Elmeri Matias Jokinen

Targeting diseases with unmet medical needs remains a major challenge, particularly when modulating protein–protein interactions. While small molecules have traditionally been employed for such targets, their limitations have driven the development of biological modalities, including monoclonal antibodies. Macrocycles occupy an intermediate space, combining the selectivity and affinity of biologics with the favorable pharmacokinetic and manufacturability profiles of small molecules. Here, we describe a rational strategy to identify medium-sized peptide-based macrocycles as ligands for TNFα. Using a minimal set of building blocks, we identified macrocycles capable of binding TNFα, demonstrating the feasibility of this approach for an otherwise undrugged cytokine target. Expansion of this strategy through larger building-block libraries, diverse cyclization chemistries, and automated high-throughput synthesis is expected to substantially enhance discovery throughput. This work illustrates the potential of rationally designing macrocycles as targeted therapeutics.

The voltage-gated potassium ion channel Kv1.3 is an attractive therapeutic target due to its central role in effector memory T cell activation and autoimmune disease. Achieving subtype-selective inhibition remains challenging due to the high sequence conservation across the Kv1 family and the lack of structural insight into binding for selective small molecule inhibitors. Here, using a ligand-based virtual screening approach seeded from an existing dichlorophenol scaffold, we identify a novel indole-based chemotype as a highly selective small molecule inhibitor of human Kv1.3. Through a combination of site-directed mutagenesis and cryoEM, we map the binding site and elucidate the inhibitory mechanism of both chemotypes. We show that dichlorophenol and indole inhibitors bind to a cryptic pocket in the P-loop adjacent to the selectivity filter, with four ligands occupying this site per Kv1.3 tetramer. Inhibitor binding induces an expansion of the selectivity filter, disrupts key hydrogen bonds, and stabilizes a channel conformation resembling the C-type inactivated state. This inhibitory mechanism is distinct from a pore block and accesses a site previously restricted to peptide-based inhibitors. This study provides the first atomic-level view of small molecule engagement to Kv1.3 and expanding the mechanistic repertoire of voltage-gated ion channel inhibition. Together, our findings enable future comparison of state-dependent inhibition and downstream effector function while establishing a structural framework for the rational development of selective small molecule voltage-gated ion channel therapeutics.

Targeting diseases with unmet medical needs remains a major challenge, particularly when modulating protein–protein interactions. While small molecules have traditionally been employed for such targets, their limitations have driven the development of biological modalities, including monoclonal antibodies. Macrocycles occupy an intermediate space, combining the selectivity and affinity of biologics with the favorable pharmacokinetic and manufacturability profiles of small molecules. Here, we describe a rational strategy to identify medium-sized peptide-based macrocycles as ligands for TNFα. Using a minimal set of building blocks, we identified macrocycles capable of binding TNFα, demonstrating the feasibility of this approach for an otherwise undrugged cytokine target. Expansion of this strategy through larger building-block libraries, diverse cyclization chemistries, and automated high-throughput synthesis is expected to substantially enhance discovery throughput. This work illustrates the potential of rationally designing macrocycles as targeted therapeutics.

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target’s binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein –ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

Enzymes in the cytochrome P450 family 1 (CYP1) catalyze metabolic activation of procarcinogens and deactivation of certain anticancer drugs. Inhibition of these enzymes is a potential approach for cancer chemoprevention and treatment of CYP1‐mediated drug resistance. We characterized inhibition of human CYP1A1, CYP1A2, and CYP1B1 enzymes by the novel inhibitor N‐(3,5‐dichlorophenyl)cyclopropanecarboxamide (DCPCC) and α‐naphthoflavone (ANF). Depending on substrate, IC₅₀ values of DCPCC for CYP1A1 or CYP1B1 were 10–95 times higher than for CYP1A2. IC₅₀ of DCPCC for CYP1A2 was 100‐fold lower than for enzymes in CYP2 and CYP3 families. DCPCC IC₅₀ values were 10–680 times higher than the ones of ANF. DCPCC was a mixed‐type inhibitor of CYP1A2. ANF was a competitive tight‐binding inhibitor of CYP1A1, CYP1A2, and CYP1B1. CYP1A1 oxidized DCPCC more rapidly than CYP1A2 or CYP1B1 to the same metabolite. Molecular dynamics simulations and binding free energy calculations explained the differences of binding of DCPCC and ANF to the active sites of all three CYP1 enzymes. We conclude that DCPCC is a more selective inhibitor for CYP1A2 than ANF. DCPCC is a candidate structure to modulate CYP1A2‐mediated metabolism of procarcinogens and anticancer drugs.

A novel virtual screening methodology called fragment‐ and negative image‐based (F‐NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A‐specific small‐molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F‐NiB combines features from both fragment‐based drug discovery and negative image‐based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein‐bound ligand(s) are seamlessly combined with the negative image of the target's ligand‐binding cavity. This cavity‐ and fragment‐based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure‐activity relationship modeling and pharmacophore modeling. Three of the small‐molecules inhibited PDE10A at ~27 μM to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F‐NiB provides a flexible way to incorporate small‐molecule fragments into the drug discovery.