Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,344

The DOI matches its Crossref record.

Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence

Guruprasad, Kunchur, Reddy, B.V.Bhasker, Pandit, Madhusudan W.·1990·"Protein Engineering, Design and Selection"

Abstract

Statistical analysis of 12 unstable and 32 stable proteins revealed that there are certain dipeptides, the occurrence of which is significantly different in the unstable proteins compared with those in the stable ones. Based on the impact of these dipeptides on the unstable proteins over the stable ones, a weight value of instability is assigned to each of the dipeptides. For a given protein the summation of these weight values normalized to the length of its sequence helps to distinguish between unstable and stable proteins. Results suggest that the in vivo instability of proteins is possibly determined by the order of certain amino acids in its sequence. An attempt is made to correlate metabolic stability of proteins with features of their primary sequence where weight values of instability for a protein of known sequence could thus be used as an index for predicting its stability characteristics.

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 · 10.1093/protein/4.2.155

Cites

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

Cited by

See all 1,344

Metadata

Authors
Guruprasad, K., Reddy, B. V. B., & Pandit, M. W.
Venue
"Protein Engineering, Design and Selection"
Published
1990
Volume
4
Issue
2
Pages
155-161
ISSN
  • 0269-2139 (ISSN-L)
  • 1741-0126
  • 1741-0134
OpenAlex
W2057021995
Cited by
1,344
References
0
FWCI
0.41
Type
journal article
Language
en
Open access
closed

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: Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. https://app.cochat.ai/papers/10.1093/protein/4.2.155
The paper
Guruprasad, K., Reddy, B. V. B., & Pandit, M. W. (1990). Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. "Protein Engineering, Design and Selection". https://doi.org/10.1093/protein/4.2.155

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.