Protein sequence randomness and sequence/structure correlations

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Identification of latent periodicity in domains of alkaline proteases

Internal repeats in protein sequences have wide-ranging implications for the structure and function of proteins. A keen analysis of the repeats in protein sequences may help us to better understand the structural organization of proteins and their evolutionary relations. In this paper, a mathematical method for searching for latent periodicity in protein sequences is developed. Using this method, we identified simple sequence repeats in the alkaline proteases and found that the sequences could show the same periodicity as their tertiary structures. This result may help us to reduce difficulties in the study of the relationship between sequences and their structures.

The subsequence composition of polypeptides

The quantitative underpinning of the information content of biosequences represents an elusive goal and yet also an obvious prerequisite to the quantitative modeling and study of biological function and evolution. Several past studies have addressed the question of what distinguishes biosequences from random strings, the latter being clearly unpalatable to the living cell. Such studies typically analyze the organization of biosequences in terms of their constituent characters or substrings and have, in particular, consistently exposed a tenacious lack of compressibility on behalf of biosequences. This article attempts, perhaps for the first time, an assessement of the structure and randomness of polypeptides in terms on newly introduced parameters that relate to the vocabulary of their (suitably constrained) subsequences rather than their substrings. It is shown that such parameters grasp structural/functional information, and are related to each other under a specific set of rules that span biochemically diverse polypeptides. Measures on subsequences separate few amino acid strings from their random permutations, but show that the random permutations of most polypeptides amass along specific linear loci.

"Hidden" sequence periodicities and protein architecture

I demonstrate the existence in protein domain sequences of sets of statistically significant periodic signals, characteristic of the architectures of those domains. It is shown that, although the frequencies of the signals characteristic of a particular architecture have definite wave numbers, they can occur in any physical property. The characteristic signals define sequence units, which may correspond to specific structural features. Study of signals in different architectures suggests that symmetric structures are characterized by sets of sequence signals, which are members of harmonic series and which therefore impose commensurate periodicities on the structure. Asymmetric structures are characterized by mutually incommensurate sets of sequence signals. It is pointed out that, although these periodic sequence units can be very short, their existence is a property of the entire sequence.

Using neural network predicted secondary structure information in automatic protein NMR assignment

In CAPRI, an automated NMR assignment software package that was developed in our laboratory, both chemical shift values and coupling topologies of spin patterns are used in a procedure for amino acids recognition. By using a knowledge base of chemical shift distributions of the 20 amino acid types, fuzzy mathematics, and pattern recognition theory, the spin coupling topological graphs are mapped onto specific amino acid residues. In this work, we investigated the feasibility of using secondary structure information of proteins as predicted by neural networks in the automated NMR assignment. As the 1H and 13C chemical shifts of proteins are known to correlate to their secondary structures, secondary structure information is useful in improving the amino acid recognition. In this study, the secondary structures of proteins predicted by the PHD protein server and our own trained neural networks are used in the amino acid type recognition. The results show that the predicted secondary structure information can help to improve the accuracy of the amino acid recognition.