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

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

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

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 Excel-lence 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.European Commission (BioExcel-2project), Grant/Award Number: 823830; Instituto de Salud Carlos III, Grant/AwardNumber: PT17/0009/0007; Ministerio de Ciencia e Innovación, Grant/Award Numbers: PID2020-116620GB-I00, RTI2018-096704-B-100.Peer ReviewedPostprint (author's final draft

Fatty-Acid Oxygenation by Fungal Peroxygenases: From Computational Simulations to Preparative Regio- and Stereoselective Epoxidation

12 páginas.- 6 figuras.- 1 tabla.- 84 referencias.- The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acscatal.0c03165Epoxidation of unsaturated fatty acids by unspecific peroxygenases (UPOs) of the best-known long-UPO subfamily, including the Agrocybe aegerita (AaeUPO) and Coprinopsis cinerea enzymes, is reported here. To understand the different oxygenation patterns by members of the long-UPO and short-UPO subfamilies, the latter represented by the Marasmius rotula enzyme (MroUPO), fatty-acid diffusion into their heme pockets was simulated with the adaptive PELE software. Computational results shed light on the inability of AaeUPO to epoxidize oleic acid (C18:1), opposed to MroUPO, due to steric hindrances to harbor (with a good interaction energy) the substrate with the Δ9 C10 atom at a catalytically relevant distance (99%) formation of cis,cis-15,16-epoxyoctadeca-9,12-dienoic acid. The nearly complete conversion of α-linolenic acid by the two enzymes was transferred to a small preparative scale, the yield of purified product was estimated, its chemical structure analyzed by NMR, and more interestingly, stereoselective production of the 15(R),16(S) enantiomer (80-83% ee) assessed by chiral HPLC. This enzymatic synthesis overcomes the unspecificity of chemical epoxidation where the reaction cannot be restricted to the formation of monoepoxides as found during m-perchlorobenzoic acid oxidation of α-linolenic acid. Moreover, the variant was able to produce the unsaturated dicarboxylic fatty acid, together with subterminal oxygenation products, during partial conversion of oleic acid. These two noteworthy reactions had not been reported for any UPO described to date. © 2020 American Chemical Society. All rights reserved.This work has received funding from the Bio Based Industries Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 792063 (“Development and pilot production of sustainable binder systems for wood based panels”, https://susbind.eu ), the CTQ2016-79138-R and BIO2017-86559-R projects of Spanish MINECO, the Secretaria d’Universitats i Recerca of Generalitat de Catalunya, and the European Social Fund (ESF-2019-FI-B-00154). The authors thank Novozymes A/S for supplying r CciUPO. MM acknowledges a Catalan Government doctoral grant.Peer reviewe