Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

The AhR Signaling Mechanism: A Structural Point of View.

The Aryl hydrocarbon Receptor (AhR) is a well-known sensor of xenobiotics; moreover, it is considered a promising drug target as it is involved in the regulation of many patho-physiological processes. For these reasons the study of its ligand-activated transcription mechanism has stimulated several studies for over twenty years. In this review we highlight the key role of molecular structural information in understanding the different steps of the signaling mechanism. The architecture of the AhR cytosolic complex, encompassing the hsp90 chaperone protein and the XAP2 and p23 co-chaperones, has become available in the last year thanks to Cryo-EM experiments. The structure of the AhR ligand-binding (PAS-B) domain has remained elusive for a long time; it has been predicted by homology modelling, based on known PAS systems, and its ligand-bound forms were modelled through ligand molecular docking. Although very recently some structural information on this domain has become available, considerable efforts are still needed to determine the binding geometries of the AhR key ligands by experimental high-resolution studies. On the other hand, the dimeric structure of AhR with the ARNT protein, bound to the specific DNA responsive element, was partially determined by X-ray crystallography and it was completed by homology modelling. On the whole the current structural knowledge of the main protein complexes that form over the AhR mechanism opens the way to confirm and further investigate the main steps of the proposed ligand-activated transcription mechanism of the AhR.

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations.

Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM.

Metadynamics-Based Approaches for Modeling the Hypoxia-Inducible Factor 2α Ligand Binding Process.

Several methods based on enhanced-sampling molecular dynamics have been proposed for studying ligand binding processes. Here, we developed a protocol that combines the advantages of steered molecular dynamics (SMD) and metadynamics. While SMD is proposed for investigating possible unbinding pathways of the ligand and identifying the preferred one, metadynamics, with the path collective variable (PCV) formalism, is suggested to explore the binding processes along the pathway defined on the basis of SMD, by using only two CVs. We applied our approach to the study of binding of two known ligands to the hypoxia-inducible factor 2α, where the buried binding cavity makes simulation of the process a challenging task. Our approach allowed identification of the preferred entrance pathway for each ligand, highlighted the features of the bound and intermediate states in the free-energy surface, and provided a binding affinity scale in agreement with experimental data. Therefore, it seems to be a suitable tool for elucidating ligand binding processes of similar complex systems.

Exploring the PXR ligand binding mechanism with advanced Molecular Dynamics methods.

The Pregnane X Receptor (PXR) is a ligand-activated transcription factor belonging to the nuclear receptor family. PXR can bind diverse drugs and environmental toxicants with different binding modes, making it an intriguing target for drug discovery. Here we investigated the binding mechanism of the SR12813 ligand to elucidate the significant steps, from the ligand entrance pathway into the binding cavity, to the ligand-induced conformational changes, and to the exploration of its alternative binding geometries. We used the advanced Molecular Dynamics-based methods implemented in the BiKi suite and developed specific methodological approaches to overcome the complexity induced by the buried and flexible binding cavity. The adopted methods provided a full dynamic description of the binding event and allowed rationalization of the observed multiple binding modes. These results suggest that the same approach could be exploited for the study of other binding processes with similar characteristics.