ArticleInternational journal of molecular sciences2014
Towards automated binding affinity prediction using an iterative linear interaction energy approach.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Molecular Dynamics as a Tool for Virtual Ligand Screening.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference.Scientific reports · 2022Article
- Article
- Article
- How Good is Jarzynski's Equality for Computer-Aided Drug Design?The journal of physical chemistry. B · 2020Article
- Optimal designs for pairwise calculation: An application to free energy perturbation in minimizing prediction variability.Journal of computational chemistry · 2020Article
- Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations.Frontiers in molecular biosciences · 2020Article
- A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation.Journal of chemical information and modeling · 2019Article
- Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.Journal of computer-aided molecular design · 2019Article
- Probing inhibition mechanisms of adenosine deaminase by using molecular dynamics simulations.PloS one · 2018Article
- Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2.Journal of computer-aided molecular design · 2018Article
- eTOX ALLIES: an automated pipeLine for linear interaction energy-based simulations.Journal of cheminformatics · 2017Article
- Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors.Journal of chemical information and modeling · 2017Article
- PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.Journal of chemical information and modeling · 2016Article
- Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions.Journal of molecular modeling · 2016Article
- Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation.PloS one · 2015Article
- The eTOX data-sharing project to advance in silico drug-induced toxicity prediction.International journal of molecular sciences · 2014Article
- CYP 2D6 binding affinity predictions using multiple ligand and protein conformations.International journal of molecular sciences · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.