Radisky, Evette S

Matrix metalloproteinases (MMPs) are secreted and cell membrane‐associated enzymes that remodel the extracellular matrix (ECM) and cleave extracellular proteins to promote tumor invasion, angiogenesis, immune evasion, and many other aspects of cancer progression. Among this family, the gelatinases MMP‐2 and MMP‐9, specialized for cleaving collagen IV, are tightly linked to metastatic progression in breast cancer and adverse clinical outcomes. Here, we summarize gelatinase biochemistry and regulation, including zymogen activation, TIMP control, cell‐surface trafficking and tethering, and receptor‐mediated clearance, and explain how crosstalk between ECM and proteases amplifies invasion and metastatic seeding. We then review therapeutic strategies in two parts: direct inhibition and indirect pathway modulation. Direct approaches emphasize selective small molecules (thiirane mechanism‐based inhibitors, allosteric blockers of pro‐MMP‐9 activation) and protein biologics (monoclonal antibodies, nanobodies, engineered TIMPs). Indirect strategies target upstream signals that drive MMP‐2/‐9, including MAPK/AP‐1, PI3K/Akt/mTOR, NF‐κB, EGFR/JAK/STAT, and nuclear receptor and Nrf2/HO‐1 programs, with agents that curb invasion, angiogenesis, and metastasis in breast cancer models. Together, these advances define a maturing toolkit for precise gelatinase control and support prospective evaluation in rational combinations to restrain metastatic progression in high‐risk breast cancer.

Selective inhibitors are essential for targeted therapeutics and for probing enzyme functions in various biological systems. The two main challenges in identifying such protein‐based inhibitors lie in the extensive experimental effort required, including the generation of large libraries, and in tailoring the selectivity of inhibitors to enzymes with homologous structures. To address these challenges, machine learning (ML) is being used to improve protein design by training on targeted libraries and identifying key interface mutations that enhance affinity and specificity. However, such ML‐based methods are limited by inaccurate energy calculations and difficulties in predicting the structural impacts of multiple mutations. Here, we present an ML‐based method that leverages HTS data to streamline the design of selective protease inhibitors. To demonstrate its utility, we applied our new method to find inhibitors of matrix metalloproteinases (MMPs), a family of homologous proteases involved in both physiological and pathological processes. By training ML models on binding data for three MMPs (MMP‐1, MMP‐3, and MMP‐9), we successfully designed a novel N‐TIMP2 variant with a differential specificity profile, namely, high affinity for MMP‐9, moderate affinity for MMP‐3, and low affinity for MMP‐1. Our experimental validation showed that this novel variant exhibited a significant specificity shift and enhanced selectivity compared with wild‐type N‐TIMP2. Through molecular modeling and energy minimization, we obtained structural insights into the variant's enhanced selectivity. Our findings highlight the power of ML‐based methods to reduce experimental workloads, facilitate the rational design of selective inhibitors, and advance the understanding of specific inhibitor–enzyme interactions in homologous enzyme systems.

Selective inhibition of the more than 100 S1 family serine proteases is a long-standing challenge due to their active site similarity. Mesotrypsin, implicated in cancer progression, exemplifies these difficulties; no current inhibitors achieve selectivity over other human trypsins. We found an unexpected autoinhibited conformation of mesotrypsin via x-ray crystallography, revealing a cryptic pocket adjacent to the active site. Using high-throughput virtual screening targeting this cryptic pocket, we identified a conformationally selective small-molecule inhibitor that stabilizes the inactive state of mesotrypsin. This inhibitor demonstrates selectivity for mesotrypsin over other trypsins. Our findings challenge the accepted view of digestive trypsins as constitutively active enzymes lacking potential for allosteric regulation. Furthermore, analyses of other structures suggest that dynamic sampling of closed states with analogous allosteric cryptic pockets appears widespread among S1 serine proteases. These observations point to a potentially generalizable strategy to achieve selective inhibition, offering broad implications for drug development targeting serine proteases in cancer and other diseases.

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-PAT100 and N-TIMP2-PAT200). Both constructs demonstrated higher apparent molecular weights and retained high inhibitory activity against MMP-9. N-TIMP2-PAT200 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.

Abstract Since the proposition of the pro‐invasive activity of proteolytic enzymes over 70 years ago, several roles for proteases in cancer progression have been established. About half of the 473 active human proteases are expressed in the prostate and many of the most well‐characterized members of this enzyme family are regulated by androgens, hormones essential for development of prostate cancer. Most notably, several kallikrein‐related peptidases, including KLK3 (prostate‐specific antigen, PSA), the most well‐known prostate cancer marker, and type II transmembrane serine proteases, such as TMPRSS2 and matriptase, have been extensively studied and found to promote prostate cancer progression. Recent findings also suggest a critical role for proteases in the development of advanced and aggressive castration‐resistant prostate cancer (CRPC). Perhaps the most intriguing evidence for this role comes from studies showing that the protease‐activated transmembrane proteins, Notch and CDCP1, are associated with the development of CRPC. Here, we review the roles of proteases in prostate cancer, with a special focus on their regulation by androgens.

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

We are presenting our on going studies with inhibitory research on Tmprss2, S-protein:Ace2, and 3CLpro using compound screening coupled with X-ray crystallography, molecular modeling, live virus screening using host human cells (BSL3 facility at UC Center for Infectious Disease and Vector Research, and organ-on-a-chip at Harvard Medical School for safety profiling before proceeding to animal models with InVivo BioSystems for ADMET. We have derived a useful chemical toolkit of 350 compounds for the community to study with biochemical assays and other biophysical-chemical studies that will prove useful in searching for optimal inhibitors of these targets to find suitable pharmacophores for blocking each of these enzyme's activities -- that would be beneficial for human health. Our past successes with these methodologies have resulted in over 28 patents, 11 technologies and two startup companies.

We are presenting our on going studies with inhibitory research on Tmprss2, S-protein:Ace2, and 3CLpro using compound screening coupled with X-ray crystallography, molecular modeling, live virus screening using host human cells (BSL3 facility at UC Center for Infectious Disease and Vector Research, and organ-on-a-chip at Harvard Medical School for safety profiling before proceeding to animal models with InVivo BioSystems for ADMET. We have derived a useful chemical toolkit of 350 compounds for the community to study with biochemical assays and other biophysical-chemical studies that will prove useful in searching for optimal inhibitors of these targets to find suitable pharmacophores for blocking each of these enzyme's activities -- that would be beneficial for human health. Our past successes with these methodologies have resulted in over 28 patents, 11 technologies and two startup companies.

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 APPIP13W/M17G/I18F/F34V, with up to 30-fold greater specificity relative to the parental APPIM17G/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.