AF2Ring: A Memory Efficient Method for Predicting Quaternary Structure of Cyclic Protein Oligomers

Cyclic protein oligomers play vital roles in many biological processes and have captured the attention of protein designers. An important step of protein design is to predict the quaternary structure for a given primary sequence of a protein complex. In recent years, results from deep-learning-based methods for protein structure prediction are becoming increasingly accurate. Unfortunately, these methods are usually slow and require a large amount of memory, which can become extremely problematic for researchers, especially when facing the reverse problem: finding a sequence that folds into a required structure. In this paper, we present a new method, AF2Ring, that achieves accurate prediction with a moderate memory requirement for predicting quaternary structures of cyclic protein assemblies with repeating subunits. AF2Ring utilizes AlphaFold2 with a reduced number of chains as input and then uses the partial results to construct the complete quaternary structure. Our experimental results show that AF2Ring significantly reduced memory consumption and computation time, while achieving prediction accuracy comparable to running AlphaFold2 directly, when possible, on full-length protein sequences. This method facilitates protein design methods by providing large-scale validation of different designs and a longer sequence length allowance.