Evidence map›Paper›PMID 34432238›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3.

Danielle Allison Brackenridge, Liam James McGuffin

Abstract read
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Article in Methods in molecular biology (Clifton, N.J.), 2021. 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
–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

3 citing papers in PubMed.

  1. The Human Omnibus of Targetable Pockets.Journal of cheminformatics · 2025
    Article
  2. Article
  3. Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024
    Review
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

2 authors.

Danielle Allison BrackenridgeSchool of Biological Sciences, University of Reading, Reading, UK.
Liam James McGuffinSchool of Biological Sciences, University of Reading, Reading, UK. l.j.mcguffin@reading.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins are essential molecules with a diverse range of functions; elucidating their biological and biochemical characteristics can be difficult and time consuming using in vitro and/or in vivo methods. Additionally, in vivo protein-ligand binding site elucidation is unable to keep place with current growth in sequencing, leaving the majority of new protein sequences without known functions. Therefore, the development of new methods, which aim to predict the protein-ligand interactions and ligand-binding site residues directly from amino acid sequences, is becoming increasingly important. In silico prediction can utilise either sequence information, structural information or a combination of both. In this chapter, we will discuss the broad range of methods for ligand-binding site prediction from protein structure and we will describe our method, FunFOLD3, for the prediction of protein-ligand interactions and ligand-binding sites based on template-based modelling. Additionally, we will describe the step-by-step instructions using the FunFOLD3 downloadable application along with examples from the Critical Assessment of Techniques for Protein Structure Prediction (CASP) where FunFOLD3 has been used to aid ligand and ligand-binding site prediction. Finally, we will introduce our newer method, FunFOLD3-D, a version of FunFOLD3 which aims to improve template-based protein-ligand binding site prediction through the integration of docking, using AutoDock Vina.

Indexed as

Amino Acid SequenceBinding SitesLigandsProtein BindingProteinsLigandsProteinsCritical Assessment of Techniques for Protein Structure Prediction (CASP)DockingLigand-binding site predictionProtein–ligand interactionsTemplate-based modelling

Identifiers

PMID34432238

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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.