ArticleBiointerphases2012
Comparison between empirical protein force fields for the simulation of the adsorption behavior of structured LK peptides on functionalized surfaces.
Article in Biointerphases, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- AMOEBA Force Field Predicts Accurate Hydrogen Bond Counts of Nitriles in SNase by Revealing Water-Protein Interaction in Vibrational Absorption Frequencies.The journal of physical chemistry. B · 2023Article
- AMOEBA Force Field Trajectories Improve Predictions of Accurate pThe journal of physical chemistry. B · 2022Article
- Developments and Ongoing Challenges for Analysis of Surface-Bound Proteins.Annual review of analytical chemistry (Palo Alto, Calif.) · 2021Article
- Surface Analysis: From Single Crystals to Biomaterials.Surface and interface analysis : SIA · 2018Article
- Application of advanced sampling and analysis methods to predict the structure of adsorbed protein on a material surface.Biointerphases · 2017Article
- Interplay between adsorbed peptide structure, trapped water, and surface hydrophobicity.Biointerphases · 2017Article
- Predicting the orientation of protein G B1 on hydrophobic surfaces using Monte Carlo simulations.Biointerphases · 2016Article
- Force fields for simulating the interaction of surfaces with biological molecules.Interface focus · 2016Review
- Perspectives on the simulation of protein-surface interactions using empirical force field methods.Colloids and surfaces. B, Biointerfaces · 2014Article
- Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.Methods in enzymology · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
All-atom empirical molecular mechanics protein force fields, which have been developed to represent the energetics of peptide folding behavior in aqueous solution, have not been parameterized for protein interactions with solid material surfaces. As a result, their applicability for representing the adsorption behavior of proteins with functionalized material surfaces should not be assumed. To address this issue, we conducted replica-exchange molecular dynamics simulations of the adsorption behavior of structured peptides to functionalized surfaces using three protein force fields that are widely used for the simulation of peptide adsorption behavior: CHARMM22, AMBER94, and OPLS-AA. Simulation results for peptide structure both in solution and when adsorbed to the surfaces were compared to experimental results for similar peptide-surface systems to provide a means of evaluating and comparing the performance of these three force fields for this type of application. Substantial differences in both solution and adsorbed peptide conformations were found amongst these three force fields, with the CHARMM22 force field found to most closely match experimental results.
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.