Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 164

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Molecular simulation of protein-surface interactions: Benefits, problems, solutions, and future directions (Review)

Open access
Latour, Robert A.·2008·Biointerphases

Abstract

While the importance of protein adsorption to materials surfaces is widely recognized, little is understood at this time regarding how to design surfaces to control protein adsorption behavior. All-atom empirical force field molecular simulation methods have enormous potential to address this problem by providing an approach to directly investigate the adsorption behavior of peptides and proteins at the atomic level. As with any type of technology, however, these methods must be appropriately developed and applied if they are to provide realistic and useful results. Three issues that are particularly important for the accurate simulation of protein adsorption behavior are the selection of a valid force field to represent the atomic-level interactions involved, the accurate representation of solvation effects, and system sampling. In this article, each of these areas is addressed and future directions for continued development are presented.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1116/1.2965132

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 164

Metadata

Authors
Latour, R. A.
Venue
Biointerphases
Published
2008-09-01
Volume
3
Issue
3
Pages
FC2-FC12
ISSN
  • 1559-4106 (ISSN-L)
  • 1934-8630
OpenAlex
W2002459596
Cited by
164
References
1
FWCI
4.94
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Molecular simulation of protein-surface interactions: Benefits, problems, solutions, and future directions (Review). https://app.cochat.ai/papers/10.1116/1.2965132
The paper
Latour, R. A. (2008). Molecular simulation of protein-surface interactions: Benefits, problems, solutions, and future directions (Review). Biointerphases. https://doi.org/10.1116/1.2965132

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.