Sequence—function relationships

The aim of the fust dimension breadth is to reveal sequence-function relationships by comparing protein sequences by sequence similarity. Simple bioinformatic algorithms can be used to compare a pair of related proteins or for sequence similarity searches e.g., BLAST (Basic Local Alignment Search Tool). Improved algorithms allow multiple alignments of larger number of proteins and extraction of consensus sequence pattern and sequence profiles or structural templates, which can be related to some functions, see e.g., under http //www. expasy.ch/tools/ similarity. 

The history of molecular biology has been a history of technological developments for determining the primary and tertiary structures of protein and nucleic acid molecules. Once the molecular structure is known, it provides clues to molecular functions. This is the principle of the structure-function relationship. Based on this principle the analysis of the amino acid sequence is performed to decipher the functional information from the sequence information. The analysis usually involves detection and prediction of empirical sequence—function relationships with additional consideration of known or predicted three-dimensional (3D) structures. Thus, the process can be represented schematically as ... 

Genes are considered here as the basic elements (building blocks) that constitute a living organism (system). A traditional strategy to establish the structure—function (or sequence—function) relationship of... 

Schlosser, K., Gu, J., Sule, L., and Li, Y. (2008a). Sequence-function relationships provide new insight into the cleavage site selectivity of the 8—17 RNA-cleaving deoxyribozyme. Nucleic Acids Res. 36, 1472-1481. 

Goffin, V., Shiverick, K. T., Kelly, P. A., and Martial, J. A. (1996). Sequence-function relationships within the expanding family of prolactin, growth hormone, placental lactogen, and related proteins in mammals. Endocrine Reviews 17, 385—410. 

If significant similarities between uncharacterized and annotated sequences are found, the transfer of annotation is straightforward at high identity levels (> 40%). Below that sequence-identity level, the establishment of firm sequence-function relationships can start to present some problems. The signal starts to become blurred at 20 to 30% identity, the so-called Twilight Zone [12,13]. If identity levels are even lower, the region is called the Midnight Zone (fig. 3.2). For identity levels over 30%,... 

Next, we explain evolutional and structural bases for searching for sequence-function relationships in proteins. Then, we describe homology graphing, a method for sequence analysis to Identify which regions In a sequence are those of functionally Importance,... 

Sequence-Function Relationships in Proteins. Based on the above two aspects of sequence similarity, regions the sequences of which show local similarity with those of other proteins are the regions of evolutional and functional importance. This is the biological basis for analyzing sequence-function relationships in proteins using sequence similarity searches. 

Since the number of amino acid residues defined In consensus sequences are usually less than 10 residues, consensus sequences are too small as a unit of function and molecular evolution of proteins. Therefore sequence segments found In homology graphs are favored for sequence-function relationships over consensus sequences. 

Breton, C., Bettler, E., Joziasse, D.H., Geremia, R.A. and Imberty, A. Sequence-function relationships of prokaryotic and eukaryotic galactosyltransferases. J. Biochem. (Tokyo) 1998, 123, 1000-1009. 

Fowler, D.M., et al.. High-resolution mapping of protein sequence-function relationships. Nat. Methods, 2010. 7(9) 741-746. 

Structure—function relationships of prolactin among a variety of species have been pubUshed (17,18). Only one gene for prolactin appears to exist (19). Although classically placed in the category of simple protein hormones, prolactin can be glycosylated. Carbohydrate attachment occurs at Asn-31, where the consensus glycosylation sequence Asn—X—Ser is found. 

To gain the most predictive utility as well as conceptual understanding from the sequence and structure data available, careful statistical analysis will be required. The statistical methods needed must be robust to the variation in amounts and quality of data in different protein families and for structural features. They must be updatable as new data become available. And they should help us generate as much understanding of the determinants of protein sequence, structure, dynamics, and functional relationships as possible. 

RNA structures, compared to the helical motifs that dominate DNA, are quite diverse, assuming various loop conformations in addition to helical structures. This diversity allows RNA molecules to assume a wide variety of tertiary structures with many biological functions beyond the storage and propagation of the genetic code. Examples include transfer RNA, which is involved in the translation of mRNA into proteins, the RNA components of ribosomes, the translation machinery, and catalytic RNA molecules. In addition, it is now known that secondary and tertiary elements of mRNA can act to regulate the translation of its own primary sequence. Such diversity makes RNA a prime area for the study of structure-function relationships to which computational approaches can make a significant contribution. 

Moreover, molecular modeling is one key method of a wide range of computer-assisted methods to analyze and predict relationships between protein sequence, 3D-molecular structure, and biological function (sequence-structure-function relationships). In molecular pharmacology these methods focus predominantly on analysis of interactions between different proteins, and between ligands (hormones, drugs) and proteins as well gaining information at the amino acid and even to atomic level. 

The constantly increasing amount of data coming from high throughput experimental methods, from genome sequences, from functional- and structural genomics has given a rise to a need for computer-assisted methods to elucidate sequence-structure-function relationships. 

There are two different dimensions, breadth and depth, used to reveal sequence-structure -functional relationships by computational methods [1]. 

The aim of the second dimension depth is to consider protein 3D-stmctures to uncover structure-function relationships. Starting from the protein sequences, the steps in the depth dimension are structure prediction, homology modeling of protein structures, and the simulation of protein-protein interactions and ligand-complexes. 

Efforts to investigate the questions posed here will lead to more useful peptoid designs while simultaneously leading to a better fundamental understanding of molecular recognition and sequence/structure/function relationships in non-natural, sequence-specific peptidomimetic ohgomers. 

A cloned complementary DNA to a neurotoxin precursor RNA extracted from the venom glands of Laticauda semifasciata was isolated and its nucleotide sequence was identified 11). The cloning of neurotoxin should aid the understanding of structure—function relationship eventually. 

Historically the Shaker (Sh) K channel was the first K channel which was cloned and characterized [6-10]. Subsequently many more channel cDNAs and genes have been isolated and studied. Yet Sh channels remained in the forefront of channel research. The study of Sh channel mutants has provided the most thorough insight into structure-function relationships of K channels to date. I will first discuss in this chapter the primary sequences of voltage-gated channels. I will only use a few selected examples for discussion. As of this time, so many related K channel protein sequences have been published that it is not feasible to discuss all of them. Subsequently, I will describe in detail the present knowledge about functional K" " channel domains which are implicated in activation, inactivation and selectivity of the channel. 

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