Evidence map›Paper›PMID 37066376›Full record

ArticlebioRxiv : the preprint server for biology2023

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.

Rafael F Guerrero, Tandin Dorji, Ra'Mal M Harris, Matthew D Shoulders, C Brandon Ogbunugafor

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Rafael F GuerreroDepartment of Biological Sciences, North Carolina State University.
Tandin DorjiDepartment of Mathematics and Statistics, University of Vermont, Burlington, VT.
Ra'Mal M HarrisDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA.
Matthew D ShouldersDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA.
C Brandon OgbunugaforDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA.

Funding

Defining the Interplay Between Viral Adaptation and Host ProteostasisR01AI168166 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Matthew Donald Shoulders · 2022 to 2026
$2.9M
Leveraging Next-Generation Directed Evolution Platforms and Chemical Control of Proteostasis to Deliver Robust Biotechnologies and Illuminate Roles of Chaperone Networks in Protein EvolutionR35GM136354 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI SHOULDERS, MATTHEW DONALD · 2020 to 2024
$2.1M
Computational tools and quantitative analyses of genome structure evolutionR35GM147107 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI Rafael F Guerrero · 2022 to 2026
$1.8M
NIAID NIH HHS R01 AI168166NIGMS NIH HHS R35 GM136354NIGMS NIH HHS R35 GM147107
6 · The paper itself

Abstract

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16

Identifiers

PMID37066376
PMCPMC10104179

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.