Application of Free Energy Perturbation for the Design of BACE1 Inhibitors

Ciordia, Myriam; Pérez‐Benito, Laura; Delgado, Francisco; Trabanco, Andrés A.; Tresadern, Gary

doi:10.1021/acs.jcim.6b00220

Cited by 112 publications

(126 citation statements)

References 65 publications

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“…4), which confirms that the Berendsen barostat did not sample correctly the expected volume fluctuations in the NPT ensemble. Moreover, the volume distribution sampled in the bound state by the Berendsen barostat during the expanded ensemble calculations is quite different from that obtained through simple MD simulations, with thicker right tails and mean 80.298 ± 0.008 nm 3 . The apparent shift to the right is consistent with the volume expansion observed in the neighbor intermediate states during the expanded ensemble calculations (SI Fig.…”

Section: The Berendsen Barostat Introduces Artifacts In Expanded Ensementioning

confidence: 59%

“…Predicting the binding free energy between a receptor and a ligand has attracted a great deal of attention due to its potential to speed up small-molecule drug discovery [1]. Among the methodologies that have been developed to carry out this task, physics-based methods employing classical force fields are starting to be routinely used in drug development projects and demonstrate success in real lead optimization scenarios [2][3][4][5]. These technologies are also often employed to obtain mechanistic insights into the physics of binding such as the discovery of binding poses [6] and pathways [7], or attempts at providing intuitive guidance on how to improve ligand binding potency [8].…”

Section: Introductionmentioning

confidence: 99%

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The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

125

189

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Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

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Section: The Berendsen Barostat Introduces Artifacts In Expanded Ensementioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

125

189

View full text Add to dashboard Cite

show abstract

“…Similarly, moving from 1D/2D to 3D‐field descriptors also was not instrumental in the development of a better model but does suggest that the latter is quite useful in gaining good insight into the nature of substituent modification required at different regions of the scaffold. This is advantageous when considering the computational cost, time, and substituent restrictions required to achieve comparable outcomes using FEP methods for h BACE‐1 inhibitors adding immense value to the day‐to‐day medicinal chemistry design in lead optimization. Likewise, models developed using either moe or canvas descriptors achieve comparable performances suggesting that both tools capture the information content needed to build predictive models.…”

Section: Discussionmentioning

confidence: 99%

In silico ligand‐based modeling of hBACE‐1 inhibitors

Subramanian

Poda

2017

Chem Biol Drug Des

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Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

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“…247,248 Binding free energy calculations (ie, an average over the ensemble binding energies) is another way to find the binding affinity. 249 Particularly, free energy perturbation (FEP) calculations was used in the design of novel spiroaminodihydropyrroles, 250 and acyl guanidine with a modified scaffold, 251 for inhibitors binding the P3 pocket of BACE1.…”

Section: Quantum Mechanical Calculationmentioning

confidence: 99%

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

Schaduangrat

Prachayasittikul

Choomwattana

et al. 2019

Medicinal Research Reviews

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The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on‐ andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease‐modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the β‐amyloid precursor protein (βAPP) and β‐amyloid (Aβ) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

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Application of Free Energy Perturbation for the Design of BACE1 Inhibitors

Cited by 112 publications

References 65 publications

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

In silico ligand‐based modeling of hBACE‐1 inhibitors

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

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