Coban, Matt

There is evidence in the literature that many serine proteases, as typified by thrombin, possess an autoinhibitory loop (AIL) that controls substrate access to the active site. The presence of an AIL has not been documented in trypsins. Mesotrypsin is a human trypsin implicated in tumor cell invasiveness for pancreatic and other cancers. However, it is a challenging therapeutic target because of high structural similarity to other trypsin‐like serine proteases, typically lower affinity for inhibitors compared to related serine proteases, and unique ability to cleave proteinaceous serine protease inhibitors. We have recently solved a high‐resolution crystal structure of unbound mesotrypsin which revealed that residues 215‐220 partially occluded the substrate binding site. Comparison of this new structure with previous inhibitor‐bound crystal structures of mesotrypsin demonstrate the conformational transition of the AIL between closed and open states. These two conformational states were investigated with molecular dynamics simulations to discern the dynamical behavior of the AIL in different conformations. The AIL has increased local dynamics in the closed position, as well as a larger number of conformational clusters; this suggests there is a more well‐defined open state ready to accept substrate, while there are a dynamic variety of closed states that preclude substrate binding to various extents. Additionally, we identified novel binding sites that are exposed by the conformational shift of the AIL to the closed state. We have conducted initial in silico screening for compounds predicted to bind preferentially to the AIL closed (but not open) structure and stabilize the closed conformation. These efforts have resulted in more than 40 compounds as initial hits. Exploiting the AIL as a novel allosteric binding site represents a new mechanism for targeting mesotrypsin and preventing its protumorigenic function.

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.

Chloroethylagelastatin A (CEAA) is an analogue of agelastatin A (AA), a natural alkaloid derived from a marine sponge. It is under development for therapeutic use against brain tumors as it has excellent central nervous system (CNS) penetration and pre-clinical therapeutic activity against brain tumors. Recently, AA was shown to inhibit protein synthesis by binding to the ribosomal A-site. In this study, we developed a novel virtual screening platform to perform a comprehensive screening of various AA analogues showing that AA analogues with proven therapeutic activity including CEAA have significant ribosomal binding capacity whereas therapeutically inactive analogues show poor ribosomal binding and revealing structural fingerprint features essential for drug-ribosome interactions. In particular, CEAA was found to have greater ribosomal binding capacity than AA. Biological tests showed that CEAA binds the ribosome and contributes to protein synthesis inhibition. Our findings suggest that CEAA may possess ribosomal inhibitor activity and that our virtual screening platform may be a useful tool in discovery and development of novel ribosomal inhibitors.

Single-minded homologue 1 (SIM1) is a transcription factor with numerous different physiological and developmental functions. SIM1 is a member of the class I basic helix-loop-helix-PER-ARNT-SIM (bHLH–PAS) transcription factor family, that includes several other conserved proteins, including the hypoxia-inducible factors, aryl hydrocarbon receptor, neuronal PAS proteins, and the CLOCK circadian regulator. Recent studies of HIF-a-ARNT and CLOCK-BMAL1 protein complexes have revealed the organization of their bHLH, PASA, and PASB domains and provided insight into how these heterodimeric protein complexes form; however, experimental structures for SIM1 have been lacking. Here, we describe the first full-length atomic structural model for human SIM1 with its binding partner ARNT in a heterodimeric complex and analyze several pathogenic variants utilizing state-of-the-art simulations and algorithms. Using local and global positional deviation metrics, deductions to the structural basis for the individual mutants are addressed in terms of the deleterious structural reorganizations that could alter protein function. We propose new experiments to probe these hypotheses and examine an interesting SIM1 dynamic behavior. The conformational dynamics demonstrates conformational changes on local and global regions that represent a mechanism for dysfunction in variants presented. In addition, we used our ab initio hybrid model for further prediction of variant hotspots that can be engineered to test for counter variant (restoration of wild-type function) or basic research probe.

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.

Enzymes of the matrix metalloproteinase (MMP) family have been shown to play critical roles in cancer progression and metastasis, making them ideal targets for developing cancer therapeutics. MMP‐9 plays a significant role in tumor progression and metastasis of triple negative breast cancer (TNBC), while MMP‐3, the biological activator of MMP‐9, can also promote mammary carcinoma through inducing epithelial‐mesenchymal‐transition (EMT) in tumor cells. These enzymes are attractive as therapeutic targets; however, broad spectrum MMP inhibitors have proven ineffective, so inhibitors with greater selectivity are desired. Tissue inhibitor of metalloproteinases‐1 (TIMP‐1) is a natural inhibitor of MMP‐3 and MMP‐9 with picomolar affinity, and offers a scaffold for engineering of selective MMP‐targeted therapeutics. To overcome the challenges of wide multispecificity of TIMP‐1 for different classes of MMPs, we used state‐of‐the‐art directed evolution and yeast surface display techniques recruiting high‐throughput library screening technology to fully evolve novel protein‐based drugs, based on the TIMP‐1 scaffold, with high selectivity for MMP‐3 or MMP‐9. We have developed a counter‐selection strategy to screen TIMP‐1 mutants that bind selectively to MMP‐3 in the presence of MMP‐10, an MMP with the greatest sequence and structural similarity to MMP‐3. The isolated TIMP‐1 mutants after five rounds of competitive screening showed up to 20‐fold improvement in binding selectivity, highlighting the significant potential of this approach for development of inhibitors with single‐MMP selectivity. In ongoing studies, I have identified some of the key TIMP‐1 mutations responsible for improvements in MMP‐3 affinity and selectivity. I have co‐crystallized the top affinity and selectivity mutants with the catalytic domain of MMP‐3, and have solved the structure. These structures provide insights into the structural basis for improvements in binding affinity and selectivity. These studies and methodology developed in this project, will lay the foundation for developing novel therapeutic strategies for MMP‐related diseases and understanding protein‐protein interaction between MMPs and their inhibitors.

:Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be “master regulators”. For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as “undruggable”, is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

In-frame decoding in the ribosome occurs through canonical or wobble Watson–Crick pairing of three mRNA codon bases (a triplet) with a triplet of anticodon bases in tRNA. Departures from the triplet–triplet interaction can result in frameshifting, meaning downstream mRNA codons are then read in a different register. There are many mechanisms to induce frameshifting, and most are insufficiently understood. One previously proposed mechanism is doublet decoding, in which only codon bases 1 and 2 are read by anticodon bases 34 and 35, which would lead to −1 frameshifting. In E. coli, tRNASer3GCU can induce −1 frameshifting at alanine (GCA) codons. The logic of the doublet decoding model is that the Ala codon’s GC could pair with the tRNASer3′s GC, leaving the third anticodon residue U36 making no interactions with mRNA. Under that model, a U36C mutation would still induce −1 frameshifting, but experiments refute this. We perform all-atom simulations of wild-type tRNASer3, as well as a U36C mutant. Our simulations revealed a hydrogen bond between U36 of the anticodon and G1 of the codon. The U36C mutant cannot make this interaction, as it lacks the hydrogen-bond-donating H3. The simulation thus suggests a novel, non-doublet decoding mechanism for −1 frameshifting by tRNASer3 at Ala codons.

Abstract Ovarian cancer remains a challenging disease, with an average 5 year survival rate of 46%. At time of diagnosis, over 70% of patients have advanced metastatic disease, for which there are no effective treatment options. Of patients with metastatic disease, more than 70% of patients develop peritoneal carcinomatosis, making the peritoneum one of the main areas of dissemination. We have previously found that expression of the serine protease inhibitor Kazal type 1 (SPINK1) by ovarian cancer cells drives anoikis resistance, allowing the tumor cells to circumvent apoptosis protocols and facilitating tumor cell metastasis. For this new study we focused on ovarian clear cell carcinoma (OCCC), a histotype of ovarian cancer that has particularly poor prognosis if diagnosed in late stage. Using OCCC cell lines, we found that knockdown of SPINK1 reduces cell survival and stimulates apoptotic pathways in OCCC cells, when cultured under attachment-free conditions to mimic metastatic disease. We then identified Interleukin-6 (IL-6) as an upstream regulator of SPINK1 expression and a potential therapeutic target for metastatic OCCC: we showed that IL-6 silencing reduced SPINK1 mRNA expression and cell survival, while treatment with recombinant IL-6 increased SPINK1 expression and attachment-free survival. We developed a new OCCC mouse model and showed that knockdown of SPINK1 results in significant reduction of metastatic lesions, highlighting the impact of SPINK1 on OCCC metastasis. In ongoing studies we are testing intraperitoneal injections of FDA approved IL-6 inhibitors in a preclinical model to assess impact on abdominal dissemination of tumor cells. By targeting the IL-6 – SPINK1 signaling axis, we aim to develop therapeutic approaches to reduce the metastatic burden and peritoneal carcinomatosis of late stage disease, resulting in better prognosis and survival of patients with ovarian clear cell carcinoma. Citation Format: Christine Mehner, Erin Miller, Mathew A. Coban, Alexandra Hockla, Derek C. Radisky, Evette S. Radisky. Targeting ovarian cancer induced peritoneal carcinomatosis by inhibition of signaling axis between Interleukin-6 (IL-6) and serine protease inhibitor Kazal type 1 (SPINK1) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3758.