TY - GEN
T1 - Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model
AU - Thygesen, Christian B.
AU - Al-Sibahi, Ahmad Salim
AU - Steenmanns, Christian S.
AU - Sanz Moreta, Lys
AU - Sørensen, Anders B.
AU - Hamelryck, Thomas Wim
PY - 2021
Y1 - 2021
N2 - Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectively due to the dynamic nature of proteins and experimental errors in protein structures. Additionally, they typically rely on information that is not generally or readily available, such as homologous sequences, related protein structures and other complementary information. To address these issues, we developed BIFROST, a novel take on the fragment library problem based on a Deep Markov Model architecture combined with directional statistics for angular degrees of freedom, implemented in the deep probabilistic programming language Pyro. BIFROST is a probabilistic, generative model of the protein backbone dihedral angles conditioned solely on the amino acid sequence. BIFROST generates fragment libraries with a quality on par with current state-of-the-art methods at a fraction of the run-time, while requiring considerably less information and allowing efficient evaluation of probabilities.
AB - Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account for aleatory and epistemic uncertainty, respectively due to the dynamic nature of proteins and experimental errors in protein structures. Additionally, they typically rely on information that is not generally or readily available, such as homologous sequences, related protein structures and other complementary information. To address these issues, we developed BIFROST, a novel take on the fragment library problem based on a Deep Markov Model architecture combined with directional statistics for angular degrees of freedom, implemented in the deep probabilistic programming language Pyro. BIFROST is a probabilistic, generative model of the protein backbone dihedral angles conditioned solely on the amino acid sequence. BIFROST generates fragment libraries with a quality on par with current state-of-the-art methods at a fraction of the run-time, while requiring considerably less information and allowing efficient evaluation of probabilities.
M3 - Article in proceedings
T3 - Proceedings of Machine Learning Research
SP - 10258
EP - 10267
BT - International Conference on Machine Learning, 18-24 July 2021, Virtual
PB - PMLR
T2 - 38th International Conference on Machine Learning
Y2 - 18 July 2021 through 24 July 2021
ER -