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Abstract Protein side-chain packing (PSCP), the problem of predicting side-chain conformations given a fixed backbone structure, has important implications in the modeling of structures and interactions. However, despite the groundbreaking progress in protein structure prediction pioneered by AlphaFold, the existing PSCP methods still rely on experimental inputs, and do not leverage AlphaFold-predicted backbone coordinates to enable PSCP at scale. Here, we perform a large-scale benchmarking of the predictive performance of various PSCP methods on public datasets from multiple rounds of the Critical Assessment of Structure Prediction challenges using a diverse set of evaluation metrics. Empirical results demonstrate that the PSCP methods perform well in packing the side-chains with experimental inputs, but they fail to generalize in repacking AlphaFold-generated structures. We additionally explore the effectiveness of leveraging the self-assessment confidence scores from AlphaFold by implementing a backbone confidence-aware integrative approach. While such a protocol often leads to performance improvement by attaining modest yet statistically significant accuracy gains over the AlphaFold baseline, it does not yield consistent and pronounced improvements. Our study highlights the recent advances and remaining challenges in PSCP in the post-AlphaFold era.
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This content will become publicly available on January 12, 2026
Enhancing Protein Side Chain Packing Using Rotamer Clustering and Machine Learning
One of the challenges and a significant part of a protein structure’s prediction in three-dimensional space is a side chain prediction/packing. This area of research has a large importance, due to its various applications in protein design. In recent years, many methodologies and techniques have been crafted for side chain prediction such as DLPacker, FASPR, SCWRL4 and OPUS-Rota4. In this research, we address the problem from a different perspective. We employed a machine learning model to predict the side chain packing of protein molecules given only the Cα trace. We analyzed 32,000 protein molecules to extract important geometrical features that can distinguish between different orientations of side chain rotamers. We designed and implemented a Random Forest model to tackle this problem. Given the accuracy of existing state-of-the-art approaches, our model represents an improvement from among other models. The results of our experiment show that Random Forest is highly effective, achieving a total average accuracy of 73.7% for proteins and 73.3% for individual amino acids.
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- Award ID(s):
- 2409093
- PAR ID:
- 10608013
- Publisher / Repository:
- 13th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2025)
- Date Published:
- Journal Name:
- Lecture notes in computer science
- ISSN:
- 1611-3349
- Format(s):
- Medium: X
- Location:
- Atlanta, Georgia
- Sponsoring Org:
- National Science Foundation
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