Editorial: Machine Learning in Bio-cheminformatics

Merz, Kenneth M.; Wei, Guo-Wei; Zhu, Feng

doi:10.1021/acs.jcim.4c00444

Cited by 2 publications

(2 citation statements)

References 69 publications

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“…Artificial intelligence (AI) provides an alternative approach for accelerating the generation of protein conformational ensembles. Novel machine learning/deep learning (ML/DL) approaches analyze simulation results to further guide conformation search with enhanced sampling techniques [19][20][21][22][23][24][25][26][27] . ML/DL optimizes coarse-grain models to speed up conformation transitions with preserved atomistic interactions 28,29 .…”

Section: Introductionmentioning

confidence: 99%

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Chang,

Ruzmetov,

Hung

et al. 2024

Preprint

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Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure–function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42’s conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

show abstract

Section: Introductionmentioning

confidence: 99%

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Chang,

Ruzmetov,

Hung

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) provides an alternative approach for accelerating the generation of protein conformational ensembles. Novel machine learning/deep learning (ML/DL) approaches analyze simulation results to further guide conformational search with enhanced sampling techniques [19][20][21][22][23][24][25][26][27][28] . ML/DL optimizes coarse-grain models to speed up conformation transitions with preserved atomistic interactions 29,30 .…”

Section: Introductionmentioning

confidence: 99%

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Ruzmetov,

Hung,

Jonnalagedda

et al. 2024

Preprint

View full text Add to dashboard Cite

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large scale motions may govern protein structure function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid beta 1 to 42 (Abeta42) monomer, our deep learning model provided a comprehensive sampling of Abeta42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

show abstract

Editorial: Machine Learning in Bio-cheminformatics

Cited by 2 publications

References 69 publications

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

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