ReviewCurrent opinion in structural biology2018
Predicting allostery and microbial drug resistance with molecular simulations.
Review in Current opinion in structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 15 citations in OpenAlex.
- Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding.The journal of physical chemistry. B · 2025Article
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.bioRxiv : the preprint server for biology · 2025Article
- Transition Path Sampling Based Free Energy Calculations of Evolution's Effect on Rates in β-Lactamase: The Contributions of Rapid Protein Dynamics to Rate.The journal of physical chemistry. B · 2024Article
- Special Issue: "Molecular Dynamics Simulations and Structural Analysis of Protein Domains".International journal of molecular sciences · 2024Article
- The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability.International journal of molecular sciences · 2021Article
- Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.Frontiers in molecular biosciences · 2020Review
- Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase.Frontiers in molecular biosciences · 2019Article
- Mutations Utilize Dynamic Allostery to Confer Resistance in TEM-1 β-lactamase.International journal of molecular sciences · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Beta-lactamase enzymes mediate the most common forms of gram-negative antibiotic resistance affecting clinical treatment. They also constitute an excellent model system for the difficult problem of understanding how allosteric mutations can augment catalytic activity of already-competent enzymes. Multiple allosteric mutations have been identified that alter catalytic activity or drug-resistance spectrum in class A beta lactamases, but predicting these in advance continues to be challenging. Here, we review computational techniques based on structure and/or molecular simulation to predict such mutations. Structure-based techniques have been particularly helpful in developing graph algorithms for analyzing critical residues in beta-lactamase function, while classical molecular simulation has recently shown the ability to prospectively predict allosteric mutations increasing beta-lactamase activity and drug resistance. These will ultimately achieve the greatest power when combined with simulation methods that model reactive chemistry to calculate activation free energies directly.
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.