Information theory in molecular biology
Christoph Adami
Abstract
This article introduces the physics of information in the context of molecular biology and genomics. Entropy and information, the two central concepts of Shannon's theory of information and communication, are often confused with each other but play transparent roles when applied to statistical ensembles (i.e., identically prepared sets) of symbolic sequences. Such an approach can distinguish between entropy and information in genes, predict the secondary structure of ribozymes, and detect the covariation between residues in folded proteins. We also review applications to molecular sequence and structure analysis, and introduce new tools in the characterization of resistance mutations, and in drug design.
Create a lesson
Related papers
Discrete Backbone Elasticity and Curvature-Dependent Torsion Statistics in Proteins
Yiquan Wang, Mingyu Bian, Jiayuan Ma
Ablating Design Choices for Protein Ensemble Generators Across Structural Regimes
Eoin Quinn, Oliver Bent
Predicting protein structures at any folding stage from sequence
Lisheng Zhang, Qingsong Li, Zinan Xie et al.
Automatic Generation of Novel Highly-Functional Molecular Sequences
Vincent R. Ragusa, Chrisoph Adami
D2ML: Models for Direct-to-Biology Small Molecule Screening
AkshatKumar Nigam, Neri Amara, Hillary Dequina et al.
Contribution of chromosome-naïve transposon-seeded network of DNA templates to intracellular pools of protein dimers of liquid-liquid phase-separated condensate-forming proteins
Gennadi Glinsky