FakeRotLib: expedient non-canonical amino acid parameterization in Rosetta
Bell, E. W.; Brown, B. P.; Meiler, J.
Show abstract
Non canonical amino acids (NCAAs) occupy an important place, both in natural biology and synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training datasets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs. We discuss the various aspects of parameterizing a NCAA for use in Rosetta, identifying rotamer distribution modeling as one of the most impactful factors of NCAA parameterization on Rosetta performance. To this end, we also present FakeRotLib, a method which uses statistical fitting of small molecule conformer to create rotamer distributions. We find that FakeRotLib outperforms existing methods in a fraction of the time and is able to parameterize NCAA types previously unmodeled by Rosetta.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- RosENet: Improving binding affinity prediction by leveraging molecular mechanics energies with a 3D Convolutional Neural Network 96%
- CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials 96%
- Are Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures 96%
Similar papers in this journal
- MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics 97%
- CG2AT2: An Enhanced Fragment-based approach for Serial Multi-scale Molecular Dynamics simulations 96%
- Learning a force field from small-molecule crystal lattice predictions enables consistent sub-Angstrom protein-ligand docking 96%
Similar papers in this journal
- Protein Structure Refinement Guided by Atomic Packing Frustration Analysis 97%
- FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution 96%
- Coevolution-driven method for efficiently simulating conformational changes in proteins reveals molecular details of ligand effects in the beta2AR receptor 96%
Similar papers in this journal
- SMOG 2 and OpenSMOG: Extending the limits of structure-based models 96%
- ExploreTurns: A web tool for the exploration, analysis, and classification of beta turns and structured loops in proteins; application to beta-bulge and Schellman loops, Asx helix caps, beta hairpins and other hydrogen-bonded motifs 95%
- A geometric parameterization for beta turns 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.