Deep profiling of protease substrate specificity enabled by dual random and scanned human proteome substrate phage libraries

High-Resolution Substrate Specificity Profiling of SARS-CoV-2 Mpro; Comparison to SARS-CoV Mpro

The SARS-CoV-2 Mpro protease from COVID-19 cleaves the pp1a and pp2b polyproteins at 11 sites during viral maturation and is the target of Nirmatrelvir, one of the two components of the frontline treatment sold as Paxlovid. We used the YESS 2.0 platform, combining protease and substrate expression in the yeast endoplasmic reticulum with fluorescence-activated cell sorting and next-generation sequencing, to carry out the high-resolution substrate specificity profiling of SARS-CoV-2 Mpro as well as the related SARS-CoV Mpro from SARS 2003. Even at such a high level of resolution, the substrate specificity profiles of both enzymes are essentially identical. The population of cleaved substrates isolated in our sorts is so deep, the relative catalytic efficiencies of the different cleavage sites on the SARS-CoV-2 polyproteins pp1a and pp2b are qualitatively predicted. These results not only demonstrated the precise and reproducible nature of the YESS 2.0/NGS approach to protease substrate specificity profiling but also should be useful in the design of next generation SARS-CoV-2 Mpro inhibitors, and by analogy, SARS-CoV Mpro inhibitors as well.

Synthetic protein protease sensor platform

Introduction: Protease activity can serve as a highly specific biomarker for application in health, biotech, and beyond. The aim of this study was to develop a protease cleavable synthetic protein platform to detect protease activity in a rapid cell-free setting. Methods: The protease sensor is modular, with orthogonal peptide tags at the N and C terminal ends, which can be uncoupled via a protease responsive module located in between. The sensor design allows for several different readouts of cleavage signal. A protein 'backbone' [Green fluorescent protein (GFP)] was designed in silico to have both a C-terminal Flag-tag and N-Terminal 6x histidine tag (HIS) for antibody detection. A protease cleavage site, which can be adapted for any known protease cleavage sequence, enables the uncoupling of the peptide tags. Three different proteases-Tobacco, Etch Virus (TEV), the main protease from coronavirus SARS-COV-2 (Mpro) and Matrix Metallopeptidase 9 (MMP9)-a cancer-selective human protease-were examined. A sandwich Enzyme-Linked Immunosorbent Assay (ELISA) was developed based on antibodies against the HIS and Flag tags. As an alternative readout, a C-terminal quencher peptide separable by protease cleavage from the GFP was also included. Purified proteins were deployed in cell-free cleavage assays with their respective protease. Western blots, fluorescence assays and immunoassay were performed on samples. Results: Following the design, build and validation of protein constructs, specific protease cleavage was initially demonstrated by Western blot. The novel ELISA proved to afford highly sensitive detection of protease activity in all cases. By way of alternative readout, activation of fluorescence signal upon protease cleavage was also demonstrated but did not match the sensitivity provided by the ELISA method. Discussion: This platform, comprising a protease-responsive synthetic protein device and accompanying readout, is suitable for future deployment in a rapid, low-cost, lateral flow setting. The modular protein device can readily accommodate any desired protease-response module (target protease cleavage site). This study validates the concept with three disparate proteases and applications-human infectious disease, cancer and agricultural crop infection.

Coordination chemistry suggests that independently observed benefits of metformin and Zn2+ against COVID-19 are not independent

Independent trials indicate that either oral Zn2+ or metformin can separately improve COVID-19 outcomes by approximately 40%. Coordination chemistry predicts a mechanistic relationship and therapeutic synergy. Zn2+ deficit is a known risk factor for both COVID-19 and non-infectious inflammation. Most dietary Zn2+ is not absorbed. Metformin is a naked ligand that presumably increases intestinal Zn2+ bioavailability and active absorption by cation transporters known to transport metformin. Intracellular Zn2+ provides a natural buffer of many protease reactions; the variable "set point" is determined by Zn2+ regulation or availability. A Zn2+-interactive protease network is suggested here. The two viral cysteine proteases are therapeutic targets against COVID-19. Viral and many host proteases are submaximally inhibited by exchangeable cell Zn2+. Inhibition of cysteine proteases can improve COVID-19 outcomes and non-infectious inflammation. Metformin reportedly enhances the natural moderating effect of Zn2+ on bioassayed proteome degradation. Firstly, the dissociable metformin-Zn2+ complex could be actively transported by intestinal cation transporters; thereby creating artificial pathways of absorption and increased body Zn2+ content. Secondly, metformin Zn2+ coordination can create a non-natural protease inhibitor independent of cell Zn2+ content. Moderation of peptidolytic reactions by either or both mechanisms could slow (a) viral multiplication (b) viral invasion and (c) the pathogenic host inflammatory response. These combined actions could allow development of acquired immunity to clear the infection before life-threatening inflammation. Nirmatrelvir (Paxlovid®) opposes COVID-19 by selective inhibition the viral main protease by a Zn2+-independent mechanism. Pending safety evaluation, predictable synergistic benefits of metformin and Zn2+, and perhaps metformin/Zn2+/Paxlovid® co-administration should be investigated.

An N terminomics toolbox combining 2-pyridinecarboxaldehyde probes and click chemistry for profiling protease specificity

Proteomic profiling of protease-generated N termini provides key insights into protease function and specificity. However, current technologies have sequence limitations or require specialized synthetic reagents for N-terminal peptide isolation. Here, we introduce an N terminomics toolbox that combines selective N-terminal biotinylation using 2-pyridinecarboxaldehyde (2PCA) reagents with chemically cleavable linkers to enable efficient enrichment of protein N termini. By incorporating a commercially available alkyne-modified 2PCA in combination with Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC), our strategy eliminates the need for chemical synthesis of N-terminal probes. Using these reagents, we developed PICS2 (Proteomic Identification of Cleavage Sites with 2PCA) to profile the specificity of subtilisin/kexin-type proprotein convertases (PCSKs). We also implemented CHOPPER (chemical enrichment of protease substrates with purchasable, elutable reagents) for global sequencing of apoptotic proteolytic cleavage sites. Based on their broad applicability and ease of implementation, PICS2 and CHOPPER are useful tools that will advance our understanding of protease biology.

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a broadly applicable platform to perform high-throughput biochemical studies of post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CL pro ), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

Shifting the balance: soluble ADAM10 as a potential treatment for Alzheimer's disease

Introduction

Accumulation of amyloid β in the brain is regarded as a key initiator of Alzheimer's disease pathology. Processing of the amyloid precursor protein (APP) in the amyloidogenic pathway yields neurotoxic amyloid β species. In the non-amyloidogenic pathway, APP is processed by membrane-bound ADAM10, the main α-secretase in the nervous system. Here we present a new enzymatic approach for the potential treatment of Alzheimer's disease using a soluble form of ADAM10.

Methods

The ability of the soluble ADAM10 to shed overexpressed and endogenous APP was determined with an ADAM10 knockout cell line and a human neuroblastoma cell line, respectively. We further examined its effect on amyloid β aggregation by thioflavin T fluorescence, HPLC, and confocal microscopy. Using N-terminal and C-terminal enrichment proteomic approaches, we identified soluble ADAM10 substrates. Finally, a truncated soluble ADAM10, based on the catalytic domain, was expressed in Escherichia coli for the first time, and its activity was evaluated.

Results

The soluble enzyme hydrolyzes APP and releases the neuroprotective soluble APPα when exogenously added to cell cultures. The soluble ADAM10 inhibits the formation and aggregation of characteristic amyloid β extracellular neuronal aggregates. The proteomic investigation identified new and verified known substrates, such as VGF and N-cadherin, respectively. The truncated variant also exhibited α-secretase capacity as shown with a specific ADAM10 fluorescent substrate in addition to shedding overexpressed and endogenous APP.

Discussion

Our in vitro study demonstrates that exogenous treatment with a soluble variant of ADAM10 would shift the balance toward the non-amyloidogenic pathway, thus utilizing its natural neuroprotective effect and inhibiting the main neurotoxic amyloid β species. The potential of such a treatment for Alzheimer's disease needs to be further evaluated in vivo.

Identification of SARS-CoV-2 PLpro and 3CLpro human proteome substrates using substrate phage display coupled with protein network analysis

Viral proteases play key roles in viral replication, and they also facilitate immune escape by proteolyzing diverse target proteins. Deep profiling of viral protease substrates in host cells is beneficial for understanding viral pathogenesis and for antiviral drug discovery. Here, we utilized substrate phage display coupled with protein network analysis (SPD-PNA) to identify human proteome substrates of SARS-CoV-2 viral proteases, including papain-like protease (PLpro) and 3C-like protease (3CLpro). We first performed peptide substrates selection of PLpro and 3CLpro, and we then used the top 24 preferred substrate sequences to identify a total of 290 putative protein substrates. Protein network analysis revealed that the top clusters of PLpro and 3CLpro substrate proteins contain ubiquitin-related proteins and cadherin-related proteins respectively. We verified that cadherin-6 (CDH6) and cadherin-12 (CDH12) are novel substrates of 3CLpro and CD177 is a novel substrate of PLpro using in vitro cleavage assays. We thus demonstrated SPD-PNA is a simple and high throughput method to identify human proteome substrates of SARS-CoV-2 viral proteases for further understanding of virus-host interactions.

Depth of Sequencing Plays a Determining Role in the Characterization of Phage Display Peptide Libraries by NGS

Next-generation sequencing (NGS) has raised a growing interest in phage display research. Sequencing depth is a pivotal parameter for using NGS. In the current study, we made a side-by-side comparison of two NGS platforms with different sequencing depths, denoted as lower-throughput (LTP) and higher-throughput (HTP). The capacity of these platforms for characterization of the composition, quality, and diversity of the unselected Ph.D.TM-12 Phage Display Peptide Library was investigated. Our results indicated that HTP sequencing detects a considerably higher number of unique sequences compared to the LTP platform, thus covering a broader diversity of the library. We found a larger percentage of singletons, a smaller percentage of repeated sequences, and a greater percentage of distinct sequences in the LTP datasets. These parameters suggest a higher library quality, resulting in potentially misleading information when using LTP sequencing for such assessment. Our observations showed that HTP reveals a broader distribution of peptide frequencies, thus revealing increased heterogeneity of the library by the HTP approach and offering a comparatively higher capacity for distinguishing peptides from each other. Our analyses suggested that LTP and HTP datasets show discrepancies in their peptide composition and position-specific distribution of amino acids within the library. Taken together, these findings lead us to the conclusion that a higher sequencing depth can yield more in-depth insights into the composition of the library and provide a more complete picture of the quality and diversity of phage display peptide libraries.

Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease

Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage - editing - of a target protein by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Efficient Cloning of Inserts for Phage Display by Golden Gate Assembly

Phage display enables the discovery of high-affinity binders. In phage display, one commonly uses traditional cloning methods to insert DNA into the coding region of one of the five capsid proteins. Here we describe the use of a new vector with kanamycin resistance and BsaI sites for the utilization of Golden Gate cloning into the N-terminus of mature protein III. We also describe the successful pentavalent display of six different inserts: the AviD-tag, the Z-domain of protein A, the Myc-tag, the ALFA nanobody, the BC2 nanobody, and the Flag-tag.

Diagnostic and therapeutic potential of protease inhibition

Proteases are enzymes that hydrolyze peptide bonds in proteins and peptides; thus, they control virtually all biological processes. Our understanding of protease function has advanced considerably from nonselective digestive enzymes to highly specialized molecular scissors that orchestrate complex signaling networks through a limited proteolysis. The catalytic activity of proteases is tightly regulated at several levels, ranging from gene expression through trafficking and maturation to posttranslational modifications. However, when this delicate balance is disturbed, many diseases develop, including cancer, inflammatory disorders, diabetes, and neurodegenerative diseases. This new understanding of the role of proteases in pathologic physiology indicates that these enzymes represent excellent molecular targets for the development of therapeutic inhibitors, as well as for the design of chemical probes to visualize their redundant activity. Recently, numerous platform technologies have been developed to identify and optimize protease substrates and inhibitors, which were further used as lead structures for the development of chemical probes and therapeutic drugs. Due to this considerable success, the clinical potential of proteases in therapeutics and diagnostics is rapidly growing and is still not completely explored. Therefore, small molecules that can selectively target aberrant protease activity are emerging in diseases cells. In this review, we describe modern trends in the design of protease drugs as well as small molecule activity-based probes to visualize selected proteases in clinical settings.

Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning

Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein-ligand interactions. We also apply ProBound to profile the kinetics of kinase-substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning

Promiscuous post-translational modification (PTM) enzymes often display nonobvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Understanding of substrate fitness landscapes for PTM enzymes is important in many areas of contemporary science, including natural product biosynthesis, molecular biology, and biotechnology. Here, we report an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features (i) a combination of mRNA display with next-generation sequencing as an ultrahigh throughput technique for data acquisition and (ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect such complexity suggests that the developed platform can facilitate a wider study of PTM enzymes.

Mechanism-based traps enable protease and hydrolase substrate discovery

Hydrolase enzymes, including proteases, are encoded by 2-3% of the genes in the human genome and 14% of these enzymes are active drug targets1. However, the activities and substrate specificities of many proteases-especially those embedded in membranes-and other hydrolases remain unknown. Here we report a strategy for creating mechanism-based, light-activated protease and hydrolase substrate traps in complex mixtures and live mammalian cells. The traps capture substrates of hydrolases, which normally use a serine or cysteine nucleophile. Replacing the catalytic nucleophile with genetically encoded 2,3-diaminopropionic acid allows the first step reaction to form an acyl-enzyme intermediate in which a substrate fragment is covalently linked to the enzyme through a stable amide bond2; this enables stringent purification and identification of substrates. We identify new substrates for proteases, including an intramembrane mammalian rhomboid protease RHBDL4 (refs. 3,4). We demonstrate that RHBDL4 can shed luminal fragments of endoplasmic reticulum-resident type I transmembrane proteins to the extracellular space, as well as promoting non-canonical secretion of endogenous soluble endoplasmic reticulum-resident chaperones. We also discover that the putative serine hydrolase retinoblastoma binding protein 9 (ref. 5) is an aminopeptidase with a preference for removing aromatic amino acids in human cells. Our results exemplify a powerful paradigm for identifying the substrates and activities of hydrolase enzymes.

Making the cut with protease engineering

Proteases cut with enviable precision and regulate diverse molecular events in biology. Such qualities drive a seemingly inexhaustible appetite for proteases with new activities and capabilities. Comprising 25% of the total industrial enzyme market, proteases appear in consumer goods, such as detergents, textile processing, and numerous foods; additionally, proteases include 25 US Food and Drug Administration-approved medicines and various research tools. Recent advances in protease engineering strategies address target specificity, catalytic efficiency, and stability. This guide to protease engineering surveys best practices and emerging strategies. We further highlight gaps and flexibilities inherent to each system that suggest opportunities for new technology development along with engineered proteases to solve challenges in proteomics, protein sequencing, and synthetic gene circuits.

Deorphanizing Caspase-3 and Caspase-9 Substrates In and Out of Apoptosis with Deep Substrate Profiling

Caspases are a family of enzymes that regulate biological processes such as inflammation and programmed cell death, through proteolysis. For example, in the intrinsic pathway of apoptosis, cell death signaling involves cytochrome c release from the mitochondria, which leads to the activation of caspase-9 and eventually the executioners caspase-3 and -7. One key step in our understanding of these proteases is to identify their respective protein substrates. Although hundreds of substrates have been linked to caspase-3, only a small handful of substrates have been reported for caspase-9. Employing deep profiling by subtiligase N-terminomics, we present here an unbiased analysis of caspase-3 and caspase-9 substrates in native cell lysates. We identified 906 putative protein substrates associated with caspase-3 and 124 protein substrates for caspase-9. This is the most comprehensive list of caspase substrates reported for each of these proteases, revealing a pool of new substrates that could not have been discovered using other approaches. Over half of the caspase-9 substrates were also cleaved by caspase-3, but often at unique sites, suggesting an evolved functional redundancy for these two proteases. Correspondingly, nearly half of the caspase-9 cleavage sites were not recognized by caspase-3. Our results suggest that in addition to its important role in activating the executioners, the role of caspase-9 is likely broader and more complex than previously appreciated, which includes proteolysis of key apoptotic substrates other than just caspase-3 and -7 and involvement in non-apoptotic pathways. Our results are well poised to aid the discovery of new biological functions for these two caspases.

tipNrich: A Tip-Based N-Terminal Proteome Enrichment Method

The mass spectrometry-based analysis of protein post-translational modifications requires large amounts of sample, complicating the analysis of samples with limited amounts of proteins such as clinical biopsies. Here, we present a tip-based N-terminal analysis method, tipNrich. The entire procedure is processed in a single pipette tip to minimize sample loss, which is so highly optimized to analyze small amounts of proteins, even femtomole-scale of a single protein. With tipNrich, we investigated various single proteins purified from different organisms using a low-resolution mass spectrometer and identified several N-terminal peptides with different Nt-modifications such as ragged N-termini. Furthermore, we applied matrix-assisted laser desorption ionization time-of-flight mass spectrometry to our method for shortening the analysis time. Moreover, we showed that our method could be utilized in disease diagnosis as exemplified by the characterization of wild-type transthyretin amyloidosis patients compared to the healthy individuals based on N-terminome profiling. In summary, tipNrich will satisfy the need of identifying N-terminal peptides even with highly scarce amounts of proteins and of having faster processing time to check the quality of protein products or to characterize N-terminal proteoform-related diseases.

Caspase-9 Activation of Procaspase-3 but Not Procaspase-6 Is Based on the Local Context of Cleavage Site Motifs and on Sequence

Studying the interactions between a protease and its protein substrates at a molecular level is crucial for identifying the factors facilitating selection of particular proteolytic substrates and not others. These selection criteria include both the sequence and the local context of the substrate cleavage site where the active site of the protease initially binds and then performs proteolytic cleavage. Caspase-9, an initiator of the intrinsic apoptotic pathway, mediates activation of executioner procaspase-3 by cleavage of the intersubunit linker (ISL) at site ¹⁷²IETD↓S. Although procaspase-6, another executioner, possesses two ISL cleavage sites (site 1, ¹⁷⁶DVVD↓N; site 2, ¹⁹⁰TEVD↓A), neither is directly cut by caspase-9. Thus, caspase-9 directly activates procaspase-3 but not procaspase-6. To elucidate this selectivity of caspase-9, we engineered constructs of procaspase-3 (e.g., swapping the ISL site, ¹⁷²IETD↓S, with DVVDN and TEVDA) and procaspase-6 (e.g., swapping site 1, ¹⁷⁶DVVD↓N, and site 2, ¹⁹⁰TEVD↓A, with IETDS). Using the substrate digestion data of these constructs, we show here that the P4-P1' sequence of procaspase-6 ISL site 1 (DVVDN) can be accessed but not cleaved by caspase-9. We also found that caspase-9 can recognize the P4-P1' sequence of procaspase-6 ISL site 2 (TEVDA); however, the local context of this cleavage site is the critical factor that prevents proteolytic cleavage. Overall, our data have demonstrated that both the sequence and the local context of the ISL cleavage sites play a vital role in preventing the activation of procaspase-6 directly by caspase-9.

Biochemical Tools for Tracking Proteolysis

All living organisms depend on tightly regulated cellular networks to control biological functions. Proteolysis is an important irreversible post-translational modification that regulates most, if not all, cellular processes. Proteases are a large family of enzymes that perform hydrolysis of protein substrates, leading to protein activation or degradation. The 473 known and 90 putative human proteases are divided into 5 main mechanistic groups: metalloproteases, serine proteases, cysteine proteases, threonine proteases, and aspartic acid proteases. Proteases are fundamental to all biological systems, and when dysregulated they profoundly influence disease progression. Inhibiting proteases has led to effective therapies for viral infections, cardiovascular disorders, and blood coagulation just to name a few. Between 5 and 10% of all pharmaceutical targets are proteases, despite limited knowledge about their biological roles. More than 50% of all human proteases have no known substrates. We present here a comprehensive list of all current known human proteases. We also present current and novel biochemical tools to characterize protease functions in vitro, in vivo, and ex vivo. These tools make it achievable to define both beneficial and detrimental activities of proteases in health and disease.

Citrullination of a phage-displayed human peptidome library reveals the fine specificities of rheumatoid arthritis-associated autoantibodies

Background

Post-translational modifications (PTMs) on proteins can be targeted by antibodies associated with autoimmunity. Despite a growing appreciation for their intrinsic role in disease, there is a lack of highly multiplexed serological assays to characterize the fine specificities of PTM-directed autoantibodies.

Methods

In this study, we used the programmable phage display technology, Phage ImmunoPrecipitation Sequencing (PhIP-Seq), to profile rheumatoid arthritis (RA) associated anti-citrullinated protein antibody (ACPA) reactivities.

Findings

Using both unmodified and peptidylarginine deiminase (PAD)-modified phage display libraries consisting of ~250,000 overlapping 90 amino acid peptide tiles spanning the human proteome, PTM PhIP-Seq robustly identified antibodies to citrulline-dependent epitopes.

Interpretation

PTM PhIP-Seq was used to quantify key differences among RA patients, including PAD isoform specific ACPA profiles, and thus represents a powerful tool for proteome-scale antibody-binding analyses.

Funding

This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. This study was made possible by a National Institute of General Medical Sciences (NIGMS) grant R01 GM136724 (HBL). MFK was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant T32AR048522. ED was supported by the Rheumatology Research Foundation.

Covalent Small Molecule Immunomodulators Targeting the Protease Active Site

Cells of the immune system utilize multiple proteases to regulate cell functions and orchestrate innate and adaptive immune responses. Dysregulated protease activities are implicated in many immune-related disorders; thus, protease inhibitors have been actively investigated for pharmaceutical development. Although historically considered challenging with concerns about toxicity, compounds that covalently modify the protease active site represent an important class of agents, emerging not only as chemical probes but also as approved drugs. Here, we provide an overview of technologies useful for the study of proteases with the focus on recent advances in chemoproteomic methods and screening platforms. By highlighting covalent inhibitors that have been designed to target immunomodulatory proteases, we identify opportunities for the development of small molecule immunomodulators.

Activatable Zymography Probes Enable In Situ Localization of Protease Dysregulation in Cancer

Recent years have seen the emergence of conditionally activated diagnostics and therapeutics that leverage protease-cleavable peptide linkers to enhance their specificity for cancer. However, due to a lack of methods to measure and localize protease activity directly within the tissue microenvironment, the design of protease-activated agents has been necessarily empirical, yielding suboptimal results when translated to patients. To address the need for spatially resolved protease activity profiling in cancer, we developed a new class of in situ probes that can be applied to fresh-frozen tissue sections in a manner analogous to immunofluorescence staining. These activatable zymography probes (AZP) detected dysregulated protease activity in human prostate cancer biopsy samples, enabling disease classification. AZPs were leveraged within a generalizable framework to design conditional cancer diagnostics and therapeutics and showcased in the Hi-Myc mouse model of prostate cancer, which models features of early pathogenesis. Multiplexed screening against barcoded substrates yielded a peptide, S16, that was robustly and specifically cleaved by tumor-associated metalloproteinases in the Hi-Myc model. In situ labeling with an AZP incorporating S16 revealed a potential role of metalloproteinase dysregulation in proliferative, premalignant Hi-Myc prostatic glands. Systemic administration of an in vivo imaging probe incorporating S16 perfectly classified diseased and healthy prostates, supporting the relevance of ex vivo activity assays to in vivo translation. We envision AZPs will enable new insights into the biology of protease dysregulation in cancer and accelerate the development of conditional diagnostics and therapeutics for multiple cancer types. SIGNIFICANCE: Visualization of protease activity within the native tissue context using AZPs provides new biological insights into protease dysregulation in cancer and guides the design of conditional diagnostics and therapeutics.