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

Group Design02:01

Group Design

10.3K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.3K
Factorial Design02:01

Factorial Design

13.7K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.7K
Design Example: Designing a Residential Plumbing System01:25

Design Example: Designing a Residential Plumbing System

1.1K
The design of residential plumbing systems requires carefully evaluating water demand, flow rates, and pressure dynamics to ensure both efficiency and reliability. The nature of water flow within pipes is defined by its Reynolds number, which classifies flow as either laminar (smooth) or turbulent.
1.1K
Design Example: Designing Water Slide01:18

Design Example: Designing Water Slide

620
When designing a water slide, controlling the speed of water flow is crucial for rider safety while maintaining an exciting experience. As water flows down the slide, gravity causes it to accelerate, with its speed at the bottom depending on the height from which it starts. The higher the slide, the more potential energy the water has at the top, which is converted into kinetic energy as it descends, increasing its speed.
Bernoulli's principle determines the water's velocity along the slide....
620
Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

782
Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
782
Design Example01:23

Design Example

531
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
531

You might also read

Related Articles

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

Sort by
Same author

Enhancing Outcome Measurement in Oncology Clinical Trials Through Artificial Intelligence: A Scoping Review.

JCO clinical cancer informatics·2026
Same author

Olaparib in HR-deficient, metastatic triple-negative breast and platinum-sensitive relapsed ovarian cancers without germline mutations in BRCA1/2: phase 2 EMBRACE trial.

British journal of cancer·2026
Same author

Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer.

Nature communications·2026
Same author

The Landscape of Prostate Tumour Methylation.

Cancer discovery·2026
Same author

The multifaceted roles of receptor tyrosine pseudokinases in cellular signalling.

Biochemical Society transactions·2026
Same author

The E3-ome gene-centric compendium reveals the human E3 ligase landscape.

Cell·2026

Related Experiment Video

Updated: Jan 22, 2026

Fabrication and Design of Wood-Based High-Performance Composites
08:08

Fabrication and Design of Wood-Based High-Performance Composites

Published on: November 9, 2019

14.0K

ProteinDJ: A high-performance and modular protein design pipeline.

Dylan Silke1,2, Julie Iskander1,2, Junqi Pan1,2

  • 1The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

Protein Science : a Publication of the Protein Society
|January 21, 2026
PubMed
Summary

ProteinDJ is a new open-source pipeline for designing synthetic proteins using artificial intelligence. This high-performance computing workflow accelerates the discovery of novel protein binders by parallelizing design and testing processes.

Keywords:
Nextflow pipelinebinder designde novo proteinshigh‐performance computingprotein design

More Related Videos

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
06:28

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit

Published on: September 2, 2025

751
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.2K

Related Experiment Videos

Last Updated: Jan 22, 2026

Fabrication and Design of Wood-Based High-Performance Composites
08:08

Fabrication and Design of Wood-Based High-Performance Composites

Published on: November 9, 2019

14.0K
Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
06:28

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit

Published on: September 2, 2025

751
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.2K

Area of Science:

  • Computational Biology
  • Protein Engineering
  • Artificial Intelligence in Drug Discovery

Background:

  • De novo protein design using deep learning enables exploration of novel structural landscapes.
  • This approach facilitates the creation of custom binders for specific protein targets.
  • Current methods often face low in silico success rates and scalability limitations.

Purpose of the Study:

  • To develop an efficient, open-source protein design pipeline for high-performance computing (HPC) systems.
  • To address the limitations of workstation-based implementations in terms of scalability and throughput.
  • To accelerate the discovery of synthetic protein binders through parallelized workflows.

Main Methods:

  • ProteinDJ utilizes Nextflow and Apptainer for HPC deployment.
  • The pipeline integrates tools like RFdiffusion/BindCraft for fold generation and ProteinMPNN/FAMPNN for sequence design.
  • It incorporates AlphaFold2/Boltz-2 for prediction and validation, alongside structural evaluation packages.

Main Results:

  • ProteinDJ parallelizes workloads across GPUs and CPUs for efficient processing.
  • The pipeline enables the generation and testing of hundreds of protein designs per hour.
  • It offers a modular and robust framework for synthetic protein binder generation.

Conclusions:

  • ProteinDJ democratizes synthetic protein binder design by providing a user-friendly HPC implementation.
  • The workflow significantly accelerates the discovery process compared to existing methods.
  • It serves as a foundational framework for future advancements in protein design pipelines.