Quantification of degeneracy in biological systems for characterization of functional interactions between modules

Abstract

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components.

Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization

“…Identified antioxidant peptides range from 2 to 14 amino acids in length. Previous studies found that most antioxidative peptides contained hydrophobic amino acids at the N-terminus and/or C-terminus, and proline, histidine, or tyrosine within the sequence (Li & Li, 2013). Hydrophobic residues may increase the interaction between peptides and fatty acid radicals.…”

“…Hydrophobic residues may increase the interaction between peptides and fatty acid radicals. Histidine is predicted to chelate and trap radicals, and tyrosine can donate hydrogen to reduce free radicals (Li & Li, 2013). In our database, 30 of the 41 peptides contained hydrophobic amino acids at the N-terminus and/or C-terminus, and all but one contained at least one proline, histidine, or tyrosine in the sequence.…”

Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides

“…In this context, Hemolytik database will be useful for developing novel in silico prediction, as well as designing methods for hemolytic or non-hemolytic peptides. In the recent past, few prediction and quantitative structure activity relationship models on various therapeutic peptides, including antimicrobial peptides (25–27), cell-penetrating peptides (28–31), antioxidant peptides (32,33), have been developed, which will be helpful to reduce the costs and efforts involved in laboratory screening. The optimized structures and SMILES of hemolytic peptides available in the Hemolytik database can also be used to develop quantitative structure activity relationship models for rapid screening of therapeutic peptides with less hemolytic activity.…”

Degeneracy allows for both apparent homogeneity and diversification in populations

“…The molecular structures underlying living systems are degenerate in the relationships between their structure and function. For example, proteins, such as enzymes, antibodies, or receptors, no matter how specific for their substrates, antigens, or ligands, respectively, function as degenerate binders, and will also bind other molecules, although less specifically (Atamas, 1996, 2003, 2005; Atamas et al, 1998; Calis et al, 2012; D’Ari and Casadesus, 1998; Edelman and Gally, 2001; Hafler, 2002; Khan and Salunke, 2012; Krieger and Stern, 2001; Kupiec, 2010; Lancet et al, 1993; Lazcano et al, 1995; Li et al, 2012; Negishi et al, 1996). At the protein level, ligand binding sites are functionally degenerate for several reasons.…”