ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Improving Circulation Half-Life of Therapeutic Candidate N-TIMP2 by Unfolded Peptide Extension.

Matrix Metalloproteinases (MMPs) are drivers of many diseases including cancer and are established targets for drug development. Tissue inhibitors of metalloproteinases (TIMPs) are human proteins that inhibit MMPs and are being pursued for the development of anti-MMP therapeutics. TIMPs possess many attractive properties of a drug candidate, such as complete MMP inhibition, low toxicity and immunogenicity, high tissue permeability and others. A major challenge with TIMPs, however, is their formulation and delivery, as these proteins are quickly cleared from the bloodstream due to their small size. In this study, we explore a new method for plasma half-life extension for the N-terminal domain of TIMP2 (N-TIMP2) through appending it with a long intrinsically unfolded tail containing a random combination of Pro, Ala, and Thr (PATylation). We design, produce and explore two PATylated N-TIMP2 constructs with a tail length of 100- and 200-amino acids (N-TIMP2-PAT100 and N-TIMP2-PAT200, respectively). We demonstrate that both PATylated N-TIMP2 constructs possess apparent higher molecular weights compared to the wild-type protein and retain high inhibitory activity against MMP-9. Furthermore, when injected into mice, N-TIMP2-PAT200 exhibited a significant increase in plasma half-life compared to the non-PATylated variant, enhancing the therapeutic potential of the protein. Thus, we establish that PATylation could be successfully applied to TIMP-based therapeutics and offers distinct advantages as an approach for half-life extension, such as fully genetic encoding of the gene construct, mono-dispersion, and biodegradability. Furthermore, PATylation could be easily applied to N-TIMP2 variants engineered to possess high affinity and selectivity toward individual MMP family members, thus creating attractive candidates for drug development against MMP-related diseases.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces.

BACKGROUND: Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection. RESULTS: In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download. CONCLUSIONS: Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab. RESEARCH: google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

Targeting Ras with protein engineering.

Ras proteins are small GTPases that regulate cell growth and division. Mutations in Ras genes are associated with many types of cancer, making them attractive targets for cancer therapy. Despite extensive efforts, targeting Ras proteins with small molecules has been extremely challenging due to Ras's mostly flat surface and lack of small molecule-binding cavities. These challenges were recently overcome by the development of the first covalent small-molecule anti-Ras drug, sotorasib, highlighting the efficacy of Ras inhibition as a therapeutic strategy. However, this drug exclusively inhibits the Ras G12C mutant, which is not a prevalent mutation in most cancer types. Unlike the G12C variant, other Ras oncogenic mutants lack reactive cysteines, rendering them unsuitable for targeting via the same strategy. Protein engineering has emerged as a promising method to target Ras, as engineered proteins have the ability to recognize various surfaces with high affinity and specificity. Over the past few years, scientists have engineered antibodies, natural Ras effectors, and novel binding domains to bind to Ras and counteract its carcinogenic activities via a variety of strategies. These include inhibiting Ras-effector interactions, disrupting Ras dimerization, interrupting Ras nucleotide exchange, stimulating Ras interaction with tumor suppressor genes, and promoting Ras degradation. In parallel, significant advancements have been made in intracellular protein delivery, enabling the delivery of the engineered anti-Ras agents into the cellular cytoplasm. These advances offer a promising path for targeting Ras proteins and other challenging drug targets, opening up new opportunities for drug discovery and development.

Computational design of matrix metalloprotenaise-9 (MMP-9) resistant to auto-cleavage.

Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix. MMP-9 has been implicated in several diseases including neurodegeneration, arthritis, cardiovascular diseases, fibrosis and several types of cancer, resulting in a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9Cat) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9Cat variant that is active but stable to auto-cleavage. For this purpose, we first identified potential auto-cleavage sites on MMP-9Cat using mass spectroscopy and then eliminated the auto-cleavage site by predicting mutations that minimize auto-cleavage potential without reducing enzyme stability. Four computationally designed MMP-9Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after 7 days of incubation at 37°C. This MMP-9Cat variant, with an identical with MMP-9Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9CAT stabilization could be applied to redesign other proteases to improve their stability for various biotechnological applications.

Computational design of Matrix Metalloprotenaise-9 (MMP-9) resistant to auto-cleavage.

Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix and has been implicated as a major driver in cancer metastasis. Hence, there is a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9 Cat ) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9 Cat variant that is active but stable to autocleavage. For this purpose, we first identified potential autocleavage sites on MMP-9 Cat using mass spectroscopy and then eliminated the autocleavage site by predicting mutations that minimize autocleavage potential without reducing enzyme stability. Four computationally designed MMP-9 Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after seven days of incubation at 37°C. This MMP-9 Cat variant, with an identical to MMP- 9 Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9 CAT stabilization could be applied to redesign of other proteases to improve their stability for various biotechnological applications.

Designed Loop Extension Followed by Combinatorial Screening Confers High Specificity to a Broad Matrix MetalloproteinaseInhibitor.

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.

Cold spots are universal in protein-protein interactions.

Proteins interact with each other through binding interfaces that differ greatly in size and physico-chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein-protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo- and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high-affinity complexes showing fewer centrally located colds spots than low-affinity complexes. A larger number of cold spots is also found in non-cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo-dimeric complexes compared to hetero-complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

Engineered variants of the Ras effector protein RASSF5 (NORE1A) promote anticancer activities in lung adenocarcinoma.

Within the superfamily of small GTPases, Ras appears to be the master regulator of such processes as cell cycle progression, cell division, and apoptosis. Several oncogenic Ras mutations at amino acid positions 12, 13, and 61 have been identified that lose their ability to hydrolyze GTP, giving rise to constitutive signaling and eventually development of cancer. While disruption of the Ras/effector interface is an attractive strategy for drug design to prevent this constitutive activity, inhibition of this interaction using small molecules is impractical due to the absence of a cavity to which such molecules could bind. However, proteins and especially natural Ras effectors that bind to the Ras/effector interface with high affinity could disrupt Ras/effector interactions and abolish procancer pathways initiated by Ras oncogene. Using a combination of computational design and in vitro evolution, we engineered high-affinity Ras-binding proteins starting from a natural Ras effector, RASSF5 (NORE1A), which is encoded by a tumor suppressor gene. Unlike previously reported Ras oncogene inhibitors, the proteins we designed not only inhibit Ras-regulated procancer pathways, but also stimulate anticancer pathways initiated by RASSF5. We show that upon introduction into A549 lung carcinoma cells, the engineered RASSF5 mutants decreased cell viability and mobility to a significantly greater extent than WT RASSF5. In addition, these mutant proteins induce cellular senescence by increasing acetylation and decreasing phosphorylation of p53. In conclusion, engineered RASSF5 variants provide an attractive therapeutic strategy able to oppose cancer development by means of inhibiting of procancer pathways and stimulating anticancer processes.

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 ΔΔGbind 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.

Computational design and experimental optimization of protein binders with prospects for biomedical applications.

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

A KLK4 proteinase substrate capture approach to antagonize PAR1.

Proteinase-activated receptor-1 (PAR1), triggered by thrombin and other serine proteinases such as tissue kallikrein-4 (KLK4), is a key driver of inflammation, tumor invasiveness and tumor metastasis. The PAR1 transmembrane G-protein-coupled receptor therefore represents an attractive target for therapeutic inhibitors. We thus used a computational design to develop a new PAR1 antagonist, namely, a catalytically inactive human KLK4 that acts as a proteinase substrate-capture reagent, preventing receptor cleavage (and hence activation) by binding to and occluding the extracellular R41-S42 canonical PAR1 proteolytic activation site. On the basis of in silico site-saturation mutagenesis, we then generated KLK4S207A,L185D, a first-of-a-kind 'decoy' PAR1 inhibitor, by mutating the S207A and L185D residues in wild-type KLK4, which strongly binds to PAR1. KLK4S207A,L185D markedly inhibited PAR1 cleavage, and PAR1-mediated MAPK/ERK activation as well as the migration and invasiveness of melanoma cells. This 'substrate-capturing' KLK4 variant, engineered to bind to PAR1, illustrates proof of principle for the utility of a KLK4 'proteinase substrate capture' approach to regulate proteinase-mediated PAR1 signaling.

RASSF effectors couple diverse RAS subfamily GTPases to the Hippo pathway.

Small guanosine triphosphatases (GTPases) of the RAS superfamily signal by directly binding to multiple downstream effector proteins. Effectors are defined by a folded RAS-association (RA) domain that binds exclusively to GTP-loaded (activated) RAS, but the binding specificities of most RA domains toward more than 160 RAS superfamily GTPases have not been characterized. Ten RA domain family (RASSF) proteins comprise the largest group of related effectors and are proposed to couple RAS to the proapoptotic Hippo pathway. Here, we showed that RASSF1-6 formed complexes with the Hippo kinase ortholog MST1, whereas RASSF7-10 formed oligomers with the p53-regulating effectors ASPP1 and ASPP2. Moreover, only RASSF5 bound directly to activated HRAS and KRAS, and RASSFs did not augment apoptotic induction downstream of RAS oncoproteins. Structural modeling revealed that expansion of the RASSF effector family in vertebrates included amino acid substitutions to key residues that direct GTPase-binding specificity. We demonstrated that the tumor suppressor RASSF1A formed complexes with the RAS-related GTPases GEM, REM1, REM2, and the enigmatic RASL12. Furthermore, interactions between RASSFs and RAS GTPases blocked YAP1 nuclear localization. Thus, these simple scaffolds link the activation of diverse RAS family small G proteins to Hippo or p53 regulation.

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 (ΔΔGbind) 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 ΔΔGbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔGbind data points on purified proteins to generate ΔΔGbind 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 ΔΔGbind for this interaction could be quantified with high accuracy over the range of 12 kcal mol-1 displayed by various BPTI single mutants.

Allosteric Modulation of Binding Specificity by Alternative Packing of Protein Cores.

Hydrophobic cores are often viewed as tightly packed and rigid, but they do show some plasticity and could thus be attractive targets for protein design. Here we explored the role of different functional pressures on the core packing and ligand recognition of the SH3 domain from human Fyn tyrosine kinase. We randomized the hydrophobic core and used phage display to select variants that bound to each of three distinct ligands. The three evolved groups showed remarkable differences in core composition, illustrating the effect of different selective pressures on the core. Changes in the core did not significantly alter protein stability, but were linked closely to changes in binding affinity and specificity. Structural analysis and molecular dynamics simulations revealed the structural basis for altered specificity. The evolved domains had significantly reduced core volumes, which in turn induced increased backbone flexibility. These motions were propagated from the core to the binding surface and induced significant conformational changes. These results show that alternative core packing and consequent allosteric modulation of binding interfaces could be used to engineer proteins with novel functions.

Converting a broad matrix metalloproteinase family inhibitor into a specific inhibitor of MMP-9 and MMP-14.

MMP-14 and MMP-9 are two well-established cancer targets for which no specific clinically relevant inhibitor is available. Using a powerful combination of computational design and yeast surface display technology, we engineered such an inhibitor starting from a nonspecific MMP inhibitor, N-TIMP2. The engineered purified N-TIMP2 variants showed enhanced specificity toward MMP-14 and MMP-9 relative to a panel of off-target MMPs. MMP-specific N-TIMP2 sequence signatures were obtained that could be understood from the structural perspective of MMP/N-TIMP2 interactions. Our MMP-9 inhibitor exhibited 1000-fold preference for MMP-9 vs. MMP-14, which is likely to translate into significant differences under physiological conditions. Our results provide new insights regarding evolution of promiscuous proteins and optimization strategies for design of inhibitors with single-target specificities.

Analysis of Structural Features Contributing to Weak Affinities of Ubiquitin/Protein Interactions.

Ubiquitin is a small protein that enables one of the most common post-translational modifications, where the whole ubiquitin molecule is attached to various target proteins, forming mono- or polyubiquitin conjugations. As a prototypical multispecific protein, ubiquitin interacts non-covalently with a variety of proteins in the cell, including ubiquitin-modifying enzymes and ubiquitin receptors that recognize signals from ubiquitin-conjugated substrates. To enable recognition of multiple targets and to support fast dissociation from the ubiquitin modifying enzymes, ubiquitin/protein interactions are characterized with low affinities, frequently in the higher µM and lower mM range. To determine how structure encodes low binding affinity of ubiquitin/protein complexes, we analyzed structures of more than a hundred such complexes compiled in the Ubiquitin Structural Relational Database. We calculated various structure-based features of ubiquitin/protein binding interfaces and compared them to the same features of general protein-protein interactions (PPIs) with various functions and generally higher affinities. Our analysis shows that ubiquitin/protein binding interfaces on average do not differ in size and shape complementarity from interfaces of higher-affinity PPIs. However, they contain fewer favorable hydrogen bonds and more unfavorable hydrophobic/charge interactions. We further analyzed how binding interfaces change upon affinity maturation of ubiquitin toward its target proteins. We demonstrate that while different features are improved in different experiments, the majority of the evolved complexes exhibit better shape complementarity and hydrogen bond pattern compared to wild-type complexes. Our analysis helps to understand how low-affinity PPIs have evolved and how they could be converted into high-affinity PPIs.

Development of High Affinity and High Specificity Inhibitors of Matrix Metalloproteinase 14 through Computational Design and Directed Evolution.

Degradation of the extracellular matrices in the human body is controlled by matrix metalloproteinases (MMPs), a family of more than 20 homologous enzymes. Imbalance in MMP activity can result in many diseases, such as arthritis, cardiovascular diseases, neurological disorders, fibrosis, and cancers. Thus, MMPs present attractive targets for drug design and have been a focus for inhibitor design for as long as 3 decades. Yet, to date, all MMP inhibitors have failed in clinical trials because of their broad activity against numerous MMP family members and the serious side effects of the proposed treatment. In this study, we integrated a computational method and a yeast surface display technique to obtain highly specific inhibitors of MMP-14 by modifying the natural non-specific broad MMP inhibitor protein N-TIMP2 to interact optimally with MMP-14. We identified an N-TIMP2 mutant, with five mutations in its interface, that has an MMP-14 inhibition constant (Ki ) of 0.9 pm, the strongest MMP-14 inhibitor reported so far. Compared with wild-type N-TIMP2, this variant displays ∼900-fold improved affinity toward MMP-14 and up to 16,000-fold greater specificity toward MMP-14 relative to other MMPs. In an in vitro and cell-based model of MMP-dependent breast cancer cellular invasiveness, this N-TIMP2 mutant acted as a functional inhibitor. Thus, our study demonstrates the enormous potential of a combined computational/directed evolution approach to protein engineering. Furthermore, it offers fundamental clues into the molecular basis of MMP regulation by N-TIMP2 and identifies a promising MMP-14 inhibitor as a starting point for the development of protein-based anticancer therapeutics.

Identifying Residues that Determine SCF Molecular-Level Interactions through a Combination of Experimental and In silico Analyses.

The stem cell factor (SCF)/c-Kit receptor tyrosine kinase complex-with its significant roles in hematopoiesis and angiogenesis-is an attractive target for rational drug design. There is thus a need to map, in detail, the SCF/c-Kit interaction sites and the mechanisms that modulate this interaction. While most residues in the direct SCF/c-Kit binding interface can be identified from the existing crystal structure of the complex, other residues that affect binding through protein unfolding, intermolecular interactions, allosteric or long-distance electrostatic effects cannot be directly inferred. Here, we describe an efficient method for protein-wide epitope mapping using yeast surface display. A library of single SCF mutants that span the SCF sequence was screened for decreased affinity to soluble c-Kit. Sequencing of selected clones allowed the identification of mutations that reduce SCF binding affinity to c-Kit. Moreover, the screening of these SCF clones for binding to a structural antibody helped identify mutations that result in small or large conformational changes in SCF. Computational modeling of the experimentally identified mutations showed that these mutations reduced the binding affinity through one of the three scenarios: through SCF destabilization, through elimination of favorable SCF/c-Kit intermolecular interactions, or through allosteric changes. Eight SCF variants were expressed and purified. Experimentally measured in vitro binding affinities of these mutants to c-Kit confirmed both the yeast surface display selection results and the computational predictions. This study has thus identified the residues crucial for c-Kit/SCF binding and has demonstrated the advantages of using a combination of computational and combinatorial methods for epitope mapping.