Restrictor synergizes with Symplekin and PNUTS to terminate extragenic transcription.

Transcription termination pathways mitigate the detrimental consequences of unscheduled promiscuous initiation occurring at hundreds of thousands of genomic cis-regulatory elements. The Restrictor complex, composed of the Pol II-interacting protein WDR82 and the RNA-binding protein ZC3H4, suppresses processive transcription at thousands of extragenic sites in mammalian genomes. Restrictor-driven termination does not involve nascent RNA cleavage, and its interplay with other termination machineries is unclear. Here we show that efficient termination at Restrictor-controlled extragenic transcription units involves the recruitment of the protein phosphatase 1 (PP1) regulatory subunit PNUTS, a negative regulator of the SPT5 elongation factor, and Symplekin, a protein associated with RNA cleavage complexes but also involved in cleavage-independent and phosphatase-dependent termination of noncoding RNAs in yeast. PNUTS and Symplekin act synergistically with, but independently from, Restrictor to dampen processive extragenic transcription. Moreover, the presence of limiting nuclear levels of Symplekin imposes a competition for its recruitment among multiple transcription termination machineries, resulting in mutual regulatory interactions. Hence, by synergizing with Restrictor, Symplekin and PNUTS enable efficient termination of processive, long-range extragenic transcription.

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.