Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Amino acids03:42

Amino acids

111.5K
Amino acids are the monomers that comprise proteins. Each amino acid has the same fundamental structure, which consists of a central carbon atom, or the alpha (α) carbon, bonded to an amino group (NH2), a carboxyl group (COOH), and to a hydrogen atom. Every amino acid also has another atom or group of atoms bonded to the central atom known as the R group. There are 20 common amino acids present in proteins, each with a different R group. Variation in the amino acid sequence is responsible for...
111.5K
Leaky Scanning02:28

Leaky Scanning

5.9K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.9K
From DNA to Protein03:06

From DNA to Protein

24.9K
The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
24.9K
Mutations01:39

Mutations

98.2K
Overview
98.2K
Mutations01:35

Mutations

45.8K
Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
45.8K
Conserved Binding Sites01:49

Conserved Binding Sites

5.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Twelve-year trends in sex and age of patients with COPD and chronic respiratory failure undergoing pulmonary rehabilitation.

European journal of internal medicine·2026
Same author

Why almost all ML models for medicine are wrong-and what we need for evidence-based medical AI.

International journal of medical informatics·2026
Same author

Evaluating large language models for structuring cardiology reports: a real-world clinical study on patient subtyping and trial recruitment.

International journal of medical informatics·2026
Same author

Consistency in causal reasoning for large language models in scenarios of HIV antiretroviral treatment, drug interactions, and side effects.

NPJ digital medicine·2026
Same author

ViTMARE - A Vision Transformer Pipeline for Anomaly Detection in 3D Brain MRI.

Studies in health technology and informatics·2026
Same author

Environmental Personal Exposure Clusters to Investigate Multiple Sclerosis and Amyotrophic Lateral Sclerosis Progression.

Studies in health technology and informatics·2026
Same journal

Model-based quantification of protein-protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels.

BMC bioinformatics·2026
Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026
Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 13, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.8K

PaPI: pseudo amino acid composition to score human protein-coding variants.

Ivan Limongelli1,2, Simone Marini3, Riccardo Bellazzi4

  • 1IRCCS Policlinico S. Matteo, Pzz.le Volontari del Sangue 2, 27100, Pavia, Italy. ivan.limongelli@unipv.it.

BMC Bioinformatics
|May 1, 2015
PubMed
Summary
This summary is machine-generated.

PaPI is a machine-learning tool that predicts the impact of human genetic variations on protein function. It accurately scores coding variants, including complex ones, to identify potential disease-causing mutations.

More Related Videos

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

4.2K

Related Experiment Videos

Last Updated: Apr 13, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.8K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K
An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

4.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing identifies extensive genomic variations.
  • Analyzing the functional impact of these variants is computationally challenging.
  • Current methods often focus on coding sequences and single nucleotide changes.

Purpose of the Study:

  • To develop a novel machine-learning approach for classifying and scoring human coding variants.
  • To estimate the probability of variants damaging protein function.
  • To identify potential disease-associated genetic variations.

Main Methods:

  • Utilized pseudo amino acid composition to represent wild and mutated protein sequences in a discrete model.
  • Trained a machine learning classifier on known deleterious and benign coding variants.
  • Integrated amphiphilic pseudo amino acid composition, evolutionary conservation, and homologous protein data.

Main Results:

  • The PaPI approach accurately classifies and scores human coding variants.
  • Demonstrated superior performance compared to several existing prediction algorithms.
  • Successfully scored complex variants like deletions, insertions, and indels.

Conclusions:

  • A machine-learning method, PaPI, effectively predicts the deleteriousness of human coding variants.
  • A publicly accessible web application is available for scoring thousands of variants.
  • The tool aids in understanding the functional consequences of genomic variations.