Structure and computation-guided yeast surface display for the evolution of TIMP-based matrix metalloproteinase inhibitors.

The study of protein-protein interactions (PPIs) and the engineering of protein-based inhibitors often employ two distinct strategies. One approach leverages the power of combinatorial libraries, displaying large ensembles of mutant proteins, for example, on the yeast cell surface, to select binders. Another approach harnesses computational modeling, sifting through an astronomically large number of protein sequences and attempting to predict the impact of mutations on PPI binding energy. Individually, each approach has inherent limitations, but when combined, they generate superior outcomes across diverse protein engineering endeavors. This synergistic integration of approaches aids in identifying novel binders and inhibitors, fine-tuning specificity and affinity for known binding partners, and detailed mapping of binding epitopes. It can also provide insight into the specificity profiles of varied PPIs. Here, we outline strategies for directing the evolution of tissue inhibitors of metalloproteinases (TIMPs), which act as natural inhibitors of matrix metalloproteinases (MMPs). We highlight examples wherein design of combinatorial TIMP libraries using structural and computational insights and screening these libraries of variants using yeast surface display (YSD), has successfully optimized for MMP binding and selectivity, and conferred insight into the PPIs involved.

Statistical Mechanics Metrics in Pairing and Parsing In Silico and Phenotypic Data of a Novel Genetic NFκB1 (c.T638A) Variant.

(1) Background: Mutations in NFκB1, a transcriptional regulator of immunomodulating proteins, are a known cause of inborn errors of immunity. Our proband is a 22-year-old male with a diagnosis of common variable immunodeficiency (CVID), cytopenias with massive splenomegaly, and nodular regenerative hyperplasia of the liver. Genetic studies identified a novel, single-point mutation variant in NFκB1, c. T638A p. V213E. (2) Methods: Next-generation panel sequencing of the patient uncovered a novel single-point mutation in the NFκB1 gene that was modeled using the I-TASSER homology-modeling software, and molecular dynamics were assessed using the YASARA2 software (version 20.14.24). (3) Results: This variant replaces valine with glutamic acid at position 213 in the NFκB1 sequence. Molecular modeling and molecular dynamic studies showed altered dynamics in and around the rel homology domain, ankyrin regions, and death domain of the protein. We postulate that these changes alter overall protein function. (4) Conclusions: This case suggests the pathogenicity of a novel variant using protein-modeling techniques and molecular dynamic simulations.

Unveiling an indole alkaloid diketopiperazine biosynthetic pathway that features a unique stereoisomerase and multifunctional methyltransferase.

The 2,5-diketopiperazines are a prominent class of bioactive molecules. The nocardioazines are actinomycete natural products that feature a pyrroloindoline diketopiperazine scaffold composed of two D-tryptophan residues functionalized by N- and C-methylation, prenylation, and diannulation. Here we identify and characterize the nocardioazine B biosynthetic pathway from marine Nocardiopsis sp. CMB-M0232 by using heterologous biotransformations, in vitro biochemical assays, and macromolecular modeling. Assembly of the cyclo-L-Trp-L-Trp diketopiperazine precursor is catalyzed by a cyclodipeptide synthase. A separate genomic locus encodes tailoring of this precursor and includes an aspartate/glutamate racemase homolog as an unusual D/L isomerase acting upon diketopiperazine substrates, a phytoene synthase-like prenyltransferase as the catalyst of indole alkaloid diketopiperazine prenylation, and a rare dual function methyltransferase as the catalyst of both N- and C-methylation as the final steps of nocardioazine B biosynthesis. The biosynthetic paradigms revealed herein showcase Nature's molecular ingenuity and lay the foundation for diketopiperazine diversification via biocatalytic approaches.

Substitution of PINK1 Gly411 modulates substrate receptivity and turnover.

The ubiquitin (Ub) kinase-ligase pair PINK1-PRKN mediates the degradation of damaged mitochondria by macroautophagy/autophagy (mitophagy). PINK1 surveils mitochondria and upon stress accumulates on the mitochondrial surface where it phosphorylates serine 65 of Ub to activate PRKN and to drive mitochondrial turnover. While loss of either PINK1 or PRKN is genetically linked to Parkinson disease (PD) and activating the pathway seems to have great therapeutic potential, there is no formal proof that stimulation of mitophagy is always beneficial. Here we used biochemical and cell biological methods to study single nucleotide variants in the activation loop of PINK1 to modulate the enzymatic function of this kinase. Structural modeling and in vitro kinase assays were used to investigate the molecular mechanism of the PINK1 variants. In contrast to the PD-linked PINK1G411S mutation that diminishes Ub kinase activity, we found that the PINK1G411A variant significantly boosted Ub phosphorylation beyond levels of PINK1 wild type. This resulted in augmented PRKN activation, mitophagy rates and increased viability after mitochondrial stress in midbrain-derived, gene-edited neurons. Mechanistically, the G411A variant stabilizes the kinase fold of PINK1 and transforms Ub to adopt the preferred, C-terminally retracted conformation for improved substrate turnover. In summary, we identify a critical role of residue 411 for substrate receptivity that may now be exploited for drug discovery to increase the enzymatic function of PINK1. The genetic substitution of Gly411 to Ala increases mitophagy and may be useful to confirm neuroprotection in vivo and might serve as a critical positive control during therapeutic development.Abbreviations: ATP: adenosine triphosphate; CCCP: carbonyl cyanide m-chlorophenyl hydrazone; Ub-CR: ubiquitin with C-terminally retracted tail; CTD: C-terminal domain (of PINK1); ELISA: enzyme-linked immunosorbent assay; HCI: high-content imaging; IB: immunoblot; IF: immunofluorescence; NPC: neuronal precursor cells; MDS: molecular dynamics simulation; PD: Parkinson disease; p-S65-Ub: ubiquitin phosphorylated at Ser65; RMSF: root mean scare fluctuation; TOMM: translocase of outer mitochondrial membrane; TVLN: ubiquitin with T66V and L67N mutation, mimics Ub-CR; Ub: ubiquitin; WT: wild-type.

Activity-based protein profiling reveals active serine proteases that drive malignancy of human ovarian clear cell carcinoma.

Ovarian clear cell carcinoma (OCCC) is an understudied poor prognosis subtype of ovarian cancer lacking in effective targeted therapies. Efforts to define molecular drivers of OCCC malignancy may lead to new therapeutic targets and approaches. Among potential targets are secreted proteases, enzymes which in many cancers serve as key drivers of malignant progression. Here, we found that inhibitors of trypsin-like serine proteases suppressed malignant phenotypes of OCCC cell lines. To identify the proteases responsible for malignancy in OCCC, we employed activity-based protein profiling to directly analyze enzyme activity. We developed an activity-based probe featuring an arginine diphenylphosphonate warhead to detect active serine proteases of trypsin-like specificity and a biotin handle to facilitate affinity purification of labeled proteases. Using this probe, we identified active trypsin-like serine proteases within the complex proteomes secreted by OCCC cell lines, including two proteases in common, tissue plasminogen activator and urokinase-type plasminogen activator. Further interrogation of these proteases showed that both were involved in cancer cell invasion and proliferation of OCCC cells and were also detected in in vivo models of OCCC. We conclude the detection of tissue plasminogen activator and urokinase-type plasminogen activator as catalytically active proteases and significant drivers of the malignant phenotype may point to these enzymes as targets for new therapeutic strategies in OCCC. Our activity-based probe and profiling methodology will also serve as a valuable tool for detection of active trypsin-like serine proteases in models of other cancers and other diseases.

Engineering of tissue inhibitor of metalloproteinases TIMP-1 for fine discrimination between closely related stromelysins MMP-3 and MMP-10.

Matrix metalloproteinases (MMPs) have long been known as key drivers in the development and progression of diseases, including cancer and neurodegenerative, cardiovascular, and many other inflammatory and degenerative diseases, making them attractive potential drug targets. Engineering selective inhibitors based upon tissue inhibitors of metalloproteinases (TIMPs), endogenous human proteins that tightly yet nonspecifically bind to the family of MMPs, represents a promising new avenue for therapeutic development. Here, we used a counter-selective screening strategy for directed evolution of yeast-displayed human TIMP-1 to obtain TIMP-1 variants highly selective for the inhibition of MMP-3 in preference over MMP-10. As MMP-3 and MMP-10 are the most similar MMPs in sequence, structure, and function, our results thus clearly demonstrate the capability for engineering full-length TIMP proteins to be highly selective MMP inhibitors. We show using protein crystal structures and models of MMP-3-selective TIMP-1 variants bound to MMP-3 and counter-target MMP-10 how structural alterations within the N-terminal and C-terminal TIMP-1 domains create new favorable and selective interactions with MMP-3 and disrupt unique interactions with MMP-10. While our MMP-3-selective inhibitors may be of interest for future investigation in diseases where this enzyme drives pathology, our platform and screening strategy can be employed for developing selective inhibitors of additional MMPs implicated as therapeutic targets in disease.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement.

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.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins.

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.

A Virtual Screening Platform Identifies Chloroethylagelastatin A as a Potential Ribosomal Inhibitor.

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.

Targeting an autocrine IL-6-SPINK1 signaling axis to suppress metastatic spread in ovarian clear cell carcinoma.

A major clinical challenge of ovarian cancer is the development of malignant ascites accompanied by widespread peritoneal metastasis. In ovarian clear cell carcinoma (OCCC), a challenging subtype of ovarian cancer, this problem is compounded by near-universal primary chemoresistance; patients with advanced stage OCCC thus lack effective therapies and face extremely poor survival rates. Here we show that tumor-cell-expressed serine protease inhibitor Kazal type 1 (SPINK1) is a key driver of OCCC progression and metastasis. Using cell culture models of human OCCC, we find that shRNA silencing of SPINK1 sensitizes tumor cells to anoikis and inhibits proliferation. Knockdown of SPINK1 in OCCC cells also profoundly suppresses peritoneal metastasis in mouse implantation models of human OCCC. We next identify a novel autocrine signaling axis in OCCC cells whereby tumor-cell-produced interleukin-6 (IL-6) regulates SPINK1 expression to stimulate a common protumorigenic gene expression pattern leading to anoikis resistance and proliferation of OCCC cells. We further demonstrate that this signaling pathway can be successfully interrupted with the IL-6Rα inhibitor tocilizumab, sensitizing cells to anoikis in vitro and reducing metastasis in vivo. These results suggest that clinical trials of IL-6 pathway inhibitors in OCCC may be warranted, and that SPINK1 might offer a candidate predictive biomarker in this population.

Structural Models for the Dynamic Effects of Loss-of-Function Variants in the Human SIM1 Protein Transcriptional Activation Domain.

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.