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Related Experiment Video

Updated: Jan 11, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Pretrained protein language models choose between sequence novelty and structural completeness.

Arjuna M Subramanian¹, Zachary A Martinez¹, Matt Thomson¹

¹Division of Biology and Biological Engineering, California Institute of Technology, CA, USA.

Biorxiv : the Preprint Server for Biology

|November 19, 2025

Summary

This summary is machine-generated.

Protein language models (PLMs) can generate novel sequences but struggle with structural diversity, often falling into a "helical bundle trap." New strategies are needed to guide PLMs toward generating diverse, biophysically valid protein designs.

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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
Artificial intelligence in biochemistry

Background:

Protein language models (PLMs) are increasingly used for protein design and variant effect prediction.
Understanding the biophysical rules and diversity of natural protein-space is crucial for advancing PLM capabilities.

Purpose of the Study:

To computationally profile and characterize the sequence and structure statistics of PLM-generated small proteins.
To assess the extent to which PLM outputs recapitulate natural protein diversity and biophysical rules.

Main Methods:

Free, unconstrained generation of hundreds of thousands of small proteins using architecturally distinct PLMs.
Computational profiling of sequence and structure statistics and properties.

Main Results:

PLMs demonstrate a high capacity for generating novel amino-acid sequences.
PLM-generated proteins exhibit limited structural variation compared to natural proteins.
A trade-off exists between sequence novelty and structural breadth, with a tendency towards a "helical bundle trap" outside natural sequence space.

Conclusions:

PLMs can generate novel sequences but struggle to replicate the structural diversity of natural proteins.
Current PLM design strategies may be limited by a tendency towards specific structural motifs.
Developing new methods is essential to guide PLMs in generating a broader range of biophysically valid protein structures and functions.