ArticleJournal of chemical theory and computation2019
Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations.
Article in Journal of chemical theory and computation, 2019. 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.
- Synergistic Potential of Repurposing non-β-lactam Compounds as Class A serine β-lactamases Inhibitor: Insights from MolecularDocking, Molecular Dynamics Simulations and Antimicrobial Potentiation.Scientific reports · 2026Article
- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- Enhancing the thermostability of lignin peroxidase: Heme as a keystone cofactor driving stability changes in heme enzymes.Heliyon · 2024Article
- De novo design of drug-binding proteins with predictable binding energy and specificity.Science (New York, N.Y.) · 2024Article
- Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media.Journal of chemical theory and computation · 2024Article
- Article
- Engineering excitonically coupled dimers in an artificial protein for light harvesting via computational modeling.Protein science : a publication of the Protein Society · 2023Article
- Recognition of quinolone antibiotics by the multidrug efflux transporter MexB ofPhysical chemistry chemical physics : PCCP · 2022Article
- The stability and dynamics of computationally designed proteins.Protein engineering, design & selection : PEDS · 2022Review
- Molecular Dynamics Simulations in Designing DARPins as Phosphorylation-Specific Protein Binders of ERK2.Molecules (Basel, Switzerland) · 2021Article
- A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro.PLoS computational biology · 2020Article
- Protein storytelling through physics.Science (New York, N.Y.) · 2020Review
- New computational protein design methods for de novo small molecule binding sites.PLoS computational biology · 2020Article
- Multiple reactivities of flavonoids towards pathological elements in Alzheimer's disease: structure-activity relationship.Chemical science · 2020Article
- A defined structural unit enables de novo design of small-molecule-binding proteins.Science (New York, N.Y.) · 2020Article
- Water Networks and Correlated Motions in Mutant Isocitrate Dehydrogenase 1 (IDH1) Are Critical for Allosteric Inhibitor Binding and Activity.Biochemistry · 2020Article
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
Abstract
Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.
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.