Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO
This work addresses scaling challenges in computational protein design, which is important for researchers in bioinformatics and structural biology, but it appears incremental as it builds directly on an existing algorithm.
The authors tackled the scalability issue of the AOBB-K* protein re-design algorithm by introducing three new versions (AOBB-K*-b, AOBB-K*-DH, and AOBB-K*-UFO) that significantly enhance its performance.
Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks. However, certain important tasks are less amenable to these technologies, benefiting from innovations to traditional inference schemes. One such task is protein re-design. Recently a new re-design algorithm, AOBB-K*, was introduced and was competitive with state-of-the-art BBK* on small protein re-design problems. However, AOBB-K* did not scale well. In this work we focus on scaling up AOBB-K* and introduce three new versions: AOBB-K*-b (boosted), AOBB-K*-DH (with dynamic heuristics), and AOBB-K*-UFO (with underflow optimization) that significantly enhance scalability.