordinates of the boundA6 CDRs (1AO7) were uploaded to the Ramachan-dran Plot Server to compute the bound CDR dihedral


angles and project them on the proline andpre-proline Ramachandran plots. A CDR position iwas considered for mutation into proline if and only ifthe following backbone preferences were met:

(1) The φ–ψ angular coordinates of CDRposition i are in a “Preferred” proline back-bone conformation.

(2) The φ–ψ angular coordinates of CDRposition i − 1 (preceding CDR position) arein a “Preferred” or “Accessible” pre-prolinebackbone conformation.

Only eight αCDR (four in αCDR1, one in αCDR2and three in αCDR3) and five βCDR (one in βCDR1,two in βCDR2 and two in βCDR3) residues of the A6TCR satisfied the conditions mentioned above. Afterengineering the 13 proline mutations in Table 2according to the protocols in Materials and Methods,we utilized SPR (Biacore 3000) to determine theirkinetic and affinity constants to Tax-HLAA2.Mutation βR102P (βCDR3) had a 26.5-fold im-

provement (KD_wt/KD_mut N 1 in Table 2) in its affinityto the Tax-HLAA2 complex. The kon of this mutant(15.7 × 104 1/Ms) was three times faster (favorable)than the association rate of the wild-type A6(5.1 × 104 1/Ms) and its koff (0.13 × 10−1 1/s) wasapproximately nine times slower (favorable) than thedissociation rate of the wild-type A6 (1.1 × 10−1 1/s).Additionally, mutants αK103P (αCDR3) and βA52P(βCDR2) had 1.6- and 1.7-fold improvements, re-spectively, in binding affinity to Tax-HLAA2. The kon ofmutant αK103P was three times faster than theassociation rate of the wild-type A6/Tax-HLAA2interaction, yet the kon of βA52P was slightly slower(unfavorable) in Table 2. However, the koff of αK103Pwas approximately two times faster (unfavorable) andthat of βA52P was two times slower. MutationsβR102A and αK103A (the wild type at β52 is alreadyalanine) completely lost binding to pep-MHC (viaSPR); thus, the proline substitution at these positionsand not the shaving of the charged wild-type sidechains is the cause behind the affinity improvement.Figure 6 contains the crystal structure of the wild-typeA6/Tax-HLAA2 complex (PDB ID: 1AO7) with thebackbones ofCDRpositions βA52,βR102 andαK103colored red. Among these three mutants, only residueβR102 directly contacts Tax-HLAA2 (Fig. 6).Mutations αR27P (αCDR1) and βG101P (βCDR3)

also retained binding to Tax-HLAA2, but the affinitiesof these interactions were half (KD_wt/KD_mut ~ 0.5in Table 2) of that of the wild-type A6. Both theassociation (kon) and dissociation (koff) constants forthese mutants were unfavorably impacted. MutationαG102P (αCDR3) bound to Tax-HLAA2 with verylow affinity (KD N 200 μM) that was not possible toaccurately evaluate with Biacore 3000 (Langmuirbinding). In Fig. 6, CDR positions βG101, αR27 and



Table 2. The kinetics of Tax-HLAA2 binding to the immobilized A6 TCR wild type and single-point proline mutants

1AO7 CDR kon (104 1/Ms) koff (10−1 1/s) KD = koff/kon (μM) KD_wt/KD_mut

WT 5.1 1.1 2.2 1D26P α1 NB NB NB NBR27P α1 4.4 ± 0.2 1.6 ± 0.1 3.7 ± 0.2 0.6G28P α1 NB NB NB NBS29P α1 NB NB NB NBN52P α2 NB NB NB NBG102P α3 ND ND N200 b0.01K103P α3 14.0 ± 6.0 1.91 ± 0.01 1.4 ± 0.2 1.6L104P α3 NB NB NB NBH29P β1 NB NB NB NBV50P β2 NB NB NB NBA52P β2 4.4 ± 0.2 0.56 ± 0.03 1.27 ± 0.03 1.73G101P β3 3.9 ± 0.7 1.8 ± 0.1 4.7 ± 1.2 0.5R102P β3 15.7 ± 1.0 0.13 ± 0.02 0.083 ± 0.002 26.5

The binding parameters kon, koff and KD = koff/kon were calculated by fitting the binding sensograms (Biacore 3000) of these interactions tothe Langmuir model of the BIAevaluation analysis package. The ratio KD_wt/KD_mut compares the affinity of Tax-HLAA2 to the prolinemutants with respect to its affinity to the wild-type A6 TCR. If the ratio KD_wt/KD_mut is greater than 1, then the corresponding proline mutantbinds to Tax-HLAA2 with a higher affinity than that to wild-type A6. Mutants labeled with NB (no binding) lost binding to Tax-HLAA2 (flatSPR sensogram).

Fig. 6. The A6 CDR positions that allow prolinesubstitutions are mapped on the structure of A6/Tax-H-LAA2 complex (PDB identifier 1AO7). The α1/α2 domain ofHLAA2 is in white, the Tax peptide is in green and the A6TCR is in ribbon (α-chain and β-chain are in light and darkgrays, respectively). The positions of the CDR prolinemutations that caused loss of binding to Tax-HLAA2(labeled with NB in Table 2) are highlighted in blue. TheCDR positions that are highlighted in “yellow” correspondto the proline mutations in that bound to Tax-HLAA2 with anaffinity similar to that of the wild-type A6 (KD_wt/KD_mut ≤ 1 inTable 2). The CDR positions that are highlighted in redcorrespond to the proline mutations αK103P, βA52Pand βR102P that significantly enhanced the affinity ofthe A6/Tax-HLAA2 interaction (KD_wt/KD_mut N 1 inTable 2).



αG102 were highlighted in yellow. The remainingseven proline mutations were labeled with nobinding (NB) in Table 2 because their affinity to theTax-HLAA2 complex was undetectable with Biacore3000 and their positions were colored blue in Fig. 6.

Combining the A6 proline mutations with otherdesigned mutations leads to 2400-fold improve-ment in affinity for Tax-HLAA2

Proline CDR point mutations that improved bindingto pep-MHC were then combined into the triplemutant αK103P/βA52P/βR102P (KAR) to determinewhether it was possible to further improve the affinitybeyond those of the single-point mutants. Toengineer the KAR triple mutant, we combined thedouble mutant βA52P/βR102P via standardPCR-based mutagenesis and then expressed itsinclusion in Escherichia coli. The triple mutant KARwas refolded and purified from the β-chain inclusionbodies of the double mutant βA52P/βR102P incombination with the α-chain inclusion bodies ofthe mutant αK103P. Surprisingly, the KAR prolinetriple mutant only had a 15.7-fold improvement(Table 3) in its affinity to the pep-MHC, less thanthe 26.5-fold increase shown for the single mutantβR102P in Table 2. The kon of the KAR mutant(4.3 ± 0.8 × 104 1/Ms) was slightly slower than theassociation rate of the wild-type A6 (5.1 × 104 1/Ms),yet its koff (0.06 ± 0.01 × 10−1 1/s) was 18 timesslower than the dissociation rate of the wild-type A6(1.1 × 10−1 1/s). Hence, the affinity enhancement ofthe KAR mutant to the pep-MHC was exclusivelycaused by the protracted dissociation phase of theinteraction.



Table 3. The kinetics of Tax-HLAA2 binding to the immobilized A6 TCR wild-type and A6 mutants that containcombinations of proline single-point mutations from Table 2 in addition to previous mutations that we have rationallydesigned

1AO7 kon (104 1/Ms) koff (10−1 1/s) KD = koff/kon (μM) KD_wt/KD_mut

WT¶ 5.1 1.1 2.2 1αK103P/βA52P/βR102P (KAR) 4.3 ± 0.8 0.06 ± 0.01 0.14 ± 0.05 15.7αK103P/βR102P 52.7 ± 10.5 1.50 ± 0.36 0.28 ± 0.05 7.9WFGMT 6.32 0.0135 0.0214 ~100WFGMT/βR102Pa 4.7 0.0035 0.0074 297WFGMT/KARa 3.4 0.0003 0.0009 2444

The binding parameters kon, koff and KD = koff/kon were calculated by fitting the binding sensograms (Biacore 3000) of these interactions tothe Langmuir model of the BIAevaluation analysis package. The ratio KD_wt/KD_mut compares the affinity of Tax-HLAA2 to the prolinemutants with respect to its affinity to the wild-type A6 TCR. If the ratio KD_wt/KD_mut is greater than 1, then the corresponding mutant bindsto Tax-HLAA2 with a higher affinity than that to wild-type A6.

a Statistics are not available because we were not able to regenerate the surface.


To capture the cooperative nature of the combinedtriple proline mutant (KAR), we compared itsmeasured kinetics (Table 3) to the sum of thekinetics of the individual point mutants in Table 2.Specifically, we employed the Moza et al. [75]definition of cooperativity as the difference betweenthe measured ΔΔG for the triple mutant and the sumof the ΔΔG values for the component singlemutations. According to Moza et al., the impact ofa mutational combination on binding to pep-MHC isconsidered cooperative if this difference is less than−0.5 kcal/mol, anti-cooperative if greater than0.5 kcal/mol and additive otherwise [75]. We com-puted ΔΔGon [RT × ln(kon_wt/kon_mut)] and ΔΔGoff[RT × ln(koff_mut/koff_wt)] for the KAR mutant and thecomponent single mutants from the SPR determinedkinetics in Tables 2 and 3. The impact of the triple

Fig. 7. The raw and unfitted SPR sensograms (Biacore)of 9 μM Tax/HLAA2 flown over 400 response units ofimmobilized wild-type A6 TCR (black broken line) or A6mutants KAR (black continuous line), WFGMT/βR102P(blue continuous line) and WFGMT/KAR (red continuousline). This data were fitted into 1:1 binding model as part ofthe reported analysis in Table 3.


mutation on the association rate was unexpectedlyanti-cooperative (+1.27 kcal/mol) since the konvalues of both single mutants βR102P and αK103Pwere enhanced relative to that of the wild-type A6(Table 2). However, the impact of KAR mutationalcombination on the dissociation rate was additive(cooperativity = −0.38 kcal/mol).In Table 2, mutations βR102P and αK103P

enhanced the kon of binding to pep-MHC and thedouble mutant αK103P/βR102P had a 7.9-foldimprovement (Table 3) in its affinity to pep-MHC.The kon of the αK103P/βR102P mutant (52.7 ±10.5 × 104 1/Ms) was 10 times faster than that ofthe wild-type A6 (5.1 × 104 1/Ms), while its koff(1.50 ± 0.36 × 10−1 1/s) was approximately identi-cal with that of the wild-type A6 (1.1 × 10−1 1/s). Theimpact of this double mutation on the association(cooperativity = −0.12 kcal/mol) and dissociation(cooperativity = −0.24 kcal/mol) rates was additive.This is consistent with our previous observationsregarding the additivity of double mutants belongingto the different TCR chains [49,73]. The favorable konadditivity of the double mutant αK103P/βR102Pdemonstrates that the mutation βA52P has anunfavorable impact on the kon of the KAR mutant.However, the favorable koff additivities of bothαK103P/βR102P and KAR mutants illustrate thatmutation βA52P maintained its positive impact onthe dissociation phase of the interaction withpep-MHC when combined with the two other prolinemutants.We have previously combined single-point muta-

tions that were computationally designed at posi-tions 26, 27, 28, 51 and 100 of the A6 TCRα chain[49] to engineer the WFGMT (position 28 is not inboldface because it was back-mutated to the wild-type substitution glycine [49]) mutant that bound tothe pep-MHC with an affinity 100-fold (KD_wt/KD_mutratio in Table 3) better than that of the wild-type A6(kon = 6.32 × 104 1/Ms; koff = 0.0135 × 10−1 1/s)[49]. Combining the WFGMT (100-fold) and βR102P(26-fold) mutations only resulted in a 297-fold



Table 4. The kinetics of C225 wild-type Fab and the C225 proline mutants binding to the immobilized huEGFR

1YY9 CDR kon (106 1/Ms) koff (10−3 1/s) KD = koff/kon (nM) KD_wt/KD_mut

WT 1.46 ± 0.23 1.91 ± 0.47 1.31 ± 0.18 1L29P H1 2.27 ± 0.61 3.14 ± 0.93 1.38 ± 0.26 0.95G33P H1 No expressionW52P H2 1.09 ± 0.41 17.30 ± 6.29 15.9 ± 2.91 0.08S53P H2 0.89 ± 0.12 4.65 ± 0.22 5.21 ± 0.74 0.25G54P H2 2.20 ± 0.05 4.53 ± 0.71 2.06 ± 0.13 0.64A98P H3 0.85 ± 0.09 6.38 ± 0.19 7.55 ± 0.46 0.17L99P H3 No expressionY101P H3 1.46 ± 0.71 31.10 ± 5.42 21.3 ± 3.8 0.06F106P H3 No expressionA25P L1 0.67 ± 0.09 1.0 ± 0.1 1.48 ± 0.06 0.89S26P L1 2.14 ± 0.31 2.75 ± 0.29 1.29 ± 0.34 1S28P L1 2.36 ± 0.62 2.13 ± 0.76 0.90 ± 0.08 1.5S54P L2 2.08 ± 0.85 2.63 ± 0.37 1.27 ± 0.17 1W94P L3 No expressionT96P L3 0.024 ± 0.015 26.60 ± 11.73 1130 ± 462 0.0012

The binding parameters kon, koff and KD = koff/kon were calculated by fitting the binding sensograms (Biacore 2000) of these interactions tothe Langmuir model of the BIAevaluation analysis package. The ratio KD_wt/KD_mut compares the affinity of huEGFR to the proline mutantswith respect to its affinity to the wild-type C225 Fab. If the ratio KD_wt/KD_mut is greater than 1, then the corresponding mutant has a higheraffinity to huEGFR than that to wild-type C225 Fab. Mutants that did not transiently express in HEK 293 were labeled with “no expression”.

Fig. 8. The C225 CDR positions that allow prolinesubstitutions are mapped on the structure of C225–Fab/huEGFR complex (PDB identifier 1YY9). Domain III ofhuEGFR is in white and the variable region of C225 Fab isin ribbon (heavy and light chains are in light and darkgrays, respectively). The CDR positions highlighted in bluecorrespond to the proline mutations whose mutants did notexpress in HEK 293 cells according to Table 4. Thepositions of the CDR proline mutations that caused loss ofbinding to huEGFR (KD_wt/KD_mut ≤ 0.5 in Table 4) arehighlighted in yellow. The CDR positions (only backbones)that are highlighted in green correspond to the prolinemutations in Table 4 that bound to huEGFR with an affinitysimilar to that of the wild-type C225 (0.5 ≤ KD_wt/KD_mut ≤ 1 in Table 4). The light chain CDR position S29is highlighted because S29P is the only mutation in Table 4that slightly enhanced the affinity of the C225/huEGFRinteraction (KD_wt/KD_mut = 1.5).



improvement (Table 3) in the TCR/pep-MHC affinityover wild type. However, combining the WFGMT(100-fold) with the KAR (15.7-fold) mutant leads to2444-fold enhancement (Table 3) in the TCR/pep-MHC affinity. To our knowledge, such affinityimprovement based on rational design has notbeen previously reported in the literature forimmunoglobulins.It is clear from the SPR sensograms in Fig. 7 that

the enhanced affinity for both combinationsWFGMT/βR102P and WFGMT/KAR is due to theirslow dissociation rates as evaluated by the Langmuirmodel in Table 3. While the association rate ofβR102P significantly improved in Table 2, theassociation rates of both WFGMT/βR102P andWFGMT/KAR did not vary from that of the wild-typeA6 in Table 3. For the high-affinity mutants in Fig. 7,we have extended the SPR dissociation time to1000 s. It is also important to note that we were notable to run experimental replicates for WFGMT/βR102P and WFGMT/KAR because our regenera-tion buffer failed to restore the TCR baseline in thesample flow cells. However, these data are repro-ducible when repeated in a separate experiment.

Proline substitution in the CDRs of C225 Fabdoes not impact its affinity to huEGFR

Constricting the backbone of CDRs with prolinemutations enhanced the stability of the low-affinity(KD = 2.2 μM) A6/TAX-HLAA2 interface. Hence, wetested this mutational scheme on the high-affinityC225/huEGFR interface (KD = 1.31 nM in Table 4).We uploaded the Euclidean coordinates of the C225Fab boundCDRs (1YY9) [76] onto theRamachandran



Table 5. Comparing the amino acid composition of theCDR3s of affinity-matured (PDB) and germline(KabatMan) antibodies

Cluster Amino acid Heavy CDR3 Light CDR3

KabatMan PDB KabatMan PDB

1 Gly 11.2 11.5 4.8 4.32 Asn 2.5 2.8 5.0 5.3

Asp 10.3 13.2 4.0 1.73 His 1.4 2.0 3.9 6.24 Arg 10.6 10.9 2.6 3.2

Cys 0.7 0.2 0.3 0.1Gln 0.9 0.6 15.1 18.3Glu 1.8 2.7 1.2 2.1Lys 1.6 0.6 0.7 1.1Ser 7.5 5.2 15.9 11.1

5 Ala 11.9 12.3 3.4 2.0Met 2.1 2.6 0.7 0.3Phe 5.4 6.6 2.8 4.3Thr 3.8 4.3 5.2 3.6Trp 2.7 2.2 5.5 5.8Tyr 14.3 12.4 9.5 10.1

6 Leu 3.4 2.9 6.6 5.87 Ile 1.7 1.5 1.4 1.1

Val 3.5 2.4 2.6 1.58 Pro 2.8 3.1 8.6 12.2

The cluster number is consistent with the sheet/coil backboneentropy cluster each amino acid belongs to in Table 1. Percent-ages in boldface indicate that the absolute difference between theaffinity-matured and germline CDRs is ≥0.9%.


Plot Server to identify the CDR positions whose φ–ψangular coordinateswould allow for proline substitutionaccording to the graphical schemewe discussed in theA6/TAX-HLAA2 case study. The bound C225 Fab hasnine positions of its heavy chain CDRs (two in CDR1,three in CDR2 and four in CDR3 of the heavy chain)and six positions of its light chain CDRs (three inCDR1, one in CDR2 and two in CDR3 of the lightchain) that satisfy the conditions needed for substitu-tion with proline. After engineering the 15 prolinemutations in Table 4 according to the protocols inMaterials and Methods, we utilized SPR (Biacore2000) to determine their kinetic andaffinity constants tohuEGFR.Surprisingly, heavy chain mutations G33P, L99P

and F106P and light chainmutationW94P (numberingis consistent with the 1YY9 structure) did not expresstransiently in HEK 293 cells. These CDR positions arecolored blue in Fig. 8. Of all the proline mutations inTable 4, only the light chain mutation A25P had aslower dissociation rate (koff = 1 × 10−3 1/s) than thatof the wild-type C225 (koff = 1.91 × 10−3 1/s). Addi-tionally, heavy chain mutations L29P and G54P andlight chainmutationsS26P,S28PandS54Phad fasterkon values than those of the wild-type C225 in Table 4.Only S28P of the light chain CDR1 (colored red inFig. 8) had a 1.5-fold improvement in its affinity to theTax-HLAA2 complex. Light chain mutants A25P,S26P and S54P (backbone is colored green inFig. 8) retained similar affinity to that of the wild type


(KD_wt/KD_mut ~ 1). The rest ofmutants (colored yellowin Fig. 8) had reduced affinity to pep-MHC in Table 4(KD_wt/KD_mut b 1).

Discussion

In this study, we took advantage of the rapidgrowth of the PDB and estimated the backboneentropies of the 20 amino acids in commonsecondary structures. The relative rankings ofamino acids in each secondary structure typeagree well with previous knowledge on proteinbiochemistry. We found helix residues to havelowest backbone entropy. One surprising finding isthat sheet and coil residues have nearly identicalbackbone entropies (Fig. 1, Table 1 and Supple-mentary Fig. S1), although the current paradigmstates that secondary structures are less flexiblethan coils in folded protein structures. We do notpropose that a sheet residue in a particular foldedprotein could explore all possible backbone confor-mations allowable by the Ramachandran mapderived from this type of residue in sheets of allPDB structures. Rather, this might be the case in theunfolded state of the protein. We suppose that afraction of this entropy, proportional to the percent-age ASA of a residue, remains in the foldedsecondary structure or coil. To support this argu-ment, we computed in Fig. 2 the percentage ASA ofthe backbone entropy clusters in Table 1 by utilizingthe structural data of 520 CASP protein templates[59]. It is apparent that coil residues are moreaccessible to the solvent than sheet residues.Consequently, the coil backbone can retain higherfraction of its φ–ψ conformational entropy—propor-tional to the relative solvent accessibility [56]—thanthat of the sheet backbone in the final state of thefolded protein. Hence, the flexibility of a foldedprotein backbone depends on two major variables:solvent accessibility and backbone entropy. Theseobservations are captured in Eq. (1) and werevalidated in Fig. 5.The utility of our backbone flexibility measure is

illustrated in Fig. 4 as it was able to distinguishlow-affinity immunoglobulin complexes from high-affinity immunoglobulin complexes. Additionally, thesignificant but marginal correlations between thebackbone flexibility loss and the affinity of immuno-globulin binding in Fig. 5 demonstrate the power ofour measures because backbone entropy is onlyone component among many that usually contributeto the total binding free energy of protein complexes.In addition to utility, those measures revealedinteresting observations about immunoglobulin in-teractions. For example, this is the first comprehen-sive study—not a case study—to report that onlythe flexibility of the CDR3, rather than the flexibilityof all six CDRs, impacts the kinetics and stability of




immunoglobulin/protein interfaces (Fig. 4). Addition-ally, Figs. 4 and 5 show that immunoglobulininteractions that lose more CDR3 backbone flexibil-ity will have slower association rates, faster disso-ciation rates and lower affinity. Losing more CDR3sbackbone conformational flexibility due to complexformation would require the backbone of the CDR3loops to sample a larger φ–ψ conformational spacewhile rearranging into the optimal binding conforma-tion. Hence, losing more conformational flexibilityupon complex formation will result with slowerassociation rate for the interaction, yet its impacton the dissociation phase of the interaction needsfurther investigation and explanation.Upon comparing high-affinity (Fabs) to low-affinity

(TCR) CDRs in Figs. 4 and 5, we observed thatCDR3s lose the most backbone conformationalentropy among all immunoglobulin CDRs. This isconsistent with a recent observation by Pierce andWeng regarding CDR3s that undergo the largestconformational changes [77]. Pierce and Wengreached this conclusion by comparing the boundand unbound crystal structures of 20 TCR molecules.To explain this difference between the high-affinityand low-affinity immunoglobulins, we compared inTable 5 the amino acid content of the CDR3s of the 40affinity-matured antibodies (Supplementary Table S1)to that of the germline antibody sequences within theKabatMan database [78]. Upon close examination ofthe abundance calculations in Table 5, we canobserve subtle fluctuations in amino acid propensitybetween the two datasets. If we ignore biasesattributed to X-ray crystallography, we can say thataffinity maturation slightly enriches the abundance ofPro andGln in theCDR3of the light chain in addition toAsp in the CDR3 of the heavy chain. In contrast, Serand Asp get slightly depleted from the CDR3 of thelight chain. These subtle differences get even furthermasked upon pooling the amino acids based on theirbackbone flexibility cluster. Hence, we can concludethat the sequence content of high- and low-affinityCDR3s are being sampled from similar amino aciddistributions and consequently of similar amino acidbackbone cluster (Table 1) distributions. However,Eq. (1) and the observations deduced fromFigs. 4 and5 demonstrate that the flexibility of CDR3s and itsrelationship to the kinetics of immunoglobulin recog-nition are strongly impacted by two features ofCDR3s:primary sequence and structural constraints that arecaptured by the percentage solvent accessibilitycorrection in Eq. (1). We have focused here on therelationship between CDR3 flexibility and immuno-globulin sensitivity but not immunoglobulin specificity,even though multiple case studies relate the multi-specificity of antibodies to the structural plasticity oftheir CDRs [36–47]. The backbone flexibility mea-sures we reported in Table 1 and Eq. (1) may helpexplain the structural mechanism by which high-affinity two-in-one antibodies recognize multiple anti-


gens [79–81] and of low-affinity TCRs such as 42 F3whose CDR3α undergoes considerable conforma-tional change to recognize two unrelated peptides(QL9 and a fragment of p3A1) presented by H2-Ld

[35].Traditionally, restricting CDR flexibility to enhance

binding affinity of antibodies has been achievedthrough forming disulfide bridges by systematicinsertion of cysteine doublets into the CDRs [48].Alternatively, we proposed in this study a simplealgorithm to achieve the same outcome by insertingsingle-point proline mutations into the CDRs ofimmunoglobulins. Interestingly, this simple mutation-al scheme yielded three single-point mutations(Table 2) that enhanced the low affinity of the A6/Tax-HLAA2 interaction. In contrast, no prolinemutations were able to significantly enhance thealready high affinity of the C225 Fab to its antigen,huEGFR (Table 3). The latter observation is inagreement with published reports that high-affinityFabs—like C225—recognize their antigens in akey-and-lock-like manner [41,43,50] and thus cannotbe further rigidified by proline substitutions in theirCDRs. Consistent with Fig. 3, only single-pointmutations of the CDR3s (αK103P and βR102P)significantly improved the kon (also koff) of the A6/Tax-HLAA2 interaction in Table 2. However, Zaffi—our previously optimized target function—failed topredict these mutations because it did not include aparameter to capture backbone stability [49].In the future, protein design algorithms could take

advantage of our findings to redesign proteininterfaces to enhance the affinity and kinetics ofprotein–protein interactions [49,82]. Alternatively,our approach can be applied to residues that donot directly contact the binding partner (αK103P andβA52P in Fig. 6) yet are close enough to affect therigidity of the binding site backbone. In the exampleof antibodies and TCRs, these would be positions atthe stems of the CDR loops. However, relying solelyon strategies that restrict CDR flexibility to enhancethe affinity of immunoglobulin will be limiting becauseour Pro scanning method had low success rates inTable 2 (3/13) and Table 4 (1/15) in achieving thisgoal. Hence, our backbone measure should befurther incorporated into target function along withother energy components to improve protein–proteinand protein–ligand docking algorithms. These en-tropic measures can be implemented to predictflexible regions on protein surfaces, and it can beapplied to improve the performance of algorithmsthat predict protein binding sites or patches thatimpact protein solubility [83]. Additionally, suchterms can aid with further understanding thestructural mechanisms that drive the somatic hyper-mutations within B-cells to generate a diversityof antibodies with different affinities whenencountering antigens. With the ever-increasingtherapeutic usage of antibodies, such inferences




will have a direct impact on the design of proteinpharmaceuticals.

Materials and Methods

PDB structures and backbone entropy computation

We used the 18,555 high-quality crystal structurescollected in a previous study [74], which included allavailable structures in the PDB with a resolution of 2.5 Å orbetter and an R-value of 0.25 or less. The secondarystructure type of each residue (α-helix, β-strand or coil)was obtained directly from the PDB record. We construct-ed a Ramachandran map for each residue type in eachsecondary structure type. The map was discretized into10° × 10° bins to approximate 36 × 36 or 1296 backboneconformations. The number of occurrences of eachconformation was tallied, which upon division by the totalnumber of residues yielded the probability of thatconformation. For 20 amino acids (j = 1…20) and threesecondary structure types (k = 1, 2, 3), we computed 60entropy values (Sjk) using the entropy definition given inIntroduction. Similarly, we pooled 20 amino acids for eachsecondary structure to obtain three entropy values (Sk),which were used to investigate the impact of secondarystructures.The absolute values of the resulting entropies depend

on how finely we discretize the Ramachandran map; thatis, S would increase with larger total number of bins. Atsmaller bin sizes, there were too few data points in eachbin, while at larger bin sizes the discretization of theRamachandran map became too coarse. Nonetheless, therelative rankings of the entropies of 20 amino acids (Sjkand Sk) remain largely invariant. We performed all ouranalysis using the bin size of 10° × 10° because it wasused in previous studies [7,84] and resulted in sufficientdata points in most bins yet was fine enough fordiscretization of the Ramachandran map. Some of thebins still had low counts and corresponded to energeticallyunfavorable backbone conformations. To address theserare counts, we computed the Sjk and Sk values at aprobability threshold Pc, with the probabilities of bins belowthe cutoff set to 0. The probabilities of the remaining binswere rescaled to ensure that they summed to 1. Theabsolute S was a monotonically decreasing function of Pc.We varied Pc and plotted S–Pc curves, which displayedintriguing grouping patterns of residues (see Fig. 1 andResults).We performed our calculations atPc = 6.1 × 10−4;however, our resultswere not sensitive toPc, as described inResults.

Structural dataset of antibody and TCR complexes

We collected in Supplementary Table S1 the bindingkinetics data for 40 antibodies and 34 TCRs whose boundstructure coordinates were determined via X-ray crystal-lography and were deposited in the PDB§ [31]. We onlyincluded the complexes with published binding affinity andkinetics constants as determined by SPR. We determinedthe CDRs of the selected antibodies with the “Kabat”numbering option of the Abnum server∥ [85]. We alsoutilize the “Kabat” definitions to manually determine the


CDRs of the TCRs. The Kabat CDR regions for theselected structures in Supplementary Table S1 aremarked in the primary sequences found in SupplementaryTable S2.

C225 sFab expression and purification

We cloned the heavy and light chains of the C225 Fabinto the GS vector. Point mutations were introduced intothe GS construct via site-directed mutagenesis usingstandard PCR protocols. Human 293-Freestyle cells(Invitrogen Corp., Carlsbad, CA) were cultivated andtransfected with wild-type and mutant GS vectors accord-ing to manufacturer's specifications in suspension shakeflask cultures. Briefly, uncut plasmid DNA and 293 fectinwere allowed to complex for 25 min. HEK 293 cells werere-suspended in fresh medium (vortex to remove clumps)and subsequently combined with DNA/fectin complexbefore incubation at 37 °C. Fifty milliliters of the condi-tioned supernatant was harvested after 6 days andassayed for protein expression. We utilized standardaffinity column chromatography (Protein G agarosecolumn from Pierce; catalogue number: 20399) to purifythe soluble Fabs from the 50 ml of the HEK 293 cellexpression supernatant. Before loading the supernatantonto the Protein G column, we concentrated the 50-ml293-supernatant to 10 ml then mixed it with 2 volumes ofthe binding buffer provided with the Pierce kit.

A6 TCR and TAX-HLA-A2 expression and purification

Wild-typeHLA-A2,β2M, TCRα (wild type andmutant) andTCRβ (wild type and mutant) were expressed separately asinclusion bodies in E. coli. Mutations were introduced to theconstructs via site-directed mutagenesis using standardPCR protocols. The inclusion bodies were refolded usingprotocols based on those of Garboczi et al. [86,87].However, fast protein liquid chromatography using asize-exclusion column (rather than dialysis and ion ex-change) was used to purify the refolded proteins from theaggregates [49]; this resulted in much faster purification ofthe proteins than the previously published method [86].

Measurement of binding kinetics

HBSEP [0.01 M Hepes (pH 7.4), 0.15 mM NaCl, 3 mMethylenediaminetetraacetic acid and 0.005% (v/v) Surfac-tant P20] was used as a running buffer during the SPRexperiments while measuring the binding kinetics of C225(wild type and mutants) to huEGFR-Fc and A6 (wild typeand mutants) to TAX-HLA-A2. We assessed the binding ofwild-type C225 and its proline mutants to huEGFR-Fc at25 °C using Biacore 2000 (SPR) biosensor. Around 500–1000 response units of huEGFR-Fc were immobilized ontoa CM5 chip using the standard amine coupling procedure.The sFabs were injected for 180 s over immobilizedhuEGFR-Fc at a flow rate of 10 μl/min, followed by 300 sdissociation using HBSEP buffer. The sFab concentra-tions were gradients of 2-fold dilutions. After the bindingdissociation phase, regeneration of the huEGFR-Fcsurface was achieved with a single 60-s injection of10 mM HCl over all channels.



Present address: X. Luna, Kadmon Corporation,LLC, 450 East 29th Street, New York, NY 10016, USA.

http://zlab.bu.edu/ramahttp://www.pdb.org/pdb/home/home.do

http://www.bioinfo.org.uk/abs/abnum


Binding of wild-type and mutant A6 TCRs to theTax-HLA-A2 complex was evaluated at 25 °C usingBiacore 3000 (SPR) biosensor. Approximately 400 re-sponse units of each TCR was immobilized on the CM5chip using the standard amine coupling procedure. TheTax-HLA-A2 complex was injected for 180 s over theimmobilized TCRs at a flow rate of 100 μl/min, followed by900 s dissociation using HBSEP buffer. The TAX-HLA-A2concentrations were gradients of 2-fold dilutions. After thebinding dissociation phase, the TCR baseline wasregenerated with a single 120-s injection of 0.01 MHepes (pH 7.4) 1 M NaCl over all channels.In order to correct for non-specific binding of the analyte

(C225 sFabs or Tax-HLA-A2) to the chip surface, we alsoinjected analyte over a reference surface on which noligand (huEGFR-Fc or A6 TCR) was bound; this signal wassubtracted from those of the ligand-bound cells. BIAeva-luation (Biacore) was used to analyze the results from thekinetic experiments. After double referencing to removeartifacts from nonspecific binding, we performed simulta-neous global fitting of the data for each concentrationgradient to a 1:1 Langmuir model to determine theassociation rate (kon), dissociation rate (koff) and dissoci-ation constant (KD = koff/kon). We used at least threedifferent concentration gradients of the analyte to computethe kinetic parameters and their corresponding samplestandard deviation.

Acknowledgments

The authors thank Mr. Robert Anderson for givingus access to the Rama Server. We also thank Ms.Michelle Iacolina for sequencing the C225 prolinemutations. We also thank Dr. Yong Yu for his inputwhile refolding and purifying the TAX-HLAA2 and A6mutants. The authors would like to acknowledgeDr. Alan Rigby, Dr. Aaron Chamberlain, Dr. J. MichaelSauder andMs. LeeMangiante for critically readingthis manuscript and for providing intriguingdiscussions.

Appendix A. Supplementary data

Supplementary data to this article can be foundonline at http://dx.doi.org/10.1016/j.jmb.2013.12.024.

Received 11 September 2013;Received in revised form 3 December 2013;

Accepted 19 December 2013Available online xxxx

Keywords:T-cell receptor;

antibody;conformational flexibility;

backbone entropy;affinity maturation


Abbreviations used:CDR, complementarity-determining region; TCR, T-cell

receptor; PDB, Protein Data Bank; SPR, surface plasmonresonance.

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Backbone Flexibility of CDR3 and Immune Recognition of Antigens