The Proteomic Code: a molecular recognition code for proteins

Background

The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.

Review

The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.

Methods and conclusions

A novel cloning method for the design and production of specific, high-affinity-reacting proteins (SHARP) is presented. This method is based on the concept of proteomic codes and is suitable for large-scale, industrial production of specifically interacting peptides.     

Proteomic Code

On the basis of recent basic developments in protein structure, a proteomic code is suggested, which would potentially allow the sequence, structure, and function of proteins to be described by a spectrum of elementary loop-n-lock units. All major characteristics of the nearly standard units are described, and the first five codons of the proteomic code are presented with their respective unique sequences, structures, and functions. More such codons are to be discovered, and the general procedure for their identification is described.     

Proteomic analysis

The field of proteomics is becoming increasingly important as genome sequences are being completed and annotated. Recent advances in proteomics include experimental and mathematical proofs of the need to complement microarray analysis with protein analysis, improved sensitivity for mass spectrometric analysis of separated proteins, better informatic tools for gel analysis and protein spot annotation, first steps towards automated experimental procedures, and new technology for quantitation of protein changes.     

The genetic code, an instantaneous absolutely optimal code

The genetic code is an instantaneous code i.e., each codon is deciphered out of ambiguities without knowing other symbols than the constituting nucleotides. Moreover entropy of the genetic source of information has a maximal value.     

Proteomic analysis of lymph

This report provides the first proteomic analysis of normal ovine lymph. By establishing the fact that lymph is more than an ultrafiltrate of blood plasma, it documents that the lymph proteome contains an array of proteins that differentiates it from plasma. The protein chip technology, surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS), two-dimensional gel electrophoresis (2-D PAGE) and MS, were employed to examine the protein expression profiles of ovine lymph. Using a weak cation exchange chip surface to assay lymph and plasma samples by SELDI-TOF-MS showed that the analysis of peak maps from lymph contained three protein peaks that were found only in lymph, while analysis of peak maps from plasma samples showed that five protein peaks were found only in plasma. Lymph and plasma samples showed eight peaks that were common to both. There were also more ions present in plasma than in lymph, which is consistent with the 2-D PAGE analysis. MS analysis of a large number of protein spots from 2-D PAGE gels of lymph produced MS/MS sequences for 18 proteins that were identified by searching against a comprehensive protein sequence database. As in plasma, large protein spots of albumin dominated the protein pattern in lymph. Other major proteins identified in 2-D PAGE gels of lymph included, fibrinogen alpha- and beta-chains, immunoglobulin G (IgG) heavy chain, serotransferrin precursor, lactoferrin, and apolipoprotein A-1. Two proteins that were identified and were differentially expressed in lymph were glial fibrillary astrocyte acidic protein and neutrophil cytosol factor-1. By bringing the technologies of proteomics to bear on the analysis of lymph, it is possible to detect proteins in lymph that are quantitatively and qualitatively differentially expressed from those of plasma.     

Proteomic studies in plants

Proteomics is a leading technology for the high-throughput analysis of proteins on a genome-wide scale. With the completion of genome sequencing projects and the development of analytical methods for protein characterization, proteomics has become a major field of functional genomics. The initial objective of proteomics was the large-scale identification of all protein species in a cell or tissue. The applications are currently being extended to analyze various functional aspects of proteins such as post-translational modifications, protein-protein interactions, activities and structures. Whereas the proteomics research is quite advanced in animals and yeast as well as Escherichia coli, plant proteomics is only at the initial phase. Major studies of plant proteomics have been reported on subcellular proteomes and protein complexes (e.g. proteins in the plasma membranes, chloroplasts, mitochondria and nuclei). Here several plant proteomics studies will be presented, followed by a recent work using multidimensional protein identification technology (MudPIT).     

Proteomic traces of speciation

Recent work has shown that the network of structural similarity between protein domains exhibits a power-law distribution of edges per node. The scale-free nature of this graph, termed the protein domain universe graph or PDUG, may be reproduced via a divergent model of structural evolution. The performance of this model, however, does not preclude the existence of a successful convergent model. To further resolve the issue of protein structural evolution, we explore the predictions of both convergent and divergent models directly. We show that when nodes from the PDUG are partitioned into subgraphs on the basis of their occurrence in the proteomes of particular organisms, these subgraphs exhibit a scale-free nature as well. We explore a simple convergent model of structural evolution and find that the implications of this model are inconsistent with features of these organismal subgraphs. Importantly, we find that biased convergent models are inconsistent with our data. We find that when speciation mechanisms are added to a simple divergent model, subgraphs similar to the organismal subgraphs are produced, demonstrating that dynamic models can easily explain the distributions of structural similarity that exist within proteomes. We show that speciation events must be included in a divergent model of structural evolution to account for the non-random overlap of structural proteomes. These findings have implications for the long-standing debate over convergent and divergent models of protein structural evolution, and for the study of the evolution of organisms as a whole.     

Proteomic tools for biomedicine

Proteomic tools measure gene expression, protein activity and interactions of biological events at the protein level. Proteins are the major catalysts of biological functions and contain several dimensions of information that collectively indicate the actual rather than the potential functional state as indicated by mRNA analysis. Measurements can be made in terms of protein quantity, location, and time-point. For the future we see a further integration of existing and new technologies for proteomics from a wide range of areas of biochemistry, chemistry, physics, computing science and molecular biology. This will further advance our knowledge of how biological systems are built up and what mechanisms control these systems. However, the potential of proteomics to comprehensively answer all biological questions is limited as only protein activity is measured. A unification of genomics, proteomics, and other technologies is needed if we are to start to understand the complexity of biological function in the context of disease and health.     

Proteomic applications in ecotoxicology

Within the growing body of proteomics studies, issues addressing problems of ecotoxicology are on the rise. Generally speaking, ecotoxicology uses quantitative expression changes of distinct proteins known to be involved in toxicological responses as biomarkers. Unlike these directed approaches, proteomics examines how multiple expression changes are associated with a contamination that is suspected to be detrimental. Consequently, proteins involved in toxicological responses that have not been described previously may be revealed. Following identification of key proteins indicating exposure or effect, proteomics can potentially be employed in environmental risk assessment. To this end, bioinformatics may unveil protein patterns specific to an environmental stress that would constitute a classifier able to distinguish an exposure from a control state. The combined use of sets of marker proteins associated with a given pollution impact may prove to be more reliable, as they are based not only on a few unique markers which are measured independently, but reflect the complexity of a toxicological response. Such a proteomic pattern might also integrate some of the already established biomarkers of environmental toxicity. Proteomics applications in ecotoxicology may also comprise functional examination of known classes of proteins, such as glutathione transferases or metallothioneins, to elucidate their toxicological responses.