  Protein loop modeling is the most challenging yet highly non-trivial task in
protein structure prediction. Despite recent progress, existing methods
including knowledge-based, ab initio, hybrid and deep learning (DL) methods
fall significantly short of either atomic accuracy or computational efficiency.
Moreover, an overarching focus on backbone atoms has resulted in a dearth of
attention given to side-chain conformation, a critical aspect in a host of
downstream applications including ligand docking, molecular dynamics simulation
and drug design. To overcome these limitations, we present KarmaLoop, a novel
paradigm that distinguishes itself as the first DL method centered on full-atom
(encompassing both backbone and side-chain heavy atoms) protein loop modeling.
Our results demonstrate that KarmaLoop considerably outperforms conventional
and DL-based methods of loop modeling in terms of both accuracy and efficiency,
with the average RMSD improved by over two-fold compared to the second-best
baseline method across different tasks, and manifests at least two orders of
magnitude speedup in general. Consequently, our comprehensive evaluations
indicate that KarmaLoop provides a state-of-the-art DL solution for protein
loop modeling, with the potential to hasten the advancement of protein
engineering, antibody-antigen recognition, and drug design.
