Evidence map›Paper›PMID 33538585›Full record

ReviewACS nano2021

Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery.

Marcelo D T Torres, Jicong Cao, Octavio L Franco, Timothy K Lu, Cesar de la Fuente-Nunez

Open access · greenAbstract readReview
In one paragraph

Review in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
10.7field-weighted citation impact, top 1% 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

58 citing papers in PubMed, 110 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Trojan Horse Strategy: How Biomimetic Nanomedicine Remodels the Tumor Microenvironment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Discovery of a Novel Antimicrobial Peptide fromAntibiotics (Basel, Switzerland) · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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 at 3 institutions in 2 countries.

Marcelo D T TorresMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
Jicong CaoSynthetic Biology Group, MIT Synthetic Biology Center, Department of Biological Engineering and Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-5253-3365
Octavio L FrancoCentro de Análises Proteômicas e Bioquímicas, Universidade Católica de Brasília, Brasília, DF 70790160, Brazil.ORCID 0000-0001-9546-0525
Timothy K LuSynthetic Biology Group, MIT Synthetic Biology Center, Department of Biological Engineering and Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.ORCID 0000-0002-3918-8923
Cesar de la Fuente-NunezMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.ORCID 0000-0002-2005-5629
Massachusetts Institute of Technology · USTranslational Therapeutics (United States) · USUniversidade Católica de Brasília · BR

Funding

Combining chemical and computational tools for predictive models of microbiome communitiesR35GM138201 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI DE LA FUENTE, CESAR · 2020 to 2024
$1.8M
NIGMS NIH HHS R35 GM138201
6 · The paper itself

Abstract

Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and,

Indexed as

Antimicrobial Cationic PeptidesSynthetic BiologyAnti-Bacterial AgentsComputersHumansPore Forming Cytotoxic ProteinsAnti-Bacterial AgentsAntimicrobial Cationic PeptidesPore Forming Cytotoxic Proteinsantimicrobial peptidescomputational biologymolecular design frameworkspeptide chemistrypeptide designpeptide discoveryrational designsynthetic biology

Identifiers

PMID33538585
PMCPMC8734659
OpenAlexW3127026236

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.