Combinatorial protein engineering of proteolytically resistant mesotrypsin inhibitors as candidates for cancer therapy

Serine protease inhibitors decrease metastasis in prostate, breast, and ovarian cancers

Targeted therapies for prostate, breast, and ovarian cancers are based on their activity against primary tumors rather than their anti-metastatic activity. Consequently, there is an urgent need for new agents targeting the metastatic process. Emerging evidence correlates in vitro and in vivo cancer invasion and metastasis with increased activity of the proteases mesotrypsin (prostate and breast cancer) and kallikrein 6 (KLK6; ovarian cancer). Thus, mesotrypsin and KLK6 are attractive putative targets for therapeutic intervention. As potential therapeutics for advanced metastatic prostate, breast, and ovarian cancers, we report novel mesotrypsin- and KLK6-based therapies, based on our previously developed mutants of the human amyloid β-protein precursor Kunitz protease inhibitor domain (APPI). These mutants, designated APPI-3M (prostate and breast cancer) and APPI-4M (ovarian cancer), demonstrated significant accumulation in tumors and therapeutic efficacy in orthotopic preclinical models, with the advantages of long retention times in vivo, high affinity and favorable pharmacokinetic properties. The applicability of the APPIs, as a novel therapy and for imaging purposes, is supported by their good safety profile and their controlled and scalable manufacturability in bioreactors.

Non-Immunoglobulin Synthetic Binding Proteins for Oncology

Extensive application of technologies like phage display in screening peptide and protein combinatorial libraries has not only facilitated creation of new recombinant antibodies but has also significantly enriched repertoire of the protein binders that have polypeptide scaffolds without homology to immunoglobulins. These innovative synthetic binding protein (SBP) platforms have grown in number and now encompass monobodies/adnectins, DARPins, lipocalins/anticalins, and a variety of miniproteins such as affibodies and knottins, among others. They serve as versatile modules for developing complex affinity tools that hold promise in both diagnostic and therapeutic settings. An optimal scaffold typically has low molecular weight, minimal immunogenicity, and demonstrates resistance against various challenging conditions, including proteolysis - making it potentially suitable for peroral administration. Retaining functionality under reducing intracellular milieu is also advantageous. However, paramount to its functionality is the scaffold's ability to tolerate mutations across numerous positions, allowing for the formation of a sufficiently large target binding region. This is achieved through the library construction, screening, and subsequent expression in an appropriate system. Scaffolds that exhibit high thermodynamic stability are especially coveted by the developers of new SBPs. These are steadily making their way into clinical settings, notably as antagonists of oncoproteins in signaling pathways. This review surveys the diverse landscape of SBPs, placing particular emphasis on the inhibitors targeting the oncoprotein KRAS, and highlights groundbreaking opportunities for SBPs in oncology.

Avidity observed between a bivalent inhibitor and an enzyme monomer with a single active site

Although myriad protein–protein interactions in nature use polyvalent binding, in which multiple ligands on one entity bind to multiple receptors on another, to date an affinity advantage of polyvalent binding has been demonstrated experimentally only in cases where the target receptor molecules are clustered prior to complex formation. Here, we demonstrate cooperativity in binding affinity (i.e., avidity) for a protein complex in which an engineered dimer of the amyloid precursor protein inhibitor (APPI), possessing two fully functional inhibitory loops, interacts with mesotrypsin, a soluble monomeric protein that does not self-associate or cluster spontaneously. We found that each inhibitory loop of the purified APPI homodimer was over three-fold more potent than the corresponding loop in the monovalent APPI inhibitor. This observation is consistent with a suggested mechanism whereby the two APPI loops in the homodimer simultaneously and reversibly bind two corresponding mesotrypsin monomers to mediate mesotrypsin dimerization. We propose a simple model for such dimerization that quantitatively explains the observed cooperativity in binding affinity. Binding cooperativity in this system reveals that the valency of ligands may affect avidity in protein–protein interactions including those of targets that are not surface-anchored and do not self-associate spontaneously. In this scenario, avidity may be explained by the enhanced concentration of ligand binding sites in proximity to the monomeric target, which may favor rebinding of the multiple ligand binding sites with the receptor molecules upon dissociation of the protein complex.

Potent Inhibitor of Human Trypsins from the Aeruginosin Family of Natural Products

Serine proteases regulate many physiological processes and play a key role in a variety of cancers. Aeruginosins are a family of natural products produced by cyanobacteria that exhibit pronounced structural diversity and potent serine protease inhibition. Here, we sequenced the complete genome of Nodularia sphaerocarpa UHCC 0038 and identified the 43.7 kb suomilide biosynthetic gene cluster. Bioinformatic analysis demonstrated that suomilide belongs to the aeruginosin family of natural products. We identified 103 complete aeruginosin biosynthetic gene clusters from 12 cyanobacterial genera and showed that they encode an unexpected chemical diversity. Surprisingly, purified suomilide inhibited human trypsin-2 and -3, with IC₅₀ values of 4.7 and 11.5 nM, respectively, while trypsin-1 was inhibited with an IC₅₀ of 104 nM. Molecular dynamics simulations suggested that suomilide has a long residence time when bound to trypsins. This was confirmed experimentally for trypsin-1 and -3 (residence times of 1.5 and 57 min, respectively). Suomilide also inhibited the invasion of aggressive and metastatic PC-3M prostate cancer cells without affecting cell proliferation. The potent inhibition of trypsin-3, together with a long residence time and the ability to inhibit prostate cancer cell invasion, makes suomilide an attractive drug lead for targeting cancers that overexpress trypsin-3. These results substantially broaden the genetic and chemical diversity of the aeruginosin family and suggest that aeruginosins may be a source of selective inhibitors of human serine proteases.

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

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

High Expression of PRSS3 Indicates Unfavorable Clinical Outcomes in Colon Adenocarcinoma

INTRODUCTION

Serine proteases have been implicated as key drivers and facilitators of cancer malignancy. Protease, serine, 3 (PRSS3), which belongs to the serine proteases family, is reported to be abundantly expressed in a variety of types of tumor and contributes to the initiation and development of cancers. However, the clinical role of PRSS3 in colon adenocarcinoma (CAC) was not clarified yet. In the present study, we explored the potential effect of PRSS3 in CAC and whether it is related to the poor survival of CAC patients.

MATERIALS AND METHODS

The mRNA and protein levels of PRSS3 were examined in CAC samples and connective noncancerous colon samples through quantitative real-time polymerase chain reaction assay and immunohistochemistry staining. Univariate and multivariate analyses were performed to estimate the prognostic role of PRSS3 in enrolled CAC patients.

RESULTS

PRSS3 expression in CAC samples was significantly increased compared with connective noncancerous samples. Moreover, a higher level of PRSS3 was found to be correlated with the larger tumor size, advanced T stage, and positive lymph node metastasis. In addition, PRSS3 was also defined as an unfavorable prognosis factor for CAC patients.

CONCLUSIONS

High expression of PRSS3 was significantly related to the unfavorable clinical features and poor prognosis in CAC patients. It suggested that PRSS3 might serve as a novel prognostic indicator and potential drug target for CAC treatment.

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

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

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

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

Surface loops of trypsin-like serine proteases as determinants of function

Trypsin and chymotrypsin-like serine proteases from family S1 (clan PA) constitute the largest protease group in humans and more generally in vertebrates. The prototypes chymotrypsin, trypsin and elastase represent simple digestive proteases in the gut, where they cleave nearly any protein. Multidomain trypsin-like proteases are key players in the tightly controlled blood coagulation and complement systems, as well as related proteases that are secreted from diverse immune cells. Some serine proteases are expressed in nearly all tissues and fluids of the human body, such as the human kallikreins and kallikrein-related peptidases with specialization for often unique substrates and accurate timing of activity. HtrA and membrane-anchored serine proteases fulfill important physiological tasks with emerging roles in cancer. The high diversity of all family members, which share the tandem β-barrel architecture of the chymotrypsin-fold in the catalytic domain, is conferred by the large differences of eight surface loops, surrounding the active site. The length of these loops alters with insertions and deletions, resulting in remarkably different three-dimensional arrangements. In addition, metal binding sites for Na+, Ca2+ and Zn2+ serve as regulatory elements, as do N-glycosylation sites. Depending on the individual tasks of the protease, the surface loops determine substrate specificity, control the turnover and allow regulation of activation, activity and degradation by other proteins, which are often serine proteases themselves. Most intriguingly, in some serine proteases, the surface loops interact as allosteric network, partially tuned by protein co-factors. Knowledge of these subtle and complicated molecular motions may allow nowadays for new and specific pharmaceutical or medical approaches.

An Engineered Variant of the B1 Domain of Protein G Suppresses the Aggregation and Toxicity of Intra- and Extracellular Aβ42

Intra- and extraneuronal deposition of amyloid β (Aβ) peptides have been linked to Alzheimer's disease (AD). While both intra- and extraneuronal Aβ deposits affect neuronal cell viability, the molecular mechanism by which these Aβ structures, especially when intraneuronal, do so is still not entirely understood. This makes the development of inhibitors challenging. To prevent the formation of toxic Aβ structural assemblies so as to prevent neuronal cell death associated with AD, we used a combination of computational and combinatorial-directed evolution approaches to develop a variant of the HTB1 protein (HTB1_M2). HTB1_M2 inhibits in vitro self-assembly of Aβ42 peptide and shifts the Aβ42 aggregation pathway to the formation of oligomers that are nontoxic to neuroblastoma SH-SY5Y cells overexpressing or treated with Aβ42 peptide. This makes HTB1_M2 a potential therapeutic lead in the development of AD-targeted drugs and a tool for elucidating conformational changes in the Aβ42 peptide.

PRSS3/Mesotrypsin and kallikrein-related peptidase 5 are associated with poor prognosis and contribute to tumor cell invasion and growth in lung adenocarcinoma

Serine proteases have been implicated as key drivers and facilitators of lung cancer malignancy, and while these proteins represent straightforward targets for therapeutic inhibitors, identification of optimal points for intervention has been complicated by the complex networks in which these enzymes function. Here we implicate a signaling pathway consisting of PRSS3/mesotrypsin and kallikrein-related peptidase 5 (KLK5) in lung adenocarcinoma malignancy. We show that elevated PRSS3/mesotrypsin expression is prognostic for poor outcome for patients with lung adenocarcinoma, and that genetic or pharmacologic targeting of PRSS3/mesotrypsin reduces lung adenocarcinoma cell invasiveness and proliferation. We further show that genetic targeting of KLK5, a known target of PRSS3/mesotrypsin, phenocopies the effect of PRSS3/mesotrypsin knockdown, and also that elevated expression of KLK5 is similarly prognostic for outcome in lung adenocarcinoma. Finally, we use transcriptional profiling experiments to show that PRSS3/mesotrypsin and KLK5 control a common malignancy-promoting pathway. These experiments implicate a potential PRSS3/mesotrypsin-KLK5 signaling module in lung adenocarcinoma and reveal the potential therapeutic benefit of selectively targeting these pathways.

Disulfide engineering of human Kunitz-type serine protease inhibitors enhances proteolytic stability and target affinity toward mesotrypsin

Serine protease inhibitors of the Kunitz-bovine pancreatic trypsin inhibitor (BPTI) family are ubiquitous biological regulators of proteolysis. These small proteins are resistant to proteolysis, but can be slowly cleaved within the protease-binding loop by target proteases, thereby compromising their activity. For the human protease mesotrypsin, this cleavage is especially rapid. Here, we aimed to stabilize the Kunitz domain structure against proteolysis through disulfide engineering. Substitution within the Kunitz inhibitor domain of the amyloid precursor protein (APPI) that incorporated a new disulfide bond between residues 17 and 34 reduced proteolysis by mesotrypsin 74-fold. Similar disulfide engineering of tissue factor pathway inhibitor-1 Kunitz domain 1 (^KD1TFPI1) and bikunin Kunitz domain 2 (^KD2bikunin) likewise stabilized these inhibitors against mesotrypsin proteolysis 17- and 6.6-fold, respectively. Crystal structures of disulfide-engineered APPI and ^KD1TFPI1 variants in a complex with mesotrypsin at 1.5 and 2.0 Å resolution, respectively, confirmed the formation of well-ordered disulfide bonds positioned to stabilize the binding loop. Long all-atom molecular dynamics simulations of disulfide-engineered Kunitz domains and their complexes with mesotrypsin revealed conformational stabilization of the primed side of the inhibitor-binding loop by the engineered disulfide, along with global suppression of conformational dynamics in the Kunitz domain. Our findings suggest that the Cys-17-Cys-34 disulfide slows proteolysis by dampening conformational fluctuations in the binding loop and minimizing motion at the enzyme-inhibitor interface. The generalizable approach developed here for the stabilization against proteolysis of Kunitz domains, which can serve as important scaffolds for therapeutics, may thus find applications in drug development.

Proteases: Pivot Points in Functional Proteomics

Proteases drive the life cycle of all proteins, ensuring the transportation and activation of newly minted, would-be proteins into their functional form while recycling spent or unneeded proteins. Far from their image as engines of protein digestion, proteases play fundamental roles in basic physiology and regulation at multiple levels of systems biology. Proteases are intimately associated with disease and modulation of proteolytic activity is the presumed target for successful therapeutics. "Proteases: Pivot Points in Functional Proteomics" examines the crucial roles of proteolysis across a wide range of physiological processes and diseases. The existing and potential impacts of proteolysis-related activity on drug and biomarker development are presented in detail. All told the decisive roles of proteases in four major categories comprising 23 separate subcategories are addressed. Within this construct, 15 sets of subject-specific, tabulated data are presented that include identification of proteases, protease inhibitors, substrates, and their actions. Said data are derived from and confirmed by over 300 references. Cross comparison of datasets indicates that proteases, their inhibitors/promoters and substrates intersect over a range of physiological processes and diseases, both chronic and pathogenic. Indeed, "Proteases: Pivot Points …" closes by dramatizing this very point through association of (pro)Thrombin and Fibrin(ogen) with: hemostasis, innate immunity, cardiovascular and metabolic disease, cancer, neurodegeneration, and bacterial self-defense.

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

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

Epitope-specific affinity maturation improved stability of potent protease inhibitory antibodies

Targeting effectual epitopes is essential for therapeutic antibodies to accomplish their desired biological functions. This study developed a competitive dual color fluorescence-activated cell sorting (FACS) to maturate a matrix metalloprotease 14 (MMP-14) inhibitory antibody. Epitope-specific screening was achieved by selection on MMP-14 during competition with N-terminal domain of tissue inhibitor of metalloproteinase-2 (TIMP-2) (nTIMP-2), a native inhibitor of MMP-14 binding strongly to its catalytic cleft. 3A2 variants with high potency, selectivity, and improved affinity and proteolytic stability were isolated from a random mutagenesis library. Binding kinetics indicated that the affinity improvements were mainly from slower dissociation rates. In vitro degradation tests suggested the isolated variants had half lives 6-11-fold longer than the wt. Inhibition kinetics suggested they were competitive inhibitors which showed excellent selectivity toward MMP-14 over highly homologous MMP-9. Alanine scanning revealed that they bound to the vicinity of MMP-14 catalytic cleft especially residues F204 and F260, suggesting that the desired epitope was maintained during maturation. When converted to immunoglobulin G, B3 showed 5.0 nM binding affinity and 6.5 nM inhibition potency with in vivo half-life of 4.6 days in mice. In addition to protease inhibitory antibodies, the competitive FACS described here can be applied for discovery and engineering biosimilars, and in general for other circumstances where epitope-specific modulation is needed.

Combinatorial engineering of N-TIMP2 variants that selectively inhibit MMP9 and MMP14 function in the cell

Developing selective inhibitors for proteolytic enzymes that share high sequence homology and structural similarity is important for achieving high target affinity and functional specificity. Here, we used a combination of yeast surface display and dual-color selective library screening to obtain selective inhibitors for each of the matrix metalloproteinases (MMPs) MMP14 and MMP9 by modifying the non-specific N-terminal domain of the tissue inhibitor of metalloproteinase-2 (N-TIMP2). We generated inhibitor variants with 30- to 1175-fold improved specificity to each of the proteases, respectively, relative to wild type N-TIMP2. These biochemical results accurately predicted the selectivity and specificity obtained in cell-based assays. In U87MG cells, the activation of MMP2 by MMP14 was inhibited by MMP14-selective blockers but not MMP9-specific inhibitors. Target specificity was also demonstrated in MCF-7 cells stably expressing either MMP14 or MMP9, with only the MMP14-specific inhibitors preventing the mobility of MMP14-expressing cells. Similarly, the mobility of MMP9-expressing cells was inhibited by the MMP9-specific inhibitors, yet was not altered by the MMP14-specific inhibitors. The strategy developed in this study for improving the specificity of an otherwise broad-spectrum inhibitor will likely enhance our understanding of the basis for target specificity of inhibitors to proteolytic enzymes, in general, and to MMPs, in particular. We, moreover, envision that this study could serve as a platform for the development of next-generation, target-specific therapeutic agents. Finally, our methodology can be extended to other classes of proteolytic enzymes and other important target proteins.

A potent, proteolysis-resistant inhibitor of kallikrein-related peptidase 6 (KLK6) for cancer therapy, developed by combinatorial engineering

Human tissue kallikrein (KLK) proteases are hormone-like signaling molecules with important functions in cancer pathophysiology. KLK-related peptidase 6 (KLK6), specifically, is highly up-regulated in several types of cancer, where its increased activity promotes cancer invasion and metastasis. This characteristic suggests KLK6 as an attractive target for therapeutic interventions. However, inhibitors that specifically target KLK6 have not yet been reported, possibly because KLK6 shares a high sequence homology and structural similarity with other serine proteases and resists inhibition by many polypeptide inhibitors. Here, we present an innovative combinatorial approach to engineering KLK6 inhibitors via flow cytometry-based screening of a yeast-displayed mutant library of the human amyloid precursor protein Kunitz protease inhibitor domain (APPI), an inhibitor of other serine proteases, such as anionic and cationic trypsins. On the basis of this screening, we generated APPI_{M17L,I18F,S19F,F34V} (APPI-4M), an APPI variant with a KLK6 inhibition constant (K_i ) of 160 pm and a turnover time of 10 days. To the best of our knowledge, APPI-4M is the most potent KLK6 inhibitor reported to date, displaying 146-fold improved affinity and 13-fold improved proteolytic stability compared with WT APPI (APPI_WT). We further demonstrate that APPI-4M acts as a functional inhibitor in a cell-based model of KLK6-dependent breast cancer invasion. Finally, the crystal structures of the APPI_WT/KLK6 and APPI-4M/KLK6 complexes revealed the structural and mechanistic bases for the improved KLK6 binding and proteolytic resistance of APPI-4M. We anticipate that APPI-4M will have substantial translational potential as both imaging agent and therapeutic.

Engineering potent mesotrypsin inhibitors based on the plant-derived cyclic peptide, sunflower trypsin inhibitor-1

Plants produce a diverse range of peptides and proteins that inhibit the activity of different serine proteases. The value of these inhibitors not only stems from their native role(s) in planta, but they are also regarded as promising templates for inhibitor engineering. Interest in this field has grown rapidly in recent years, particularly for therapeutic applications. The serine protease mesotrypsin has been implicated in several cancers, but is a challenging target for inhibitor engineering as a number of serine protease inhibitors that typically display broad-range activity show limited activity against mesotrypsin. In this study, we use a cyclic peptide isolated from sunflower seeds, sunflower trypsin inhibitor-1 (SFTI-1), as a scaffold for engineering potent mesotrypsin inhibitors. SFTI-1 comprises 14-amino acids and is a potent inhibitor of human cationic trypsin (K_i = 30 ± 0.8 pM) but shows 165,000-fold weaker activity against mesotrypsin (K_i = 4.96 ± 0.2 μM). Using an inhibitor library based on SFTI-1, we show that the inhibitor's P2' residue (Ile) is a key contributor to SFTI-1's limited activity against mesotrypsin. Substituting P2' Ile with chemically diverse amino acids, including non-canonical aromatic residues, produced new inhibitor variants that maintained a similar structure to SFTI-1 and showed marked improvements in activity (exceeding 100-fold). An assessment of the activity of the new inhibitors against closely-related trypsin paralogs revealed that the improved activity against mesotrypsin was accompanied by a loss in activity against off-target proteases, such that several engineered variants showed comparable activity against mesotrypsin and human cationic trypsin. Together, these findings identify potent mesotrypsin inhibitors that are suitable for further optimisation studies and demonstrate the potential gains in activity and selectivity that can be achieved by optimising the P2' residue, particularly for engineered SFTI-based inhibitors.

Pre-equilibrium competitive library screening for tuning inhibitor association rate and specificity toward serine proteases

High structural and sequence similarity within protein families can pose significant challenges to the development of selective inhibitors, especially toward proteolytic enzymes. Such enzymes usually belong to large families of closely similar proteases and may also hydrolyze, with different rates, protein- or peptide-based inhibitors. To address this challenge, we employed a combinatorial yeast surface display library approach complemented with a novel pre-equilibrium, competitive screening strategy for facile assessment of the effects of multiple mutations on inhibitor association rates and binding specificity. As a proof of principle for this combined approach, we utilized this strategy to alter inhibitor/protease association rates and to tailor the selectivity of the amyloid β-protein precursor Kunitz protease inhibitor domain (APPI) for inhibition of the oncogenic protease mesotrypsin, in the presence of three competing serine proteases, anionic trypsin, cationic trypsin and kallikrein-6. We generated a variant, designated APPI_{P13W/M17G/I18F/F34V}, with up to 30-fold greater specificity relative to the parental APPI_{M17G/I18F/F34V} protein, and 6500- to 230 000-fold improved specificity relative to the wild-type APPI protein in the presence of the other proteases tested. A series of molecular docking simulations suggested a mechanism of interaction that supported the biochemical results. These simulations predicted that the selectivity and specificity are affected by the interaction of the mutated APPI residues with nonconserved enzyme residues located in or near the binding site. Our strategy will facilitate a better understanding of the binding landscape of multispecific proteins and will pave the way for design of new drugs and diagnostic tools targeting proteases and other proteins.

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.

A computational combinatorial approach identifies a protein inhibitor of superoxide dismutase 1 misfolding, aggregation, and cytotoxicity

Molecular agents that specifically bind and neutralize misfolded and toxic superoxide dismutase 1 (SOD1) mutant proteins may find application in attenuating the disease progression of familial amyotrophic lateral sclerosis. However, high structural similarities between the wild-type and mutant SOD1 proteins limit the utility of this approach. Here we addressed this challenge by converting a promiscuous natural human IgG-binding domain, the hyperthermophilic variant of protein G (HTB1), into a highly specific aggregation inhibitor (designated HTB1_M) of two familial amyotrophic lateral sclerosis-linked SOD1 mutants, SOD1^G93A and SOD1^G85R We utilized a computational algorithm for mapping protein surfaces predisposed to HTB1 intermolecular interactions to construct a focused HTB1 library, complemented with an experimental platform based on yeast surface display for affinity and specificity screening. HTB1_M displayed high binding specificity toward SOD1 mutants, inhibited their amyloid aggregation in vitro, prevented the accumulation of misfolded proteins in living cells, and reduced the cytotoxicity of SOD1^G93A expressed in motor neuron-like cells. Competition assays and molecular docking simulations suggested that HTB1_M binds to SOD1 via both its α-helical and β-sheet domains at the native dimer interface that becomes exposed upon mutated SOD1 misfolding and monomerization. Our results demonstrate the utility of computational mapping of the protein-protein interaction potential for designing focused protein libraries to be used in directed evolution. They also provide new insight into the mechanism of conversion of broad-spectrum immunoglobulin-binding proteins, such as HTB1, into target-specific proteins, thereby paving the way for the development of new selective drugs targeting the amyloidogenic proteins implicated in a variety of human diseases.

Small molecule inhibitors of mesotrypsin from a structure-based docking screen

PRSS3/mesotrypsin is an atypical isoform of trypsin, the upregulation of which has been implicated in promoting tumor progression. To date there are no mesotrypsin-selective pharmacological inhibitors which could serve as tools for deciphering the pathological role of this enzyme, and could potentially form the basis for novel therapeutic strategies targeting mesotrypsin. A virtual screen of the Natural Product Database (NPD) and Food and Drug Administration (FDA) approved Drug Database was conducted by high-throughput molecular docking utilizing crystal structures of mesotrypsin. Twelve high-scoring compounds were selected for testing based on lowest free energy docking scores, interaction with key mesotrypsin active site residues, and commercial availability. Diminazene (CID22956468), along with two similar compounds presenting the bis-benzamidine substructure, was validated as a competitive inhibitor of mesotrypsin and other human trypsin isoforms. Diminazene is the most potent small molecule inhibitor of mesotrypsin reported to date with an inhibitory constant (Ki) of 3.6±0.3 μM. Diminazene was subsequently co-crystalized with mesotrypsin and the crystal structure was solved and refined to 1.25 Å resolution. This high resolution crystal structure can now offer a foundation for structure-guided efforts to develop novel and potentially more selective mesotrypsin inhibitors based on similar molecular substructures.

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

The molecular basis of enzyme catalytic power and specificity derives from dynamic interactions between enzyme and substrate during catalysis. Although considerable effort has been devoted to understanding how conformational dynamics within enzymes affect catalysis, the role of conformational dynamics within protein substrates has not been addressed. Here, we examine the importance of substrate dynamics in the cleavage of Kunitz-bovine pancreatic trypsin inhibitor protease inhibitors by mesotrypsin, finding that the varied conformational dynamics of structurally similar substrates can profoundly impact the rate of catalysis. A 1.4-Å crystal structure of a mesotrypsin-product complex formed with a rapidly cleaved substrate reveals a dramatic conformational change in the substrate upon proteolysis. By using long all-atom molecular dynamics simulations of acyl-enzyme intermediates with proteolysis rates spanning 3 orders of magnitude, we identify global and local dynamic features of substrates on the nanosecond-microsecond time scale that correlate with enzymatic rates and explain differential susceptibility to proteolysis. By integrating multiple enhanced sampling methods for molecular dynamics, we model a viable conformational pathway between substrate-like and product-like states, linking substrate dynamics on the nanosecond-microsecond time scale with large collective substrate motions on the much slower time scale of catalysis. Our findings implicate substrate flexibility as a critical determinant of catalysis.

A Camelid-derived Antibody Fragment Targeting the Active Site of a Serine Protease Balances between Inhibitor and Substrate Behavior

A peptide segment that binds the active site of a serine protease in a substrate-like manner may behave like an inhibitor or a substrate. However, there is sparse information on which factors determine the behavior a particular peptide segment will exhibit. Here, we describe the first x-ray crystal structure of a nanobody in complex with a serine protease. The nanobody displays a new type of interaction between an antibody and a serine protease as it inserts its complementary determining region-H3 loop into the active site of the protease in a substrate-like manner. The unique binding mechanism causes the nanobody to behave as a strong inhibitor as well as a poor substrate. Intriguingly, its substrate behavior is incomplete, as 30-40% of the nanobody remained intact and inhibitory after prolonged incubation with the protease. Biochemical analysis reveals that an intra-loop interaction network within the complementary determining region-H3 of the nanobody balances its inhibitor versus substrate behavior. Collectively, our results unveil molecular factors, which may be a general mechanism to determine the substrate versus inhibitor behavior of other protease inhibitors.