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Ubiquitin (Ub) signals are recognized and decoded into cellular responses by Ub-receptors, proteins that tether the Ub-binding domain(s) (UBDs) with response elements. Typically, UBDs bind mono-Ub in highly dynamic and weak affinity manners, presenting challenges in identifying and characterizing their binding interfaces. Here, we report the development of a new approach to facilitate the detection of these weak interactions using split-reporter systems where two interacting proteins are proximally co-translated from a single mRNA. This proximity significantly enhances the readout signals of weak protein-protein interactions (PPIs). We harnessed this system to characterize the ultra-weak UBD and ENTH (Epsin N-terminal Homology) and discovered that the yeast Ent1-ENTH domain contains two Ub-binding patches. One is similar to a previously characterized patch on STAM1(signal-transducing adaptor molecule)-VHS (Vps27, Hrs, and STAM), and the other was predicted by AlphaFold. Using a split-CAT selection system that co-translates Ub and ENTH in combination with mutagenesis, we assessed and confirmed the existence of a novel binding patch around residue F53 on ENTH. Co-translation in the split-CAT system provides an effective tool for studying weak PPIs and offers new insights into Ub-receptor interactions.

Matrix metalloproteinases (MMPs) are a family of endopeptidases that degrade extracellular matrix proteins, functioning in various physiological processes such as tissue remodeling, embryogenesis, and morphogenesis. Dysregulation of these enzymes is linked to multiple diseases. Specific inhibition of particular MMPs is crucial for anti-MMP drug development as some MMPs have shown antidisease properties. In this study, we aimed to design a highly specific inhibitor of MMP-9, that plays a crucial role in cell invasion and metastasis, using tissue inhibitor of metalloproteinases 2 (TIMP2s), an endogenous broad-family MMP inhibitor, as a prototype. In our earlier work, we were able to narrow down the specificity of the N-terminal domain of TIMP2 (N-TIMP2) toward MMP-9, yet at the expense of lowering its affinity to MMP-9. In this study, a library of N-TIMP2 mutants based on previous design with randomized additional positions was sorted for binding to MMP-9 using yeast surface display. Two selected N-TIMP2 mutants were expressed, purified, and their inhibitory activity against a panel of MMPs was measured. The best engineered N-TIMP2 mutant (REY) exhibited a 2-fold higher affinity to MMP-9 than that of the WT N-TIMP2, and 6- to 1.1 x 10 4 -fold increase in binding specificity toward MMP-9 compared to five alternative MMPs. Moreover, REY demonstrated a significant increase in inhibition of cell invasion and proliferation compared to the WT N-TIMP2 in MDA-MB-231 breast cancer cells. Therefore, our engineered N-TIMP2 mutant emerges as a promising candidate for future therapeutic development, offering precise targeting of MMP-9 in MMP-9-driven diseases., Competing Interests: Conflict of interest J. M. S., N. R., and E. S. R. are co-inventors on US provisional patent 62/419/497, which covers the molecules reported in the article and their applications., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Matrix metalloproteinases (MMPs) are significant drivers of many diseases, including cancer, and are established targets for drug development. Tissue inhibitors of metalloproteinases (TIMPs) are endogenous MMP inhibitors and are being pursued for the development of anti-MMP therapeutics. TIMPs possess many attractive properties for drug candidates, such as complete MMP inhibition, low toxicity, low immunogenicity, and high tissue permeability. However, a major challenge with TIMPs is their rapid clearance from the bloodstream due to their small size. This study explores a method for extending the plasma half-life of the N-terminal domain of TIMP2 (N-TIMP2) by appending it with a long, intrinsically unfolded tail containing Pro, Ala, and Thr (PATylation). We designed and produced two PATylated N-TIMP2 constructs with tail lengths of 100 and 200 amino acids (N-TIMP2-PAT 100 and N-TIMP2-PAT 200 ). Both constructs demonstrated higher apparent molecular weights and retained high inhibitory activity against MMP-9. N-TIMP2-PAT 200 significantly increased plasma half-life in mice compared to the non-PATylated variant, enhancing its therapeutic potential. PATylation offers distinct advantages for half-life extension, such as fully genetic encoding, monodispersion, and biodegradability. It can be easily applied to N-TIMP2 variants engineered for high affinity and selectivity toward individual MMPs, creating promising candidates for drug development against MMP-related diseases., Competing Interests: The authors declare no conflicts of interest.

Matrix metallopeptidase 7 (MMP7) plays a crucial role in cancer metastasis and progression, making it an attractive target for therapeutic development. However, the development of selective MMP7 inhibitors is challenging due to the conservation of active sites across various matrix metalloproteinases (MMPs). Here, we have developed mirror-image random nonstandard peptides integrated discovery (MI-RaPID) technology to discover innate protease-resistant macrocyclic peptides that specifically bind to and inhibit human MMP7. One identified macrocyclic peptide against D-MMP7, termed D20, was synthesized in its mirror-image form, D'20, consisting of 12 D-amino acids, one cyclic β-amino acid, and a thioether bond. Notably, it potently inhibited MMP7 with an IC 50 value of 90 nM, and showed excellent selectivity over other MMPs with similar substrate specificity. Moreover, D'20 inhibited the migration of pancreatic cell line CFPAC-1, but had no effect on the cell proliferation and viability. D'20 exhibited excellent stability in human serum, as well as in simulated gastric and intestinal fluids. This study highlights that MI-RaPID technology can serve as a powerful tool to develop in vivo stable macrocyclic peptides for therapeutic applications., (© 2024 The Author(s). Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

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-PAT 100 and N-TIMP2-PAT 200 , 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-PAT 200 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.

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 (ΔΔG bind ) 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 ΔΔG bind . 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 ΔΔG bind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔG bind 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 ΔΔG bind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔG bind prediction, greatly facilitating future protein engineering and design studies.

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 ., (© 2024. The Author(s).)

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., (© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

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., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest with the content of this article., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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 ΔΔ G 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.

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., (The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

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.

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., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

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., (© 2018 Federation of European Biochemical Societies.)

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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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 ( K i ) 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., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)

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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

RAS is a molecular switch that regulates a large number of pathways through interactions with many effector proteins. Most RAS/effector complexes are short-lived, demonstrating fast association and fast dissociation rate and Kds ranging from 10(-8)-10(-5) M, compatible with the signaling function of these interactions in the cell. RAS effectors share little sequence homology but all contain an RAS binding domain that exhibits ubiquitin fold. All effectors bind to the same epitope on RAS by forming an intermolecular beta sheet and creating a number of favorable hydrogen bonds and salt bridges across the binding interface. Several hot-spots on both RAS and effector molecules constitute a general recognition mode. RAS/effector interactions occur only when RAS is found in the active, GTP-bound state, and are disrupted upon GTP hydrolysis, most probably due to increased flexibility of the RAS molecule. Recent NMR studies demonstrate how in the presence of multiple binding partners, RAS prefers certain effectors to others. The hierarchy of these interactions could be altered for RAS oncogenic mutants, thus perturbing the network of the downstream signaling. Insights obtained through biophysical and structural studies of effectors interacting with RAS and its mutants establish the basic principles that could be used for designing drugs in RAS-associated diseases.

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., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

Molecules capable of mimicking protein binding and/or functional sites present useful tools for a range of biomedical applications, including the inhibition of protein-ligand interactions. Such mimics of protein binding sites can currently be generated through structure-based design and chemical synthesis. Computational protein design could be further used to optimize protein binding site mimetics through rationally designed mutations that improve intermolecular interactions or peptide stability. Here, as a model for the study, we chose an interaction between human acetylcholinesterase (hAChE) and its inhibitor fasciculin-2 (Fas) because the structure and function of this complex is well understood. Structure-based design of mimics of the hAChE binding site for Fas yielded a peptide that binds to Fas at micromolar concentrations. Replacement of hAChE residues known to be essential for its interaction with Fas with alanine, in this peptide, resulted in almost complete loss of binding to Fas. Computational optimization of the hAChE mimetic peptide yielded a variant with slightly improved affinity to Fas, indicating that more rounds of computational optimization will be required to obtain peptide variants with greatly improved affinity for Fas. CD spectra in the absence and presence of Fas point to conformational changes in the peptide upon binding to Fas. Furthermore, binding of the optimized hAChE mimetic peptide to Fas could be inhibited by hAChE, providing evidence for a hAChE-specific peptide-Fas interaction., (Copyright © 2015 European Peptide Society and John Wiley & Sons, Ltd.)

Modulation of protein binding specificity is important for basic biology and for applied science. Here we explore how binding specificity is conveyed in PDZ (postsynaptic density protein-95/discs large/zonula occludens-1) domains, small interaction modules that recognize various proteins by binding to an extended C terminus. Our goal was to engineer variants of the Erbin PDZ domain with altered specificity for the most C-terminal position (position 0) where a Val is strongly preferred by the wild-type domain. We constructed a library of PDZ domains by randomizing residues in direct contact with position 0 and in a loop that is close to but does not contact position 0. We used phage display to select for PDZ variants that bind to 19 peptide ligands differing only at position 0. To verify that each obtained PDZ domain exhibited the correct binding specificity, we selected peptide ligands for each domain. Despite intensive efforts, we were only able to evolve Erbin PDZ domain variants with selectivity for the aliphatic C-terminal side chains Val, Ile and Leu. Interestingly, many PDZ domains with these three distinct specificities contained identical amino acids at positions that directly contact position 0 but differed in the loop that does not contact position 0. Computational modeling of the selected PDZ domains shows how slight conformational changes in the loop region propagate to the binding site and result in different binding specificities. Our results demonstrate that second-sphere residues could be crucial in determining protein binding specificity., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

DNA cloning is a basic methodology employed for multiple applications in all life-science disciplines. In order to facilitate DNA cloning we developed Transfer-PCR (TPCR), a novel approach that integrates in a single tube, PCR amplification of the target DNA from an origin vector and its subsequent integration into the destination vector. TPCR can be applied for incorporation of DNA fragments into any desired position within a circular plasmid without the need for purification of the intermediate PCR product and without the use of any commercial kit. TPCR reaction is most efficient within a narrow range of primer concentrations. Adaptation of the TPCR should facilitate, simplify, and significantly reduce time and costs for DNA assembly, as well as protein engineering studies. In the current publication we describe a detailed protocol for implementation of the TPCR method for DNA assembly.

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.

Learning to control, protein-binding specificity is useful for both fundamental and applied biology. In fundamental research, better understanding of complicated signaling networks could be achieved through engineering of regulator proteins to bind to only a subset of their effector proteins. In applied research such as drug design, nonspecific binding remains a major reason for failure of many drug candidates. However, developing antibodies that simultaneously inhibit several disease-associated pathways are a rising trend in pharmaceutical industry. Binding specificity could be manipulated experimentally through various display technologies that allow us to select desired binders from a large pool of candidate protein sequences. We developed an alternative approach for controlling binding specificity based on computational protein design. We can enhance binding specificity of a protein by computationally optimizing its sequence for better interactions with one target and worse interaction with alternative target(s). Moreover, we can design multispecific proteins that simultaneously interact with a predefined set of proteins. Unlike combinatorial techniques, our computational methods for manipulating binding specificity are fast, low cost and in principle are able to consider an unlimited number of desired and undesired binding partners., (Copyright © 2013 Elsevier Inc. All rights reserved.)

DNA cloning and protein engineering are basic methodologies employed for various applications in all life-science disciplines. Manipulations of DNA however, could be a lengthy process that slows down subsequent experiments. To facilitate both DNA cloning and protein engineering, we present Transfer-PCR (TPCR), a novel approach that integrates in a single tube, PCR amplification of the target DNA from an origin vector and its subsequent integration into the destination vector. TPCR can be applied for incorporation of DNA fragments into any desired position within a circular plasmid without the need for purification of the intermediate PCR product and without the use of any commercial kit. Using several examples, we demonstrate the applicability of the TPCR platform for both DNA cloning and for multiple-site targeted mutagenesis. In both cases, we show that the TPCR reaction is most efficient within a narrow range of primer concentrations. In mutagenesis, TPCR is primarily advantageous for generation of combinatorial libraries of targeted mutants but could be also applied to generation of variants with specific multiple mutations throughout the target gene. Adaptation of the TPCR platform should facilitate, simplify and significantly reduce time and costs for diverse protein structure and functional studies., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Computational prediction of stabilizing mutations into monomeric proteins has become an almost ordinary task. Yet, computational stabilization of protein–protein complexes remains a challenge. Design of protein–protein interactions (PPIs) is impeded by the absence of an energy function that could reliably reproduce all favorable interactions between the binding partners. In this work, we present three energy functions: one function that was trained on monomeric proteins, while the other two were optimized by different techniques to predict side-chain conformations in a dataset of PPIs. The performances of these energy functions are evaluated in three different tasks related to design of PPIs: predicting side-chain conformations in PPIs, recovering native binding-interface sequences, and predicting changes in free energy of binding due to mutations. Our findings show that both functions optimized on side-chain repacking in PPIs are more suitable for PPI design compared to the function trained on monomeric proteins. Yet, no function performs best at all three tasks. Comparison of the three energy functions and their performances revealed that (1) burial of polar atoms should not be penalized significantly in PPI design as in single-protein design and (2) contribution of electrostatic interactions should be increased several-fold when switching from single-protein to PPI design. In addition, the use of a softer van der Waals potential is beneficial in cases when backbone flexibility is important. All things considered, we define an energy function that captures most of the nuances of the binding energetics and hence, should be used in future for design of PPIs.

Accumulating evidence shows that many particular proteins have evolved to bind multiple targets, including other proteins, peptides, DNA, and small molecule substrates. Multispecific recognition might be not only common but also necessary for the robustness of signaling and metabolic networks in the cell. It is also important for the immune response and for regulation of transcription and translation. Multispecificity presents an apparent paradox: How can a protein encoded by a single sequence accommodate numerous targets? Analysis of sequences and structures of multispecific proteins revealed a number of mechanisms that achieve multispecificity. Interestingly, similar mechanisms appear in antibody-antigen, T-cell receptor-peptide, protein-DNA, enzyme-substrate, and protein-protein complexes. Directed evolution and protein design experiments with multispecific proteins offer some interesting insights into the evolution of such proteins and help in the dissection of molecular interactions that mediate multispecificity. Understanding the basic principles governing multispecificity could greatly assist in the unraveling of various complex processes in the cell. In addition, through manipulation of functional multispecificity, novel proteins could be created for use in various biotechnological and biomedical applications.

Protein design methods have been originally developed for the design of monomeric proteins. When applied to the more challenging task of protein–protein complex design, these methods yield suboptimal results. In particular, they often fail to recapitulate favorable hydrogen bonds and electrostatic interactions across the interface. In this work, we aim to improve the energy function of the protein design program ORBIT to better account for binding interactions between proteins. By using the advanced machine learning framework of conditional random fields, we optimize the relative importance of all the terms in the energy function, attempting to reproduce the native side-chain conformations in protein–protein interfaces. We evaluate the performance of several optimized energy functions, each describes the van der Waals interactions using a different potential. In comparison with the original energy function, our best energy function (a) incorporates a much “softer” repulsive van der Waals potential, suitable for the discrete rotameric representation of amino acid side chains; (b) does not penalize burial of polar atoms, reflecting the frequent occurrence of polar buried residues in protein–protein interfaces; and (c) significantly up-weights the electrostatic term, attesting to the high importance of these interactions for protein–protein complex formation. Using this energy function considerably improves side chain placement accuracy for interface residues in a large test set of protein–protein complexes. Moreover, the optimized energy function recovers the native sequences of protein–protein interface at a higher rate than the default function and performs substantially better in predicting changes in free energy of binding due to mutations.

Ras is a small GTP-binding protein that is an essential molecular switch for a wide variety of signaling pathways including the control of cell proliferation, cell cycle progression and apoptosis. In the GTP-bound state, Ras can interact with its effectors, triggering various signaling cascades in the cell. In the GDP-bound state, Ras looses its ability to bind to known effectors. The interaction of the GTP-bound Ras (Ras(GTP)) with its effectors has been studied intensively. However, very little is known about the much weaker interaction between the GDP-bound Ras (Ras(GDP)) and Ras effectors. We investigated the factors underlying the nucleotide-dependent differences in Ras interactions with one of its effectors, Raf kinase. Using computational protein design, we generated mutants of the Ras-binding domain of Raf kinase (Raf) that stabilize the complex with Ras(GDP). Most of our designed mutations narrow the gap between the affinity of Raf for Ras(GTP) and Ras(GDP), producing the desired shift in binding specificity towards Ras(GDP). A combination of our best designed mutation, N71R, with another mutation, A85K, yielded a Raf mutant with a 100-fold improvement in affinity towards Ras(GDP). The Raf A85K and Raf N71R/A85K mutants were used to obtain the first high-resolution structures of Ras(GDP) bound to its effector. Surprisingly, these structures reveal that the loop on Ras previously termed the switch I region in the Ras(GDP).Raf mutant complex is found in a conformation similar to that of Ras(GTP) and not Ras(GDP). Moreover, the structures indicate an increased mobility of the switch I region. This greater flexibility compared to the same loop in Ras(GTP) is likely to explain the natural low affinity of Raf and other Ras effectors to Ras(GDP). Our findings demonstrate that an accurate balance between a rigid, high-affinity conformation and conformational flexibility is required to create an efficient and stringent molecular switch., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions.

Predicting mutations that enhance protein-protein affinity remains a challenging task, especially for high-affinity complexes. To test our capability to improve the affinity of such complexes, we studied interaction of acetylcholinesterase with the snake toxin, fasciculin. Using the program ORBIT, we redesigned fasciculin's sequence to enhance its interactions with Torpedo californica acetylcholinesterase. Mutations were predicted in 5 out of 13 interfacial residues on fasciculin, preserving most of the polar inter-molecular contacts seen in the wild-type toxin/enzyme complex. To experimentally characterize fasciculin mutants, we developed an efficient strategy to over-express the toxin in Escherichia coli, followed by refolding to the native conformation. Despite our predictions, a designed quintuple fasciculin mutant displayed reduced affinity for the enzyme. However, removal of a single mutation in the designed sequence produced a quadruple mutant with improved affinity. Moreover, one designed mutation produced 7-fold enhancement in affinity for acetylcholinesterase. This led us to reassess our criteria for enhancing affinity of the toxin for the enzyme. We observed that the change in the predicted inter-molecular energy, rather than in the total energy, correlates well with the change in the experimental free energy of binding, and hence may serve as a criterion for enhancement of affinity in protein-protein complexes.

Calmodulin (CaM) is a ubiquitous second messenger protein that regulates a variety of structurally and functionally diverse targets in response to changes in Ca(2+) concentration. CaM-dependent protein kinase II (CaMKII) and calcineurin (CaN) are the prominent CaM targets that play an opposing role in many cellular functions including synaptic regulation. Since CaMKII and CaN compete for the available Ca(2+)/CaM, the differential affinity of these enzymes for CaM is crucial for achieving a balance in Ca(2+) signaling. We used the computational protein design approach to modify CaM binding specificity for these two targets. Starting from the X-ray structure of CaM in complex with the CaM-binding domain of CaMKII, we optimized CaM interactions with CaMKII by introducing mutations into the CaM sequence. CaM optimization was performed with a protein design program, ORBIT, using a modified energy function that emphasized intermolecular interactions in the sequence selection procedure. Several CaM variants were experimentally constructed and tested for binding to the CaMKII and CaN peptides using the surface plasmon resonance technique. Most of our CaM mutants demonstrated small increase in affinity for the CaMKII peptide and substantial decrease in affinity for the CaN peptide compared to that of wild-type CaM. Our best CaM design exhibited an about 900-fold increase in binding specificity towards the CaMKII peptide, becoming the highest specificity switch achieved in any protein-protein interface through the computational protein design approach. Our results show that computational redesign of protein-protein interfaces becomes a reliable method for altering protein binding affinity and specificity.

Multistate protein design is the task of predicting the amino acid sequence that is best suited to selectively and stably fold to one state out of a set of competing structures. Computationally, it entails solving a challenging optimization problem. Therefore, notwithstanding the increased interest in multistate design, the only implementations reported are based on either genetic algorithms or Monte Carlo methods. The dead-end elimination (DEE) theorem cannot be readily transfered to multistate design problems despite its successful application to single-state protein design. In this article we propose a variant of the standard DEE, called type-dependent DEE. Our method reduces the size of the conformational space of the multistate design problem, while provably preserving the minimal energy conformational assignment for any choice of amino acid sequence. Type-dependent DEE can therefore be used as a preprocessing step in any computational multistate design scheme. We demonstrate the applicability of type-dependent DEE on a set of multistate design problems and discuss its strength and limitations., ((c) 2007 Wiley Periodicals, Inc.)

Changes in synaptic strength that underlie memory formation in the CNS are initiated by pulses of Ca2+ flowing through NMDA-type glutamate receptors into postsynaptic spines. Differences in the duration and size of the pulses determine whether a synapse is potentiated or depressed after repetitive synaptic activity. Calmodulin (CaM) is a major Ca2+ effector protein that binds up to four Ca2+ ions. CaM with bound Ca2+ can activate at least six signaling enzymes in the spine. In fluctuating cytosolic Ca2+, a large fraction of free CaM is bound to fewer than four Ca2+ ions. Binding to targets increases the affinity of CaM's remaining Ca2+-binding sites. Thus, initial binding of CaM to a target may depend on the target's affinity for CaM with only one or two bound Ca2+ ions. To study CaM-dependent signaling in the spine, we designed mutant CaMs that bind Ca2+ only at the two N-terminal or two C-terminal sites by using computationally designed mutations to stabilize the inactivated Ca2+-binding domains in the "closed" Ca2+-free conformation. We have measured their interactions with CaMKII, a major Ca2+/CaM target that mediates initiation of long-term potentiation. We show that CaM with two Ca2+ ions bound in its C-terminal lobe not only binds to CaMKII with low micromolar affinity but also partially activates kinase activity. Our results support the idea that competition for binding of CaM with two bound Ca2+ ions may influence significantly the outcome of local Ca2+ signaling in spines and, perhaps, in other signaling pathways.

Calmodulin (CaM) is a second messenger protein that has evolved to bind tightly to a variety of targets and, as such, exhibits low binding specificity. We redesigned CaM by using a computational protein design algorithm to improve its binding specificity for one of its targets, smooth muscle myosin light chain kinase (smMLCK). Residues in or near the CaM/smMLCK binding interface were optimized; CaM interactions with alternative targets were not directly considered in the optimization. The predicted CaM sequences were constructed and tested for binding to a set of eight targets including smMLCK. The best CaM variant, obtained from a calculation that emphasized intermolecular interactions, showed up to a 155-fold increase in binding specificity. The increase in binding specificity was not due to improved binding to smMLCK, but due to decreased binding to the alternative targets. This finding is consistent with the fact that the sequence of wild-type CaM is nearly optimal for interactions with numerous targets.

We report the computational redesign of the protein-binding interface of calmodulin (CaM), a small, ubiquitous Ca(2+)-binding protein that is known to bind to and regulate a variety of functionally and structurally diverse proteins. The CaM binding interface was optimized to improve binding specificity towards one of its natural targets, smooth muscle myosin light chain kinase (smMLCK). The optimization was performed using optimization of rotamers by iterative techniques (ORBIT), a protein design program that utilizes a physically based force-field and the Dead-End Elimination theorem to compute sequences that are optimal for a given protein scaffold. Starting from the structure of the CaM-smMLCK complex, the program considered 10(22) amino acid residue sequences to obtain the lowest-energy CaM sequence. The resulting eightfold mutant, CaM_8, was constructed and tested for binding to a set of seven CaM target peptides. CaM_8 displayed high binding affinity to the smMLCK peptide (1.3nM), similar to that of the wild-type protein (1.8nM). The affinity of CaM_8 to six other target peptides was reduced, as intended, by 1.5-fold to 86-fold. Hence, CaM_8 exhibited increased binding specificity, preferring the smMLCK peptide to the other targets. Studies of this type may increase our understanding of the origins of binding specificity in protein-ligand complexes and may provide valuable information that can be used in the design of novel protein receptors and/or ligands.

The effects of various mechanisms of metalloporphyrin reduction potential modulation were investigated experimentally using a robust, well-characterized heme protein maquette, synthetic protein scaffold H10A24 [¿CH(3)()CONH-CGGGELWKL.HEELLKK.FEELLKL.AEERLKK. L-CONH(2)()¿(2)](2). Removal of the iron porphyrin macrocycle from the high dielectric aqueous environment and sequestration within the hydrophobic core of the H10A24 maquette raises the equilibrium reduction midpoint potential by 36-138 mV depending on the hydrophobicity of the metalloporphyrin structure. By incorporating various natural and synthetic metalloporphyrins into a single protein scaffold, we demonstrate a 300-mV range in reduction potential modulation due to the electron-donating/withdrawing character of the peripheral macrocycle substituents. Solution pH is used to modulate the metalloporphyrin reduction potential by 160 mV, regardless of the macrocycle architecture, by controlling the protonation state of the glutamate involved in partial charge compensation of the ferric heme. Attempts to control the reduction potential by inserting charged amino acids into the hydrophobic core at close proximity to the metalloporphyrin lead to varied success, with H10A24-L13E lowering the E(m8.5) by 40 mV, H10A24-E11Q raising it by 50 mV, and H10A24-L13R remaining surprisingly unaltered. Modifying the charge of the adjacent metalloporphyrin, +1 for iron(III) protoporphyrin IX or neutral for zinc(II) protoporphyrin IX resulted in a loss of 70 mV [Fe(III)PPIX](+) - [Fe(III)PPIX](+) interaction observed in maquettes. Using these factors in combination, we illustrate a 435-mV variation of the metalloporphyrin reduction midpoint potential in a simple heme maquette relative to the about 800-mV range observed for natural cytochromes. Comparison between the reduction potentials of the heme maquettes and other de novo designed heme proteins reveals global trends in the E(m) values of synthetic cytochromes.

We have taken a computational approach to design mutations that stabilize a large protein domain of approximately 200 residues in two alternative conformations. Mutations in the hydrophobic core of the alphaMbeta2 integrin I domain were designed to stabilize the crystallographically defined open or closed conformers. When expressed on the cell surface as part of the intact heterodimeric receptor, binding of the designed open and closed I domains to the ligand iC3b, a form of the complement component C3, was either increased or decreased, respectively, compared to wild type. Moreover, when expressed in isolation from other integrin domains using an artificial transmembrane domain, designed open I domains were active in ligand binding, whereas designed closed and wild type I domains were inactive. Comparison to a human expert designed open mutant showed that the computationally designed mutants are far more active. Thus, computational design can be used to stabilize a molecule in a desired conformation, and conformational change in the I domain is physiologically relevant to regulation of ligand binding.

Proton exchange with aqueous media coupled to heme oxidation/reduction is commonly seen but not understood in natural cytochromes. Our synthetic tetrahelix bundle heme protein maquettes successfully reproduce natural proton coupling to heme oxidation/reduction. Potentiometry reveals major pK shifts from 4.2 to 7.0 and from 9.4 to 10.3 in the maquette-associated acid/base group(s) upon heme reduction. Consequently, a 210 mV decrease in the heme redox potential is observed between the two extremes of pH. Potentiometry with resonance Raman and FTIR spectroscopy performed over a wide pH range strongly implicates glutamate side chains as the source of proton coupling below pH 8.0, whereas lysine side chains are suggested above pH 8.0. Remarkably, the pK values of several glutamates in the maquette are elevated from their solution value (4.4) to values as high as 7.0. It is suggested that these glutamates are recruited into the interior of the bundle as part of a structural rearrangement that occurs upon heme binding. Glutamate to glutamine variants of the prototype protein demonstrate that removal of the glutamate closest to the heme diminishes but does not abolish proton exchange. It is necessary to remove additional glutamates before pH-independent heme oxidation/reduction profiles are achieved. The mechanism of redox-linked proton coupling appears to be rooted in distributed partial charge compensation, the magnitude of which is governed by the dielectric distance between the ferric heme and acid/base side chains. A similar mechanism is likely to exist in native redox proteins which undergo charge change upon cofactor oxidation/reduction.