Development of corrections for the absolute free binding energy prediction

The early stages of drug design rely on hit discovery programs, where initial possible inhibitors’ binding affinities are assessed when bound to their biological target. It is an expensive and time-consuming process, requiring multiple iterations of trial and error designs. This sets the perfect ground for computer simulations. Structure-based drug design has been in the past decade a widely used computational methodology to speed up the drug discovery process for resolved protein-ligand systems[1]. However, providing a fast and reliable answer to the protein-ligand affinity problem can be an arduous task. In this context, the capacity of the software to score the binding affinity of the inhibitors will be crucial to determine possible drug leads that will be later on optimized. Hence, the main goal of this research is to add physically justified corrections as well as Machine Learning models to the energetic predictions to obtain absolute binding free energies that match the experimental results. To do it we will need to review the physics involved in the forcefields used in the simulations done with the software used in the group: PELE[2]. PELE stands for Protein Energy Landscape Exploration and it is a self-contained Monte Carlo software to model protein-ligand interactions. The reachable conformations by the protein and ligand are explored and energetically assessed with the forcefield. The forcefield is the parameterized functional (eq. 1) that enables a Monte Carlo or a Molecular dynamics simulation to calculate the potential energies involved[3]. Etotal = Ebonded + Enonbonded Ebonded = Ebond + Eangle + Edihedral Enonbonded = Eelectrostatic + Evan der Waals. (1) This functional form does not take into account different energetic contributions that should be addressed. Right now we have considered adding correction terms regarding the strain and the conformational entropy loss of the ligand upon binding, as in eq. 2. ΔG = ΔGbe + ΔHstrain − TΔSconf (2

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations

Selective inhibitors of the PSEN1-gamma-secretase complex

Clinical development of Y-secretases, a family of intramembrane cleaving proteases, as therapeutic targets for a variety of disorders including cancer and Alzheimer’s disease was aborted because of serious mechanism-based side effects in the phase III trials of unselective inhibitors. Selective inhibition of specific Y-secretase complexes, containing either PSEN1 or PSEN2 as the catalytic subunit and APH1A or APH1B as supporting subunits, does provide a feasible therapeutic window in preclinical models of these disorders. We explore here the pharmacophoric features required for PSEN1 versus PSEN2 selective inhibition. We synthesized a series of brain penetrant 2-azabicyclo[2,2,2]octane sulfonamides and identified a compound with low nanomolar potency and high selectivity (>250-fold) toward the PSEN1–APH1B subcomplex versus PSEN2 subcomplexes. We used modeling and site-directed mutagenesis to identify critical amino acids along the entry part of this inhibitor into the catalytic site of PSEN1. Specific targeting one of the different Y-secretase complexes might provide safer drugs in the future.The work was supported by an AIO-project (no. HBC.2016.0884). This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. ERC-834682 CELLPHASE_AD). This work was supported by the Flanders Institute for Biotechnology (VIB vzw), a Methusalem grant from KU Leuven and the Flemish Government, the Fonds voor Wetenschappelijk Onderzoek, KU Leuven, The Queen Elisabeth Medical Foundation for Neurosciences, the Opening the Future campaign of the Leuven Universitair Fonds, the Belgian Alzheimer Research Foundation (SAO-FRA), and the Alzheimer’s Association USA.Peer ReviewedPostprint (published version

Recent PELE Developments and Applications in Drug Discovery Campaigns

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing

aquaPELE: A Monte Carlo-based algorithm to sample the effects of buried water molecules in proteins

Water is frequently found inside proteins, carrying out important roles in catalytic reactions or molecular recognition tasks. Therefore, computational models that aim to study protein–ligand interactions usually have to include water effects through explicit or implicit approaches to obtain reliable results. While full explicit models might be too computationally daunting for some applications, implicit models are normally faster but omit some of the most important contributions of water. This is the case of our in-house software, called protein energy landscape exploration (PELE), which uses implicit models to speed up conformational explorations as much as possible; the lack of explicit water sampling, however, limits its model. In this work, we confront this problem with the development of aquaPELE. It is a new algorithm that extends the exploration capabilities while keeping efficiency as it employs a mixed implicit/explicit approach to also take into account the effects of buried water molecules. With an additional Monte Carlo (MC) routine, a set of explicit water molecules is perturbed inside protein cavities and their effects are dynamically adjusted to the current state of the system. As a result, this implementation can be used to predict the principal hydration sites or the rearrangement and displacement of conserved water molecules upon the binding of a ligand. We benchmarked this new tool focusing on estimating ligand binding modes and hydration sites in cavities with important interfacial water molecules, according to crystallographic structures. Results suggest that aquaPELE sets a fast and reliable alternative for molecular recognition studies in systems with a strong water-dependency.This work has been supported by a predoctoral fellowship from the Government of Catalonia (2019FI_B_00154 to MM).Peer ReviewedPostprint (author's final draft

Recent PELE developments and applications in drug discovery campaigns

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.Postprint (published version

Selective synthesis of 4-hydroxyisophorone and 4-ketoisophorone by fungal peroxygenases

The recently discovered unspecific peroxygenases (UPOs) from the ascomycetes Chaetomium globosum and Humicola insolens were capable of selectively hydroxylating isophorone to 4-hydroxyisophorone (4HIP) and 4-ketoisophorone (4KIP), which are substrates of interest for the pharmaceutical and flavor-and-fragrance sectors. The model UPO from the basidiomycete Agrocybe aegerita was less regioselective, forming 7-hydroxyisophorone (and 7-formylisophorone) in addition to 4HIP. However, it was the most stereoselective UPO yielding the S-enantiomer of 4HIP with 88% ee. Moreover, using H. insolens UPO full kinetic resolution of racemic HIP was obtained within only 15 min, with >75% recovery of the R-enantiomer. Surprisingly, the UPOs from two other basidiomycetes, Marasmius rotula and Coprinopsis cinerea, failed to transform isophorone. The different UPO selectivities were rationalized by computational simulations, in which isophorone and 4HIP were diffused into the enzymes using the adaptive PELE software, and the distances from heme-bound oxygen in H2O2-activated enzyme to different substrate atoms, and the corresponding binding energies were analyzed. Interestingly, for process upscaling, full conversion of 10 mM isophorone was achieved with H. insolens UPO within nine hours, with total turnover numbers up to 5500. These biocatalysts, which only require H2O2 for activation, may represent a novel, simple and environmentally-friendly route for the production of isophorone derivatives.This work was supported by the EnzOx2 (H2020-BBI-PPP-2015-2-1-720297) EU-project, the AGL2014-53730-R (BIORENZYMERY) and CTQ2016-79138-R projects of the Spanish MINECO (co-financed by FEDER) and the CSIC (201740E071) project. Novozymes (Bagsvaerd, Denmark) is acknowledged for providing samples of rCciUPO and rHinUPO.Peer Reviewe

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under: Data Science > Computer Algorithms and Programming Data Science > Databases and Expert Systems Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Method

Selective synthesis of 4-hydroxyisophorone and 4-ketoisophorone by fungal peroxygenases

8 páginas.-- 4 figuras.-- 4 tablas.-- 43 referencias.-- Supporting information http://www.rsc.org/suppdata/c8/cy/c8cy02114g/c8cy02114g1.pdfThe recently discovered unspecific peroxygenases (UPOs) from the ascomycetes Chaetomium globosum and Humicola insolens were capable of selectively hydroxylating isophorone to 4-hydroxyisophorone (4HIP) and 4-ketoisophorone (4KIP), which are substrates of interest for the pharmaceutical and flavor-and-fragrance sectors. The model UPO from the basidiomycete Agrocybe aegerita was less regioselective, forming 7-hydroxyisophorone (and 7-formylisophorone) in addition to 4HIP. However, it was the most stereoselective UPO yielding the S-enantiomer of 4HIP with 88% ee. Moreover, using H. insolens UPO full kinetic resolution of racemic HIP was obtained within only 15 min, with >75% recovery of the R-enantiomer. Surprisingly, the UPOs from two other basidiomycetes, Marasmius rotula and Coprinopsis cinerea, failed to transform isophorone. The different UPO selectivities were rationalized by computational simulations, in which isophorone and 4HIP were diffused into the enzymes using the adaptive PELE software, and the distances from heme-bound oxygen in H2O2-activated enzyme to different substrate atoms, and the corresponding binding energies were analyzed. Interestingly, for process upscaling, full conversion of 10 mM isophorone was achieved with H. insolens UPO within nine hours, with total turnover numbers up to 5500. These biocatalysts, which only require H2O2 for activation, may represent a novel, simple and environmentally-friendly route for the production of isophorone derivatives.This work was supported by the EnzOx2 (H2020-BBI-PPP-2015-2-1-720297) EU-project, the AGL2014-53730-R (BIORENZYMERY) and CTQ2016-79138-R pojects of the Spanish MINECO (co-financed by FEDER) and the CSIC (201740E071) project. Novozymes (Bagsvaerd, Denmark) is acknowledged for providing samples of rCciUPO and rHinUPO.Peer reviewe