Comprehensive and quantitative mapping of energy landscapes for protein-protein interactions by rapid combinatorial scanning

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

Effect of mutation of NS2B cofactor residues on Dengue 2 NS2B-NS3 protease complex - an insight to viral replication

Dengue fever is an endemic virus-borne disease that causes many severe ailments, including dengue hemorrhagic fever and dengue shock syndrome. NS2B-NS3 protease is present in all four strains of the dengue virus. NS2B-NS3 is a non-structural protein that performs three distinct functions: protease activity, helicase activity, and nucleoside triphosphatase activity. NS2B-NS3 pro-complex plays a crucial role in viral replication, and NS2B interacts with NS3 protease at a flat active site with an amino acid of the N-terminal region. NS2B acts as a cofactor for NS3 protease. In the current study, the conserved residues of NS2B were identified. Dengue virus-2 NS2B was mutated at the identified conserved amino acid region to investigate the role of NS2B on activation of NS3 pro. Molecular dynamics simulations were performed to investigate the mutated complex's changes in stability, conformation, and free energy. The EAG mutant complex exhibited more unstable conformation, less hydrogen bond formation, and high binding energy than wild type. This result suggests a vital role of E63, A65, G69 mutation in NS2B for the interruption of activation of the NS3.Communicated by Ramaswamy H. Sarma.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔG_bind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

Mechanically transduced immunosorbent assay to measure protein-protein interactions

Measuring protein-protein interaction (PPI) affinities is fundamental to biochemistry. Yet, conventional methods rely upon the law of mass action and cannot measure many PPIs due to a scarcity of reagents and limitations in the measurable affinity ranges. Here, we present a novel technique that leverages the fundamental concept of friction to produce a mechanical signal that correlates to binding potential. The mechanically transduced immunosorbent (METRIS) assay utilizes rolling magnetic probes to measure PPI interaction affinities. METRIS measures the translational displacement of protein-coated particles on a protein-functionalized substrate. The translational displacement scales with the effective friction induced by a PPI, thus producing a mechanical signal when a binding event occurs. The METRIS assay uses as little as 20 pmols of reagents to measure a wide range of affinities while exhibiting a high resolution and sensitivity. We use METRIS to measure several PPIs that were previously inaccessible using traditional methods, providing new insights into epigenetic recognition.

Rheostat positions: A new classification of protein positions relevant to pharmacogenomics

To achieve the full potential of pharmacogenomics, one must accurately predict the functional outcomes that arise from amino acid substitutions in proteins. Classically, researchers have focused on understanding the consequences of individual substitutions. However, literature surveys have shown that most substitutions were created at evolutionarily conserved positions. Awareness of this bias leads to a shift in perspective, from considering the outcomes of individual substitutions to understanding the roles of individual protein positions. Conserved positions tend to act as “toggle” switches, with most substitutions abolishing function. However, nonconserved positions have been found equally capable of affecting protein function. Indeed, many nonconserved positions act like functional dimmer switches (“rheostat” positions): this is revealed when multiple substitutions are made at a single position. Each substitution has a different functional outcome; the set of substitutions spans a range of outcomes. Finally, some nonconserved positions appear neutral, capable of accommodating all amino acid types without modifying function. This paper reviews the currently-known properties of rheostat positions, with examples shown for pyruvate kinase, organic anion transporting polypeptide 1B1, the beta-lactamase inhibitory protein, and angiotensin-converting enzyme 2. Outcomes observed for rheostat positions have implications for the rational design of drug analogs and allosteric drugs. Furthermore, this new framework—comprising three types of protein positions—provides a new approach to interpreting disease and population-based databases of amino acid changes. In conclusion, although a full understanding of substitution outcomes at rheostat positions poses a challenge, utilization of this new frame of reference will further advance the application of pharmacogenomics.

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG_bind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG_bind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔG_bind data points on purified proteins to generate ΔΔG_bind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔG_bind for this interaction could be quantified with high accuracy over the range of 12 kcal mol⁻¹ displayed by various BPTI single mutants.

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

Optimization of peptidic HIV-1 fusion inhibitor T20 by phage display

The HIV fusion inhibitor T20 has been approved to treat those living with HIV/AIDS, but treatment gives rise to resistant viruses. Using combinatorial phage-displayed libraries, we applied a saturation scan approach to dissect the entire T20 sequence for binding to a prefusogenic five-helix bundle (5HB) mimetic of HIV-1 gp41. Our data set compares all possible amino acid substitutions at all positions, and affords a complete view of the complex molecular interactions governing the binding of T20 to 5HB. The scan of T20 revealed that 12 of its 36 positions were conserved for 5HB binding, which cluster into three epitopes: hydrophobic epitopes at the ends and a central dyad of hydrophilic residues. The scan also revealed that the T20 sequence was highly adaptable to mutations at most positions, demonstrating a striking structural plasticity that allows multiple amino acid substitutions at contact points to adapt to conformational changes, and also at noncontact points to fine-tune the interface. Based on the scan result and structural knowledge of the gp41 fusion intermediate, a library was designed with tailored diversity at particular positions of T20 and was used to derive a variant (T20v1) that was found to be a highly effective inhibitor of infection by multiple HIV-1 variants, including a common T20-escape mutant. These findings show that the plasticity of the T20 functional sequence space can be exploited to develop variants that overcome resistance of HIV-1 variants to T20 itself, and demonstrate the utility of saturation scanning for rapid epitope mapping and protein engineering.

Novel MASP-2 inhibitors developed via directed evolution of human TFPI1 are potent lectin pathway inhibitors

The lectin pathway (LP) of the complement system is an important antimicrobial defense mechanism, but it also contributes significantly to ischemia reperfusion injury (IRI) associated with myocardial infarct, stroke, and several other clinical conditions. Mannan-binding lectin-associated serine proteinase 2 (MASP-2) is essential for LP activation, and therefore, it is a potential drug target. We have previously developed the first two generations of MASP-2 inhibitors by in vitro evolution of two unrelated canonical serine proteinase inhibitors. These inhibitors were selective LP inhibitors, but their nonhuman origin rendered them suboptimal lead molecules for drug development. Here, we present our third-generation MASP-2 inhibitors that were developed based on a human inhibitor scaffold. We subjected the second Kunitz domain of human tissue factor pathway inhibitor 1 (TFPI1 D2) to directed evolution using phage display to yield inhibitors against human and rat MASP-2. These novel TFPI1-based MASP-2 inhibitor (TFMI-2) variants are potent and selective LP inhibitors in both human and rat serum. Directed evolution of the first Kunitz domain of TFPI1 had already yielded the potent kallikrein inhibitor, Kalbitor® (ecallantide), which is an FDA-approved drug to treat acute attacks of hereditary angioedema. Like hereditary angioedema, acute IRI is also related to the uncontrolled activation of a specific plasma serine proteinase. Therefore, TFMI-2 variants are promising lead molecules for drug development against IRI.

Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries

Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. Here, we introduce a novel and efficient strategy for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and in silico next-generation sequencing analysis. We map the binding landscape of a non-selective trypsin inhibitor, the amyloid protein precursor inhibitor (APPI), to each of the four human serine proteases (kallikrein-6, mesotrypsin, and anionic and cationic trypsins). We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the four proteases. Our strategy can be used as a platform for the development of a new generation of target-selective probes and therapeutic agents based on selective protein-protein interactions.

BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces

Computational protein design (CPD) algorithms that compute binding affinity, K_a, search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design, continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK*, a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximate K_a for a combinatorial number of sequences, and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximating K_a for up to 10⁵-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein-ligand design problems, BBK* provably approximates K_a up to 1982-fold faster than the previous state-of-the-art iMinDEE/[Formula: see text]/[Formula: see text] algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods.

Most yeast SH3 domains bind peptide targets with high intrinsic specificity

A need exists to develop bioinformatics for predicting differences in protein function, especially for members of a domain family who share a common fold, yet are found in a diverse array of proteins. Many domain families have been conserved over large evolutionary spans and representative genomic data during these periods are now available. This allows a simple method for grouping domain sequences to reveal common and unique/specific binding residues. As such, we hypothesize that sequence alignment analysis of the yeast SH3 domain family across ancestral species in the fungal kingdom can determine whether each member encodes specific information to bind unique peptide targets. With this approach, we identify important specific residues for a given domain as those that show little conservation within an alignment of yeast domain family members (paralogs) but are conserved in an alignment of its direct relatives (orthologs). We find most of the yeast SH3 domain family members have maintained unique amino acid conservation patterns that suggest they bind peptide targets with high intrinsic specificity through varying degrees of non-canonical recognition. For a minority of domains, we predict a less diverse binding surface, likely requiring additional factors to bind targets specifically. We observe that our predictions are consistent with high throughput binding data, which suggests our approach can probe intrinsic binding specificity in any other interaction domain family that is maintained during evolution.

Using Evolution to Guide Protein Engineering: The Devil IS in the Details

For decades, protein engineers have endeavored to reengineer existing proteins for novel applications. Overall, protein folds and gross functions can be readily transferred from one protein to another by transplanting large blocks of sequence (i.e., domain recombination). However, predictably fine-tuning function (e.g., by adjusting ligand affinity, specificity, catalysis, and/or allosteric regulation) remains a challenge. One approach has been to use the sequences of protein families to identify amino acid positions that change during the evolution of functional variation. The rationale is that these nonconserved positions could be mutated to predictably fine-tune function. Evolutionary approaches to protein design have had some success, but the engineered proteins seldom replicate the functional performances of natural proteins. This Biophysical Perspective reviews several complexities that have been revealed by evolutionary and experimental studies of protein function. These include 1) challenges in defining computational and biological thresholds that define important amino acids; 2) the co-occurrence of many different patterns of amino acid changes in evolutionary data; 3) difficulties in mapping the patterns of amino acid changes to discrete functional parameters; 4) the nonconventional mutational outcomes that occur for a particular group of functionally important, nonconserved positions; 5) epistasis (nonadditivity) among multiple mutations; and 6) the fact that a large fraction of a protein's amino acids contribute to its overall function. To overcome these challenges, new goals are identified for future studies.

Combining ancestral sequence reconstruction with protein design to identify an interface hotspot in a key metabolic enzyme complex

It is important to identify hotspot residues that determine protein-protein interactions in interfaces of macromolecular complexes. We have applied a combination of ancestral sequence reconstruction and protein design to identify hotspots within imidazole glycerol phosphate synthase (ImGPS). ImGPS is a key metabolic enzyme complex, which links histidine and de novo purine biosynthesis and consists of the cyclase subunit HisF and the glutaminase subunit HisH. Initial fluorescence titration experiments showed that HisH from Zymomonas mobilis (zmHisH) binds with high affinity to the reconstructed HisF from the last universal common ancestor (LUCA-HisF) but not to HisF from Pyrobaculum arsenaticum (paHisF), which differ by 103 residues. Subsequent titration experiments with a reconstructed evolutionary intermediate linking LUCA-HisF and paHisF and inspection of the subunit interface of a contemporary ImGPS allowed us to narrow down the differences crucial for zmHisH binding to nine amino acids of HisF. Homology modeling and in silico mutagenesis studies suggested that at most two of these nine HisF residues are crucial for zmHisH binding. These computational results were verified by experimental site-directed mutagenesis, which finally enabled us to pinpoint a single amino acid residue in HisF that is decisive for high-affinity binding of zmHisH. Our work shows that the identification of protein interface hotspots can be very efficient when reconstructed proteins with different binding properties are included in the analysis. Proteins 2017; 85:312-321. © 2016 Wiley Periodicals, Inc.

Construction of Structural Mimetics of the Thyrotropin Receptor Intracellular Domain

The signaling of a G protein-coupled receptor (GPCR) is dictated by the complementary responsiveness of interacting intracellular effectors such as G proteins. Many GPCRs are known to couple to more than one G protein subtype and induce a multitude of signaling pathways, although the in vivo relevance of particular pathways is mostly unrecognized. Dissecting GPCR signaling in terms of the pathways that are activated will boost our understanding of the molecular fundamentals of hormone action. The structural determinants governing the selectivity of GPCR/G protein coupling, however, remain obscure. Here, we describe the design of soluble GPCR mimetics to study the details of the interplay between G-proteins and activators. We constructed functional mimetics of the intracellular domain of a model GPCR, the thyrotropin receptor. We based the construction on a unique scaffold, 6-Helix, an artificial protein that was derived from the elements of the trimer-of-hairpins structure of HIV gp41 and represents a bundle of six α-helices. The 6-Helix scaffold, which endowed the substituted thyrotropin receptor intracellular domain elements with spatial constraints analogous to those found in native receptors, enabled the reconstitution of a microdomain that consists of intracellular loops 2 and 3, and is capable of binding and activating Gα-(s). The 6-Helix-based mimetics could be used as a platform to study the molecular basis of GPCR/G protein recognition. Such knowledge could help investigators develop novel therapeutic strategies for GPCR-related disorders by targeting the GPCR/G protein interfaces and counteracting cellular dysfunctions via focused tuning of GPCR signaling.

Low-stringency selection of TEM1 for BLIP shows interface plasticity and selection for faster binders

Protein-protein interactions occur via well-defined interfaces on the protein surface. Whereas the location of homologous interfaces is conserved, their composition varies, suggesting that multiple solutions may support high-affinity binding. In this study, we examined the plasticity of the interface of TEM1 β-lactamase with its protein inhibitor BLIP by low-stringency selection of a random TEM1 library using yeast surface display. Our results show that most interfacial residues could be mutated without a loss in binding affinity, protein stability, or enzymatic activity, suggesting plasticity in the interface composition supporting high-affinity binding. Interestingly, many of the selected mutations promoted faster association. Further selection for faster binders was achieved by drastically decreasing the library-ligand incubation time to 30 s. Preequilibrium selection as suggested here is a novel methodology for specifically selecting faster-associating protein complexes.

The power of multiplexed functional analysis of genetic variants

New technologies have recently enabled saturation mutagenesis and functional analysis of nearly all possible variants of regulatory elements or proteins of interest in single experiments. Here we discuss the past, present, and future of such multiplexed (functional) assays for variant effects (MAVEs). MAVEs provide detailed insight into sequence-function relationships, and they may prove critical for the prospective clinical interpretation of genetic variants.

MASP-3 is the exclusive pro-factor D activator in resting blood: the lectin and the alternative complement pathways are fundamentally linked

MASP-3 was discovered 15 years ago as the third mannan-binding lectin (MBL)-associated serine protease of the complement lectin pathway. Lacking any verified substrate its role remained ambiguous. MASP-3 was shown to compete with a key lectin pathway enzyme MASP-2 for MBL binding, and was therefore considered to be a negative complement regulator. Later, knock-out mice experiments suggested that MASP-1 and/or MASP-3 play important roles in complement pro-factor D (pro-FD) maturation. However, studies on a MASP-1/MASP-3-deficient human patient produced contradicting results. In normal resting blood unperturbed by ongoing coagulation or complement activation, factor D is present predominantly in its active form, suggesting that resting blood contains at least one pro-FD activating proteinase that is not a direct initiator of coagulation or complement activation. We have recently showed that all three MASPs can activate pro-FD in vitro. In resting blood, however, using our previously evolved MASP-1 and MASP-2 inhibitors we proved that neither MASP-1 nor MASP-2 activates pro-FD. Other plasma proteinases, particularly MASP-3, remained candidates for that function. For this study we evolved a specific MASP-3 inhibitor and unambiguously proved that activated MASP-3 is the exclusive pro-FD activator in resting blood, which demonstrates a fundamental link between the lectin and alternative pathways.

Cold Spots in Protein Binding

Understanding the energetics and architecture of protein-binding interfaces is important for basic research and could potentially facilitate the design of novel binding domains for biotechnological applications. It is well accepted that a few key residues at binding interfaces (binding hot spots) are responsible for contributing most to the free energy of binding. In this opinion article, we introduce a new concept of 'binding cold spots', or interface positions occupied by suboptimal amino acids. Such positions exhibit a potential for affinity enhancement through various mutations. We give several examples of cold spots from different protein-engineering studies and argue that identification of such positions is crucial for studies of protein evolution and protein design.

Saturation scanning of ubiquitin variants reveals a common hot spot for binding to USP2 and USP21

A detailed understanding of the molecular mechanisms whereby ubiquitin (Ub) recognizes enzymes in the Ub proteasome system is crucial for understanding the biological function of Ub. Many structures of Ub complexes have been solved and, in most cases, reveal a large structural epitope on a common face of the Ub molecule. However, owing to the generally weak nature of these interactions, it has been difficult to map in detail the functional contributions of individual Ub side chains to affinity and specificity. Here we took advantage of Ub variants (Ubvs) that bind tightly to particular Ub-specific proteases (USPs) and used phage display and saturation scanning mutagenesis to comprehensively map functional epitopes within the structural epitopes. We found that Ubvs that bind to USP2 or USP21 contain a remarkably similar core functional epitope, or "hot spot," consisting mainly of positions that are conserved as the wild type sequence, but also some positions that prefer mutant sequences. The Ubv core functional epitope contacts residues that are conserved in the human USP family, and thus it is likely important for the interactions of Ub across many family members.

Adaptation in protein fitness landscapes is facilitated by indirect paths

The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20^L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 20⁴ = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve.

DOI:http://dx.doi.org/10.7554/eLife.16965.001

Proteins can evolve over time by changing their component parts, which are called amino acids. These changes usually happen one at a time and natural selection tends to preserve those changes that make the protein more efficient at its specific tasks, while discarding those that impair the protein’s activity. However the effect of each change depends on the protein as a whole, and so two changes that separately make the protein worse can make it much better if they occur together. This phenomenon is called epistasis and in some cases it can trap proteins in a sub-optimal form and prevent them from improving further.

Proteins are made from twenty different kinds of amino acid, and there are millions of different combinations of amino acids that could, in theory, make a protein of a given length. Studying protein evolution involves making variants of the same protein, each with just a few changes, and comparing how efficient, or “fit”, they are. Previous studies only measured the fitness of a few variants and showed that epistasis could block protein evolution by requiring the protein to lose some fitness before it could improve further. However, new techniques have now made it easier to study protein evolution by testing many more protein variants.

Wu, Dai et al. focused on four amino acids in part of a protein called GB1 and tested the efficiency of every possible combination of these four amino acids, a total of 160,000 (20⁴) variants. Contrary to expectations, the results suggested that the protein could evolve quickly to maximise fitness despite there being epistasis between the four amino acids. Overcoming epistasis typically involved making a change to one amino acid that paved the way for further changes while avoiding the need to lose fitness. The original change could then be reversed once the epistasis was overcome. The complexity of this solution means it can only be seen by studying a large number of protein variants that represent many alternative sequences of protein changes.

Wu, Dai et al. conclude that proteins are able to achieve a higher level of fitness through evolution by exploring a large number of changes. There are many possible changes for each protein and it is this variety that, despite epistasis, allows proteins to become naturally optimised for the tasks that they perform. While the full complexity of protein evolution cannot be explored at the moment, as technology advances it will become possible to study more protein variants. Such advances would therefore hopefully allow researchers to discover even more about the natural mechanisms of protein evolution.

DOI:http://dx.doi.org/10.7554/eLife.16965.002

Structural determinants governing S100A4-induced isoform-selective disassembly of nonmuscle myosin II filaments

The Ca(2+) -binding protein S100A4 interacts with the C terminus of nonmuscle myosin IIA (NMIIA) causing filament disassembly, which is correlated with an increased metastatic potential of tumor cells. Despite high sequence similarity of the three NMII isoforms, S100A4 discriminates against binding to NMIIB. We searched for structural determinants of this selectivity. Based on paralog scanning using phage display, we identified a single position as major determinant of isoform selectivity. Reciprocal single amino acid replacements showed that at position 1907 (NMIIA numbering), the NMIIA/NMIIC-specific alanine provides about 60-fold higher affinity than the NMIIB-specific asparagine. The structural background of this can be explained in part by a communication between the two consecutive α-helical binding segments. This communication is completely abolished by the Ala-to-Asn substitution. Mutual swapping of the disordered tailpieces only slightly affects the affinity of the NMII chimeras. Interestingly, we found that the tailpiece and position 1907 act in a nonadditive fashion. Finally, we also found that the higher stability of the C-terminal coiled-coil region of NMIIB also discriminates against interaction with S100A4. Our results clearly show that the isoform-selective binding of S100A4 is determined at multiple levels in the structure of the three NMII isoforms and the corresponding functional elements of NMII act synergistically with one another resulting in a complex interaction network. The experimental and in silico results suggest two divergent evolutionary pathways: NMIIA and NMIIB evolved to possess S100A4-dependent and -independent regulations, respectively.

Protein Engineering by Combined Computational and In Vitro Evolution Approaches

Two alternative strategies are commonly used to study protein-protein interactions (PPIs) and to engineer protein-based inhibitors. In one approach, binders are selected experimentally from combinatorial libraries of protein mutants that are displayed on a cell surface. In the other approach, computational modeling is used to explore an astronomically large number of protein sequences to select a small number of sequences for experimental testing. While both approaches have some limitations, their combination produces superior results in various protein engineering applications. Such applications include the design of novel binders and inhibitors, the enhancement of affinity and specificity, and the mapping of binding epitopes. The combination of these approaches also aids in the understanding of the specificity profiles of various PPIs.

Engineered Autonomous Human Variable Domains

BACKGROUND

The complex multi-chain architecture of antibodies has spurred interest in smaller derivatives that retain specificity but can be more easily produced in bacteria. Domain antibodies consisting of single variable domains are the smallest antibody fragments and have been shown to possess enhanced ability to target epitopes that are difficult to access using multidomain antibodies. However, in contrast to natural camelid antibody domains, human variable domains typically suffer from low stability and high propensity to aggregate.

METHODS

This review summarizes strategies to improve the biophysical properties of heavy chain variable domains from human antibodies with an emphasis on aggregation resistance. Several protein engineering approaches have targeted antibody frameworks and complementarity determining regions to stabilize the native state and prevent aggregation of the denatured state.

CONCLUSION

Recent findings enable the construction of highly diverse libraries enriched in aggregation-resistant variants that are expected to provide binders to diverse antigens. Engineered domain antibodies possess unique advantages in expression, epitope preference and flexibility of formatting over conventional immunoreagents and are a promising class of antibody fragments for biomedical development.

Antibody Binding Selectivity: Alternative Sets of Antigen Residues Entail High-Affinity Recognition

Understanding the relationship between protein sequence and molecular recognition selectivity remains a major challenge. The antibody fragment scFv1F4 recognizes with sub nM affinity a decapeptide (sequence 6TAMFQDPQER15) derived from the N-terminal end of human papilloma virus E6 oncoprotein. Using this decapeptide as antigen, we had previously shown that only the wild type amino-acid or conservative replacements were allowed at positions 9 to 12 and 15 of the peptide, indicating a strong binding selectivity. Nevertheless phenylalanine (F) was equally well tolerated as the wild type glutamine (Q) at position 13, while all other amino acids led to weaker scFv binding. The interfaces of complexes involving either Q or F are expected to diverge, due to the different physico-chemistry of these residues. This would imply that high-affinity binding can be achieved through distinct interfacial geometries. In order to investigate this point, we disrupted the scFv-peptide interface by modifying one or several peptide positions. We then analyzed the effect on binding of amino acid changes at the remaining positions, an altered susceptibility being indicative of an altered role in complex formation. The 23 starting variants analyzed contained replacements whose effects on scFv1F4 binding ranged from minor to drastic. A permutation analysis (effect of replacing each peptide position by all other amino acids except cysteine) was carried out on the 23 variants using the PEPperCHIP® Platform technology. A comparison of their permutation patterns with that of the wild type peptide indicated that starting replacements at position 11, 12 or 13 modified the tolerance to amino-acid changes at the other two positions. The interdependence between the three positions was confirmed by SPR (Biacore® technology). Our data demonstrate that binding selectivity does not preclude the existence of alternative high-affinity recognition modes.

Combinatorial and Computational Approaches to Identify Interactions of Macrophage Colony-stimulating Factor (M-CSF) and Its Receptor c-FMS

The molecular interactions between macrophage colony-stimulating factor (M-CSF) and the tyrosine kinase receptor c-FMS play a key role in the immune response, bone metabolism, and the development of some cancers. Because no x-ray structure is available for the human M-CSF · c-FMS complex, the binding epitope for this complex is largely unknown. Our goal was to identify the residues that are essential for binding of the human M-CSF to c-FMS. For this purpose, we used a yeast surface display (YSD) approach. We expressed a combinatorial library of monomeric M-CSF (M-CSFM) single mutants and screened this library to isolate variants with reduced affinity for c-FMS using FACS. Sequencing yielded a number of single M-CSFM variants with mutations both in the direct binding interface and distant from the binding site. In addition, we used computational modeling to map the identified mutations onto the M-CSFM structure and to classify the mutations into three groups as follows: those that significantly decrease protein stability; those that destroy favorable intermolecular interactions; and those that decrease affinity through allosteric effects. To validate the YSD and computational data, M-CSFM and three variants were produced as soluble proteins; their affinity and structure were analyzed; and very good correlations with both YSD data and computational predictions were obtained. By identifying the M-CSFM residues critical for M-CSF · c-FMS interactions, we have laid down the basis for a deeper understanding of the M-CSF · c-FMS signaling mechanism and for the development of target-specific therapeutic agents with the ability to sterically occlude the M-CSF·c-FMS binding interface.

High throughput functional epitope mapping: revisiting phage display platform to scan target antigen surface

Antibody engineering must be accompanied by mapping strategies focused on identifying the epitope recognized by each antibody to define its unique functional identity. High throughput fine specificity determination remains technically challenging. We review recent experiences aimed at revisiting the oldest and most extended display technology to develop a robust epitope mapping platform, based on the ability to manipulate target-derived molecules (ranging from the whole native antigen to antigen domains and smaller fragments) on filamentous phages. Single, multiple and combinatorial mutagenesis allowed comprehensive scanning of phage-displayed antigen surface that resulted in the identification of clusters of residues contributing to epitope formation. Functional pictures of the epitope(s) were thus delineated in the natural context. Successful mapping of antibodies against interleukin-2, epidermal growth factor and its receptor, and vascular endothelial growth factor showed the versatility of these procedures, which combine the accuracy of site-directed mutagenesis with the high throughput potential of phage display.

High-resolution sequence-function mapping of full-length proteins

Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest. Deep sequencing of library DNA allows frequency reconstruction for tens of thousands of variants in a single experiment, yet short read lengths of current sequencers makes it challenging to probe genes encoding full-length proteins. Here we extend the scope of sequence-function maps to entire protein sequences with a modular, universal sequence tiling method. We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections. Using this protocol, sequence-function maps covering full sequences can be obtained in four to six weeks. Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain

BACKGROUND

Nonadditivity in fitness effects from two or more mutations, termed epistasis, can result in compensation of deleterious mutations or negation of beneficial mutations. Recent evidence shows the importance of epistasis in individual evolutionary pathways. However, an unresolved question in molecular evolution is how often and how significantly fitness effects change in alternative genetic backgrounds.

RESULTS

To answer this question, we quantified the effects of all single mutations and double mutations between all positions in the IgG-binding domain of protein G (GB1). By observing the first two steps of all possible evolutionary pathways using this fitness profile, we were able to characterize the extent and magnitude of pairwise epistasis throughout an entire protein molecule. Furthermore, we developed a novel approach to quantitatively determine the effects of single mutations on structural stability (ΔΔGU). This enabled determination of the importance of stability effects in functional epistasis.

CONCLUSIONS

Our results illustrate common biophysical mechanisms for occurrences of positive and negative epistasis. Our results show pervasive positive epistasis within a conformationally dynamic network of residues. The stability analysis shows that significant negative epistasis, which is more common than positive epistasis, mostly occurs between combinations of destabilizing mutations. Furthermore, we show that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background. The distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.

A "fuzzy"-logic language for encoding multiple physical traits in biomolecules

To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.

SORTCERY-A High-Throughput Method to Affinity Rank Peptide Ligands

Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein-protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast-displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting of a library, deep sequencing of sorted pools and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY's utility by ranking 1026 BH3 (Bcl-2 homology 3) peptides with respect to their affinities for the anti-apoptotic protein Bcl-xL. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60nM. High-resolution ranking can be used to improve our understanding of sequence-function relationships and to support the development of computational models for predicting and designing novel interactions.

Measuring the activity of protein variants on a large scale using deep mutational scanning

Deep mutational scanning marries selection for protein function to high-throughput DNA sequencing in order to quantify the activity of variants of a protein on a massive scale. First, an appropriate selection system for the protein function of interest is identified and validated. Second, a library of variants is created, introduced into the selection system and subjected to selection. Third, library DNA is recovered throughout the selection and deep-sequenced. Finally, a functional score for each variant is calculated on the basis of the change in the frequency of the variant during the selection. This protocol describes the steps that must be carried out to generate a large-scale mutagenesis data set consisting of functional scores for up to hundreds of thousands of variants of a protein of interest. Establishing an assay, generating a library of variants and carrying out a selection and its accompanying sequencing takes on the order of 4-6 weeks; the initial data analysis can be completed in 1 week.

PURE ribosome display and its application in antibody technology

Ribosome display utilizes formation of the mRNA-ribosome-polypeptide ternary complex in a cell-free protein synthesis system to link genotype (mRNA) to phenotype (polypeptide). However, the presence of intrinsic components, such as nucleases in the cell-extract-based cell-free protein synthesis system, reduces the stability of the ternary complex, which would prevent attainment of reliable results. We have developed an efficient and highly controllable ribosome display system using the PURE (Protein synthesis Using Recombinant Elements) system. The mRNA-ribosome-polypeptide ternary complex is highly stable in the PURE system, and the selected mRNA can be easily recovered because activities of nucleases and other inhibitory factors are very low in the PURE system. We have applied the PURE ribosome display to antibody engineering approaches, such as epitope mapping and affinity maturation of antibodies, and obtained results showing that the PURE ribosome display is more efficient than the conventional method. We believe that the PURE ribosome display can contribute to the development of useful antibodies. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

Predicting affinity- and specificity-enhancing mutations at protein-protein interfaces

Manipulations of PPIs (protein-protein interactions) are important for many biological applications such as synthetic biology and drug design. Combinatorial methods have been traditionally used for such manipulations, failing, however, to explain the effects achieved. We developed a computational method for prediction of changes in free energy of binding due to mutation that bring about deeper understanding of the molecular forces underlying binding interactions. Our method could be used for computational scanning of binding interfaces and subsequent analysis of the interfacial sequence optimality. The computational method was validated in two biological systems. Computational saturated mutagenesis of a high-affinity complex between an enzyme AChE (acetylcholinesterase) and a snake toxin Fas (fasciculin) revealed the optimal nature of this interface with only a few predicted affinity-enhancing mutations. Binding measurements confirmed high optimality of this interface and identified a few mutations that could further improve interaction fitness. Computational interface scanning of a medium-affinity complex between TIMP-2 (tissue inhibitor of metalloproteinases-2) and MMP (matrix metalloproteinase) 14 revealed a non-optimal nature of the binding interface with multiple mutations predicted to stabilize the complex. Experimental results corroborated our computational predictions, identifying a large number of mutations that improve the binding affinity for this interaction and some mutations that enhance binding specificity. Overall, our computational protocol greatly facilitates the discovery of affinity- and specificity-enhancing mutations and thus could be applied for design of potent and highly specific inhibitors of any PPI.

Mapping of the binding landscape for a picomolar protein-protein complex through computation and experiment

Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.

A combinatorial mutagenesis approach for functional epitope mapping on phage-displayed target antigen: application to antibodies against epidermal growth factor

Although multiple different procedures to characterize the epitopes recognized by antibodies have been developed, site-directed mutagenesis remains the method of choice to define the energetic contribution of antigen residues to binding. These studies are useful to identify critical residues and to delineate functional maps of the epitopes. However, they tend to underestimate the roles of residues that are not critical for binding on their own, but contribute to the formation of the target epitope in an additive, or even cooperative, way. Mapping antigenic determinants with a diffuse energetic landscape, which establish multiple individually weak interactions with the antibody paratope, resulting in high affinity and specificity recognition of the epitope as a whole, is thus technically challenging. The current work was aimed at developing a combinatorial strategy to overcome the limitations of site-directed mutagenesis, relying on comprehensive randomization of discrete antigenic regions within phage-displayed antigen libraries. Two model antibodies recognizing epidermal growth factor were used to validate the mapping platform. Abrogation of antibody recognition due to the introduction of simultaneous replacements was able to show the involvement of particular amino acid clusters in epitope formation. The abundance of some of the original residues (or functionally equivalent amino acids sharing their physicochemical properties) among the set of mutated antigen variants selected on a given antibody highlighted their contributions and allowed delineation of a detailed functional map of the corresponding epitope. The use of the combinatorial approach could be expanded to map the interactions between other antigens/antibodies.

Rheostats and toggle switches for modulating protein function

The millions of protein sequences generated by genomics are expected to transform protein engineering and personalized medicine. To achieve these goals, tools for predicting outcomes of amino acid changes must be improved. Currently, advances are hampered by insufficient experimental data about nonconserved amino acid positions. Since the property “nonconserved” is identified using a sequence alignment, we designed experiments to recapitulate that context: Mutagenesis and functional characterization was carried out in 15 LacI/GalR homologs (rows) at 12 nonconserved positions (columns). Multiple substitutions were made at each position, to reveal how various amino acids of a nonconserved column were tolerated in each protein row. Results showed that amino acid preferences of nonconserved positions were highly context-dependent, had few correlations with physico-chemical similarities, and were not predictable from their occurrence in natural LacI/GalR sequences. Further, unlike the “toggle switch” behaviors of conserved positions, substitutions at nonconserved positions could be rank-ordered to show a “rheostatic”, progressive effect on function that spanned several orders of magnitude. Comparisons to various sequence analyses suggested that conserved and strongly co-evolving positions act as functional toggles, whereas other important, nonconserved positions serve as rheostats for modifying protein function. Both the presence of rheostat positions and the sequence analysis strategy appear to be generalizable to other protein families and should be considered when engineering protein modifications or predicting the impact of protein polymorphisms.

Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions

Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base changes leading to a change to the amino acid sequence of the encoded protein. Many of these variants are associated with disease, so nsSNPs have been well studied, with studies looking at the effects of nsSNPs on individual proteins, for example, on stability and enzyme active sites. In recent years, the impact of nsSNPs upon protein-protein interactions has also been investigated, giving a greater insight into the mechanisms by which nsSNPs can lead to disease. In this review, we summarize these studies, looking at the various mechanisms by which nsSNPs can affect protein-protein interactions. We focus on structural changes that can impair interaction, changes to disorder, gain of interaction, and post-translational modifications before looking at some examples of nsSNPs at human-pathogen protein-protein interfaces and the analysis of nsSNPs from a network perspective.

Side chain requirements for affinity and specificity in D5, an HIV-1 antibody derived from the VH1-69 germline segment

Background

Analysis of factors contributing to high affinity antibody-protein interactions provides insight into natural antibody evolution, and guides the design of antibodies with new or enhanced function. We previously studied the interaction between antibody D5 and its target, a designed protein based on HIV-1 gp41 known as 5-Helix, as a model system [Da Silva, G. F.; Harrison, J. S.; Lai, J. R., Biochemistry, 2010, 49, 5464–5472]. Antibody D5 represents an interesting case study because it is derived from the V_H1-69 germline segment; this germline segment is characterized by a hydrophobic second heavy chain complementarity determining region (HCDR2) that constitutes the major functional paratope in D5 and several antibodies derived from the same progenitor.

Results

Here we explore side chain requirements for affinity and specificity in D5 using phage display. Two D5-based libraries were prepared that contained diversity in all three light chain complementarity determining regions (LCDRs 1–3), and in the third HCDR (HCDR3). The first library allowed residues to vary among a restricted set of six amino acids (Tyr/Ala/Asp/Ser/His/Pro; D5-Lib-I). The second library was designed based on a survey of existing V_H1-69 antibody structures (D5-Lib-II). Both libraries were subjected to multiple rounds of selection against 5-Helix, and individual clones characterized. We found that selectants from D5-Lib-I generally had moderate affinity and specificity, while many clones from D5-Lib-II exhibited D5-like properties. Additional analysis of the D5-Lib-II functional population revealed position-specific biases for particular amino acids, many that differed from the identity of those side chains in D5.

Conclusions

Together these results suggest that there is some permissiveness for alternative side chains in the LCDRs and HCDR3 of D5, but that replacement with a minimal set of residues is not tolerated in this scaffold for 5-Helix recognition. This work provides novel information about this high-affinity interaction involving an antibody from the V_H1-69 germline segment.

Design of synthetic autonomous VH domain libraries and structural analysis of a VH domain bound to vascular endothelial growth factor

We compared the capacity of an autonomous heavy chain variable (VH) domain (VH-B1a) to support diversity within its antigen-binding site relative to the conventional antigen-binding fragment (Fab) from which it was derived. We find that VH-B1a can tolerate significant diversity within all three complementarity-determining regions (CDRs) and also within framework 3, and thus, VH-B1a and the Fab are similar in terms of the regions of the antigen-binding site that can tolerate diversity without compromising stability. We constructed libraries of synthetic VH domains and isolated binders with moderate affinity for vascular endothelial growth factor (VEGF) from a library in which only CDR3 was randomized. One binder was subjected to affinity maturation to derive an autonomous VH domain (VH-V1a) that recognized both human and mouse VEGF with high affinity (KD=16nM or 10nM, respectively). Structural analysis revealed that VH-V1a binds to an epitope that is distinct from the epitopes of a natural VEGF receptor and six different anti-VEGF Fabs. Moreover, VH-V1a recognizes VEGF by using an unusual paratope consisting predominantly of CDR3 but with significant contributions from framework residues within the former light chain interface. These results suggest that VH-B1a and other autonomous VH domains may be useful scaffolds to support both conventional libraries with antigen-binding sites built from the three CDR loops and, also, nonconventional libraries with antigen-binding sites built from CDR3 and the former light chain interface.

Robust in vitro affinity maturation strategy based on interface-focused high-throughput mutational scanning

Development of protein therapeutics or biosensors often requires in vitro affinity maturation. Here we report a robust affinity engineering strategy using a custom designed library. The strategy consists of two steps beginning with identification of beneficial single amino acid substitutions then combination. A high quality combinatorial library specifically customized to a given binding-interface can be rapidly designed by high-throughput mutational scanning of single substitution libraries. When applied to the optimization of a model antibody Fab fragment, the strategy created a diverse panel of high affinity variants. The most potent variant achieved 2110-fold affinity improvement to an equilibrium dissociation constant (Kd) of 3.45 pM with only 7 amino acid substitutions. The method should facilitate affinity engineering of a wide variety of protein-protein interactions due to its context-dependent library design strategy.