ArticleBMC bioinformatics2011
Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 52 citations in OpenAlex.
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- Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods.Journal of chemical information and modeling · 2023Article
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- Integrated approach for studying bioactive compounds from Cladosporium spp. against estrogen receptor alpha as breast cancer drug target.Scientific reports · 2022Article
- Exploring the structural constraints at cleavage site of mucin 1 isoform through molecular dynamics simulation.European biophysics journal : EBJ · 2015Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor.
methodsThis paper describes inductive learning methods to train protein-independent probabilistic models of H-bond stability from molecular dynamics (MD) simulation trajectories of various proteins. The training data contains 32 input attributes (predictors) that describe an H-bond and its local environment in a conformation c and the output attribute is the probability that the H-bond will be present in an arbitrary conformation of this protein achievable from c within a time duration Δ. We model dependence of the output variable on the predictors by a regression tree.
resultsSeveral models are built using 6 MD simulation trajectories containing over 4000 distinct H-bonds (millions of occurrences). Experimental results demonstrate that such models can predict H-bond stability quite well. They perform roughly 20% better than models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a conformation. In most tests, about 80% of the 10% H-bonds predicted as the least stable are actually among the 10% truly least stable. The important attributes identified during the tree construction are consistent with previous findings.
conclusionsWe use inductive learning methods to build protein-independent probabilistic models to study H-bond stability, and demonstrate that the models perform better than H-bond energy alone.
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