Evidence map›Paper›PMID 25237390›Full record

ArticleBMC proceedings2014

FixingTIM: interactive exploration of sequence and structural data to identify functional mutations in protein families.

Timothy Luciani, John Wenskovitch, Koonwah Chen, David Koes, Timothy Travers, G Elisabeta Marai

Open access · goldAbstract read
In one paragraph

Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 39% of its field
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

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.Computer graphics forum : journal of the European Association for Computer Graphics · 2026
    Article
  2. Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization.IEEE transactions on visualization and computer graphics · 2018
    Article
  3. Article
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

6 authors at 3 institutions in 1 country.

Timothy LucianiDepartment of Computer Science, Brown University, Box 1910, 02912 Providence, RI, US.
John WenskovitchDepartment of Computer Science, University of Pittsburgh, 210 South Bouquet, 15260 Pittsburgh, PA, US.
Koonwah ChenSchool of Information Science, University of Pittsburgh, 135 North Bellefield Avenue, 15260 Pittsburgh, PA, US.
David KoesDepartment of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, 15260 Pittsburgh, PA, US.
Timothy TraversDepartment of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, 15260 Pittsburgh, PA, US.
G Elisabeta MaraiDepartment of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., 60607 Chicago, IL, US.
University of Pittsburgh · USBrown University · USUniversity of Illinois Chicago · US

Funding

Integrated Interdisciplinary, inter-university PhD Program Computational Biology (T32)T32EB009403 · NIBIB · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, BAR-JOSEPH, ZIV · 2009 to 2023
$4.4M
NIBIB NIH HHS T32 EB009403
6 · The paper itself

Abstract

backgroundKnowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease.

methodsWe present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface; multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues.

resultsWe evaluate our approach on the triosephosphate isomerase (TIM) family structural models and sequence data and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins.

conclusionsThe TIM application shows that our tool can assist in the navigation of families of proteins, as well as in the exploration of individual protein structures. In conjunction with domain expert knowledge, this interactive tool can help provide biophysical insight into why specific mutations affect function and potentially suggest additional modifications to the protein that could be used to rescue functionality.

Identifiers

PMID25237390
PMCPMC4155608
OpenAlexW2129520735

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.