Evidence map›Paper›PMID 27787823›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2017

Assessing Predicted Contacts for Building Protein Three-Dimensional Models.

Badri Adhikari, Debswapna Bhattacharya, Renzhi Cao, Jianlin Cheng

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Badri AdhikariComputer Science Department, University of Missouri, Engineering Building West 109, Columbia, MO, 65211, USA.
Debswapna BhattacharyaComputer Science Department, University of Missouri, Engineering Building West 109, Columbia, MO, 65211, USA.
Renzhi CaoComputer Science Department, University of Missouri, Engineering Building West 109, Columbia, MO, 65211, USA.
Jianlin ChengComputer Science Department, University of Missouri, Engineering Building West 109, Columbia, MO, 65211, USA. chengji@missouri.edu.

Funding

Integrated Prediction of Protein Struture at 1D, 2D and 3D LevelsR01GM093123 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHENG, JIANLIN · 2010 to 2023
$3.8M
NIGMS NIH HHS R01 GM093123
6 · The paper itself

Abstract

Recent successes of contact-guided protein structure prediction methods have revived interest in solving the long-standing problem of ab initio protein structure prediction. With homology modeling failing for many protein sequences that do not have templates, contact-guided structure prediction has shown promise, and consequently, contact prediction has gained a lot of interest recently. Although a few dozen contact prediction tools are already currently available as web servers and downloadables, not enough research has been done towards using existing measures like precision and recall to evaluate these contacts with the goal of building three-dimensional models. Moreover, when we do not have a native structure for a set of predicted contacts, the only analysis we can perform is a simple contact map visualization of the predicted contacts. A wider and more rigorous assessment of the predicted contacts is needed, in order to build tertiary structure models. This chapter discusses instructions and protocols for using tools and applying techniques in order to assess predicted contacts for building three-dimensional models.

Indexed as

SoftwareAlgorithmsComputational BiologyDatabases, ProteinNeural Networks, ComputerProtein ConformationProtein FoldingProteinsSequence Analysis, ProteinProteinsContact-guided ab initio predictionProtein contact assessment

Identifiers

PMID27787823
PMCPMC5512107

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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.