3D structural analysis of proteins using electrostatic surfaces based on image segmentation.

Dimitrios Vlachakis, Spyridon Champeris Tsaniras, Georgia Tsiliki, Vasileios Megalooikonomou, Sophia Kossida

Abstract read

In one paragraph

Article in Journal of molecular biochemistry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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

1 citing paper in PubMed.

DNA Replication Inhibitor Geminin and Retinoic Acid Signaling Participate in Complex Interactions Associated With Pluripotency.Cancer genomics & proteomics
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

5 authors.

Dimitrios VlachakisBiomedical Research Foundation of the Academy of Athens, 11527, Athens, Greece; Bionetwork ltd. 15234, Chalandri, Athens, Greece; Computer Engineering and Informatics Department, School of Engineering, University of Patras, 26500 Patras, Greece.

Spyridon Champeris TsanirasBionetwork ltd. 15234, Chalandri, Athens, Greece; Department of Physiology, Medical School, University of Patras, 26500 Patras, Greece.

Georgia TsilikiBiomedical Research Foundation of the Academy of Athens, 11527, Athens, Greece.

Vasileios MegalooikonomouComputer Engineering and Informatics Department, School of Engineering, University of Patras, 26500 Patras, Greece.

Sophia KossidaBiomedical Research Foundation of the Academy of Athens, 11527, Athens, Greece.

Funding

Large Scale Data Analysis for Brain ImagesR01MH068066 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI MEGALOOIKONOMOU, VASILEIOS · 2004 to 2007

$1.3M

NIMH NIH HHS R01 MH068066

6 · The paper itself

Abstract

Herein, we present a novel strategy to analyse and characterize proteins using protein molecular electro-static surfaces. Our approach starts by calculating a series of distinct molecular surfaces for each protein that are subsequently flattened out, thus reducing 3D information noise. RGB images are appropriately scaled by means of standard image processing techniques whilst retaining the weight information of each protein's molecular electrostatic surface. Then homogeneous areas in the protein surface are estimated based on unsupervised clustering of the 3D images, while performing similarity searches. This is a computationally fast approach, which efficiently highlights interesting structural areas among a group of proteins. Multiple protein electrostatic surfaces can be combined together and in conjunction with their processed images, they can provide the starting material for protein structural similarity and molecular docking experiments.

Identifiers

PMID27525250

PMCPMC4981338

What OpenQuestion holds

Textmetadata

LicenceTDM

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.