Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Conserved Binding Sites

Conserved Binding Sites

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...

Conservation of Protein Domains

Conservation of Protein Domains

You might also read

Related Articles

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

Sort by

Same author

ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries.

Nature communications·2026

Same author

Efficient generation of epitope-targeted antibodies with Germinal.

Nature biotechnology·2026

Same author

De novo design of RNA pseudoknots with deep learning.

bioRxiv : the preprint server for biology·2026

Same author

A generalizable system for antigenic peptide targeting across HLA-I allotypes.

bioRxiv : the preprint server for biology·2026

Same author

Zero-shot design of a <i>de novo</i> metalloenzyme.

bioRxiv : the preprint server for biology·2026

Same author

Genome modelling and design across all domains of life with Evo 2.

Nature·2026

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.

Talal Widatalla^1,2, Richard W Shuai¹, Brian L Hie^2,3,4

¹Department of Biophysics, Stanford University, Stanford, CA.

Proceedings of Machine Learning Research

|November 27, 2025

Summary

We introduce FAMPNN (Full-Atom MPNN), a novel deep learning method for protein sequence design. FAMPNN explicitly models sidechain conformation, improving sequence recovery and achieving state-of-the-art protein packing and binding predictions.

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jan 10, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational Biology
Protein Engineering
Deep Learning

Background:

Current deep learning methods for protein sequence design often neglect explicit modeling of sidechain conformation.
Sidechain conformation is critical for protein structure, stability, and function.
Existing models infer sidechain interactions indirectly from backbone geometry and sequence labels.

Purpose of the Study:

To develop a deep learning method that explicitly models both protein sequence identity and sidechain conformation.
To improve the accuracy and applicability of computational protein design.

Main Methods:

Introduction of FAMPNN (Full-Atom MPNN), a novel graph neural network architecture.
Jointly learning discrete amino acid identity and continuous sidechain conformation using a combined categorical cross-entropy and diffusion loss objective.
Utilizing full-atom representations for each residue during sequence generation.

Main Results:

FAMPNN demonstrates improved sequence recovery compared to existing methods.
The method achieves state-of-the-art performance in sidechain packing.
Benefits of full-atom modeling extend to accurate zero-shot prediction of experimental binding and stability.

Conclusions:

Explicitly modeling sidechain conformation alongside sequence identity is a synergistic approach that enhances protein design.
FAMPNN offers a more comprehensive and accurate method for computational protein sequence design.
The developed method has practical implications for predicting protein biophysical properties.