Generative models of macromolecules carry abundant and impactful implications
for industrial and biomedical efforts in protein engineering. However, existing
methods are currently limited to modeling protein structures or sequences,
independently or jointly, without regard to the interactions that commonly
occur between proteins and other macromolecules. In this work, we introduce
MMDiff, a generative model that jointly designs sequences and structures of
nucleic acid and protein complexes, independently or in complex, using joint
SE(3)-discrete diffusion noise. Such a model has important implications for
emerging areas of macromolecular design including structure-based transcription
factor design and design of noncoding RNA sequences. We demonstrate the utility
of MMDiff through a rigorous new design benchmark for macromolecular complex
generation that we introduce in this work. Our results demonstrate that MMDiff
is able to successfully generate micro-RNA and single-stranded DNA molecules
while being modestly capable of joint modeling DNA and RNA molecules in
interaction with multi-chain protein complexes. Source code:
https://github.com/Profluent-Internships/MMDiff.
Generative Models of Macromolecules: Implications for Protein Engineering
When it comes to protein engineering, generative models of macromolecules have proven to be a valuable tool. These models allow scientists to design and manipulate protein structures and sequences to create novel and improved proteins with desired functions. However, existing methods have their limitations as they often focus only on protein structures or sequences, without taking into account the interactions that occur between proteins and other macromolecules.
In this groundbreaking work, a team of researchers introduces MMDiff, a generative model that goes beyond traditional approaches by jointly designing sequences and structures of nucleic acid and protein complexes. This is achieved through the use of joint SE(3)-discrete diffusion noise, a technique that enables the model to capture the interactions between proteins and other macromolecules.
The multi-disciplinary nature of this research is truly remarkable. By combining concepts from molecular biology, protein engineering, and computer science, the researchers have created a powerful tool that has wide-ranging implications in various areas of macromolecular design.
One such application is in structure-based transcription factor design. Transcription factors play a critical role in gene regulation, and being able to design new transcription factors with precise binding preferences can have significant implications in various fields, including medicine and biotechnology. MMDiff opens up new possibilities by allowing researchers to not only design the sequence of the transcription factor but also its three-dimensional structure, taking into account its interactions with DNA or RNA molecules.
The design of noncoding RNA sequences is another area where MMDiff can make a substantial impact. Noncoding RNAs are involved in a wide range of biological processes and have been found to play important roles in diseases such as cancer. By enabling the joint modeling of nucleic acid and protein complexes, MMDiff provides a powerful tool for designing noncoding RNA sequences that can interact specifically with target proteins, opening up new avenues for therapeutic interventions.
To demonstrate the effectiveness of MMDiff, the researchers introduce a rigorous benchmark for macromolecular complex generation. This benchmark evaluates the model’s ability to generate various types of macromolecules, including micro-RNA, single-stranded DNA molecules, and multi-chain protein complexes. The results are promising, showing that MMDiff is not only capable of successfully generating micro-RNA and single-stranded DNA molecules but also has some capability in modeling DNA and RNA molecules in interaction with multi-chain protein complexes.
The release of the source code for MMDiff on GitHub further illustrates the collaborative and open nature of this research. By making their code accessible to the scientific community, the researchers encourage further exploration and improvement of their approach, paving the way for future advancements and discoveries in the field of macromolecular design.