Random Amino Acid Mutations and Protein Misfolding Lead to Shannon Limit in Sequence-Structure Communication

Abstract

The transmission of genomic information from coding sequence to protein structure during protein synthesis is subject to stochastic errors. To analyze transmission limits in the presence of spurious errors, Shannon's noisy channel theorem is applied to a communication channel between amino acid sequences and their structures established from a large-scale statistical analysis of protein atomic coordinates. While Shannon's theorem confirms that in close to native conformations information is transmitted with limited error probability, additional random errors in sequence (amino acid substitutions) and in structure (structural defects) trigger a decrease in communication capacity toward a Shannon limit at 0.010 bits per amino acid symbol at which communication breaks down. In several controls, simulated error rates above a critical threshold and models of unfolded structures always produce capacities below this limiting value. Thus an essential biological system can be realistically modeled as a digital communication channel that is (a) sensitive to random errors and (b) restricted by a Shannon error limit. This forms a novel basis for predictions consistent with observed rates of defective ribosomal products during protein synthesis, and with the estimated excess of mutual information in protein contact potentials.

A method for partitioning the information contained in a protein sequence between its structure and function

“…The gap between the two thus increases linearly as well with protein length and is remarkably comparable to the sum of the contribution from cleavage, solubility, and function [Color figure can be viewed at wileyonlinelibrary.com] information is translated into protein sequences and can be readily quantified using information theory. [4][5][6][7][8][9] In turn, the information contained in protein sequences can be exploited to carry out function, through specific binding or catalytic sites, and regulation, hosting sites which permit proteolysis and avoid aggregation. 43 The information contents needed for these functions can be quantified as well.…”

“…Since then, information theory has been widely used to investigate protein folding. [4][5][6][7][8][9] The general strategy has been to evaluate the number of bits necessary to describe the native state of a protein and compare it with that encoded by its sequence.…”

`The frozen accident' as an evolutionary adaptation: A rate distortion theory perspective on the dynamics and symmetries of genetic coding mechanisms

“…There are at least 48 discernable codons but only 20 amino acids available (and 3 stop codons), so the code is degenerate in so far that several codons can represent the same amino acid. Entropy analysis (Adami et al, 2000;Lisewski, 2008) reveals that the information content of a random protein structure can occupy log 2 (20) ≃ 4.32 bits of entropy per amino acid residue in a primary sequence.…”

“…In view of Shannon's theorem, as long as H < C, there will be suitable fidelity in transmission. For the case of genetic coding considered here, conditions of discrete memoryless information source (DMI) and discrete memoryless channels(DMC) (Yockey, 2005;McEliece, 2004) are usually assumed, but in any event, how well a communicating system can evolve in order to satisfy such an estimate is a common problem for communications engineering since in practice the source rate may be corrupted due to low memory and coding congestion; for protein folding and mutations; references Adami, (2004), Crooks and Brenner (2004), Lisewski (2008), Tlusty (2007Tlusty ( , 2008, Wallace (2010aWallace ( , 2010c address such issues.…”

Application of neural networks methods to define the most important features contributing to xylanase enzyme thermostability

“…Several methods have been proposed for predicting the stability of proteins upon amino acid substitutions [2]. These methods are mainly based on distance and torsion potentials [3], multiple regression techniques [4], energy functions [5], contact potentials [6,7], neural networks [8], support vector machines, SVMs [9,10], average assignment [3], classification and regression tools [11,12], backbone flexibility [13] etc. Further, it has been reported that the discrimination of stabilizing and destabilizing mutants is more important than its magnitude in many cases [2,14].…”