Evidence map›Paper›PMID 37309388›Full record

ArticleACS omega2023

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2.

Sarah H Sandholtz, Jeffrey A Drocco, Adam T Zemla, Marisa W Torres, Mary S Silva, Jonathan E Allen

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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 1 institution in 1 country.

Sarah H SandholtzBiosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.ORCID https://orcid.org/0000-0001-6605-4931
Jeffrey A DroccoBiosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.
Adam T ZemlaGlobal Security Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.ORCID https://orcid.org/0000-0003-0504-8647
Marisa W TorresGlobal Security Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.
Mary S SilvaGlobal Security Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.
Jonathan E AllenGlobal Security Computing Applications Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America.ORCID https://orcid.org/0000-0002-4359-8263
Lawrence Livermore National Laboratory · US

Funding

ChimeraX -- Next Generation Visualization and Analysis Software for Multiscale ModelingR01GM129325 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2018 to 2025
$5.2M
NIGMS NIH HHS R01 GM129325
6 · The paper itself

Abstract

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Identifiers

PMID37309388
PMCPMC10254058
OpenAlexW4379534313

What OpenQuestion 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 OpenQuestion graph.