Skip to content

Study · Not a test of the peptide alone · 2016

1 result

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

Jamal S, Goyal S, Shanker A, Grover A

BMC genomics18 Oct 2016Cited by 27 (Europe PMC)

PMID27756223 (opens in a new tab)PMCIDPMC5070370 (opens in a new tab)DOI10.1186/s12864-016-3108-1 (opens in a new tab)PepBankPUB-27756223
Download PDFPubMedAsk about this study
Jump to 1 result

Summary

BackgroundAlzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics.ResultsIn the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings.ConclusionsTo the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.
CC BYText from Europe PMC. Highlighted sentences are the source of results.

Results from this study1

No result from this study comes from a test of the peptide.

Studies that did not test the peptide alone1

In this study, Davunetide was a lab tool or a carrier, part of a mix, or the subject of a lab method. These results are not evidence about taking Davunetide.

  1. 01Computer modelNot a test of Davunetide aloneIn silicoActivity measurement

    In computer docking models, AL-108 showed high affinity for most predicted Alzheimer therapeutic targets.

    In plain wordsIn computer models, AL-108 showed high affinity for most predicted Alzheimer treatment targets.

    Peptide
    Davunetide
    Outcome
    Not applicablePredicted target affinity
    Study

    n: not reported in source

    predicted Alzheimer therapeutic targets
    Outcome group
    Receptor activity and mechanism · Pharmacology

    Source sentences

    Summary
    We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets.
    1 context sentence
    Summary · context
    In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations.

    In this study, Davunetide was a lab tool or a carrier, part of a mix, or the subject of a lab method. This result is not evidence about taking Davunetide.

AI wrote and checked these results. AI can make mistakes, so check important results against the source.

Version history

PepBank keeps every version of the data on this study. Status changes from Retraction Watch, Europe PMC and Crossref are listed with their source.

  1. Version 2Current

    27 Oct 2026, 18:20 UTC

    Study results updated

  2. Version 1

    27 Oct 2026, 16:32 UTC

    First added · source update from Europe PMC

Subjects

Terms as the source lists them.

PepBank is a research reference. Talk to a doctor before you use any peptide.