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

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 modular perspective of protein structures: application to fragment based loop modeling

Proteins can be decomposed into supersecondary structure modules. We used a generic definition of supersecondary structure elements, so-called Smotifs, which are composed of two flanking regular secondary structures connected by a loop, to explore the evolution and current variety of structure building blocks. Here, we discuss recent observations about the saturation of Smotif geometries in protein structures and how it opens new avenues in protein structure modeling and design. As a first application of these observations we describe our loop conformation modeling algorithm, ArchPred that takes advantage of Smotifs classification. In this application, instead of focusing on specific loop properties the method narrows down possible template conformations in other, often not homologous structures, by identifying the most likely supersecondary structure environment that cradles the loop. Beyond identifying the correct starting supersecondary structure geometry, it takes into account information of fit of anchor residues, sterical clashes, match of predicted and observed dihedral angle preferences, and local sequence signal