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.

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.

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.

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 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.