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Conditional Invariances for Conformer Invariant Protein Representations
Balasubramaniam Srinivasan · Vassilis Ioannidis · Soji Adeshina · Mayank Kakodkar · George Karypis · Bruno Ribeiro

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in AI for Science: Progress and Promises »
Event URL: https://openreview.net/forum?id=0TUa6mo164 »

Representation learning for proteins is an emerging area in geometric deep learning. Recent works have factored in both the relational (atomic bonds) and the geometric aspects (atomic positions) of the task, notably bringing together graph neural networks (GNNs) with neural networks for point clouds. The equivariances and invariances to geometric transformations (group actions such as rotations and translations) so far treats large molecules as rigid structures. However, in many important settings, proteins can co-exist as an ensemble of multiple stable conformations. The conformations of a protein, however, cannot be described as input-independent transformations of the protein: Two proteins may require different sets of transformations in order to describe their set of viable conformations. To address this limitation, we introduce the concept of conditional transformations (CT). CT can capture protein structure, while respecting the restrictions posed by constraints on dihedral (torsion) angles and steric repulsions between atoms. We then introduce a Markov chain Monte Carlo framework to learn representations that are invariant to these conditional transformations. Our results show that endowing existing baseline models with these conditional transformations helps improve their performance without sacrificing computational cost.

Keywords: graph neural networks · Invariances · proteins · conformer invariant representations · protein representation learning · group invariant neural networks · input dependent invariances · Conditional Invariances

Author Information

Balasubramaniam Srinivasan (Amazon)
Vassilis Ioannidis (University of Minnesota, Minneapolis)
Soji Adeshina (University of California Berkeley)
Mayank Kakodkar (Purdue University)

I am a Research Assistant and Doctoral Student at Purdue University, West Lafayette, Indiana. I work with Prof. Bruno Ribeiro on topics related to Generative Models and MCMC. The rest of my information is listed on my Linkedin profile.

George Karypis (University of Minnesota, Minneapolis)
Bruno Ribeiro (Purdue)

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