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Conserved Binding Sites

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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.
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Related Experiment Video

Updated: May 17, 2025

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

Pareto-optimal sampling for multi-objective protein sequence design.

Jiaqi Luo¹, Kerr Ding¹, Yunan Luo¹

¹School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA.

|March 31, 2025

Summary

This summary is machine-generated.

MosPro efficiently designs protein sequences with desired properties using machine learning. This generative approach navigates vast search spaces for novel functional protein design.

Keywords:

Behavioral neuroscience Cognitive neuroscience Neuroscience

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational biology
Protein engineering
Machine learning

Background:

Supervised machine learning (ML) excels at predicting protein properties from sequences.
Designing protein sequences with specific properties (inverse problem) is challenging due to large search spaces and complex fitness landscapes.

Purpose of the Study:

To introduce MosPro, an efficient ML algorithm for property-guided protein sequence design.
To address the under-explored inverse problem in protein design.

Main Methods:

Framing sequence design as a discrete sampling problem.
Utilizing a pre-trained differentiable ML model to predict sequence properties.
Shaping a probability distribution towards high-property sequences.
Employing Pareto optimization for multi-property sequence design.

Main Results:

MosPro efficiently samples sequences from a constructed distribution.
Pareto optimization successfully proposed sequences optimized for multiple properties.
Evaluations on experimental fitness landscapes confirmed MosPro's ability to balance multiple design desiderata.

Conclusions:

MosPro demonstrates significant potential for efficient and controllable functional protein design.
Generative ML offers powerful tools for tackling complex protein engineering challenges.