ReviewMarine drugs2019
Snails In Silico: A Review of Computational Studies on the Conopeptides.
Review in Marine drugs, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- From Marine Peptides to Oncology: The Therapeutic Potential of Conotoxins in Cancer Treatment.Chemistry & biodiversity · 2026Review
- Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning.ACS chemical neuroscience · 2025Article
- Diverse toxins exhibit a common binding mode to the nicotinic acetylcholine receptors.Biophysical journal · 2025Article
- In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024Review
- Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.Journal of chemical theory and computation · 2023Article
- Inferring Pathways of Oxidative Folding from Prefolding Free Energy Landscapes of Disulfide-Rich Toxins.The journal of physical chemistry. B · 2023Article
- Bibliometric Review of the Literature on Cone Snail Peptide Toxins from 2000 to 2022.Marine drugs · 2023Review
- Cone snail species off the Brazilian coast and their venoms: a review and update.The journal of venomous animals and toxins including tropical diseases · 2023Review
- High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development.Biodesign research · 2022Article
- Absolute Structure Determination and Kv1.5 Ion Channel Inhibition Activities of New Debromoaplysiatoxin Analogues.Marine drugs · 2021Article
- Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination.Marine drugs · 2020Article
- Editorial: Chemical Design and Biomedical Applications of Disulfide-rich Peptides: Challenges and Opportunities.Frontiers in chemistry · 2020Article
- ConoMode, a database for conopeptide binding modes.Database : the journal of biological databases and curation · 2020Article
- Conotoxin Diversity in the Venom Gland Transcriptome of the Magician's Cone,Marine drugs · 2019Article
- Structure, Function, and Therapeutic Potential of Marine Bioactive Peptides.Marine drugs · 2019Article
- Transcriptomic Analysis of Marine GastropodMarine drugs · 2019Article
- Structural and Functional Analyses of Cone Snail Toxins.Marine drugs · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.