Protein sequence prediction

Koonin, E. V., Mushegian, A. R., Galperin, M. Y., and Walker, D. R. (1997). Comparison of archaeal and bacterial genomes computer analysis of protein sequences predicts novel functions and suggests a chimeric origin for the archaea. Mol. Microbiol. 25, 619-637. 

The computer has become an essential tool in biochemical research. A computer may be used for the routine jobs of word processing and data collection and analysis. In addition, if a computer is connected to the Internet, then it may be used for biochemical literature searching, accessing information about nucleic acid and protein sequences, predicting protein structure, and seeking research methodology. In this experiment, students will be introduced to all of these skills in bioinformatics. 

Analysis of tlie global statistics of protein sequences has recently allowed light to be shed on anotlier puzzle, tliat of tlie origin of extant sequences [170]. One proposition is tliat proteins evolved from random amino acid chains, which predict tliat tlieir length distribution is a combination of the exponentially distributed random variable giving tlie intervals between start and stop codons, and tlie probability tliat a given sequence can fold up to fonii a compact... 

Protein Structure Prediction, Sequence Analysis and Protein Folding... 

Barton G J1996. Protein Sequence Alignment and Database Scanning. In Sternberg M E (Editor) Prote Structure Prediction - A Practical Approach. Oxford, IRL Press, pp. 31-63. 

Barton G J 1998. Protein Sequence Aligrunent Techniques. Acta Crystallographica 054 1139-1146. Blundell T L, B L Sibanda, M J E Sterbnerg and J M Thornton. Knowledge-based Prediction of Prote Structures and the Design of Novel Molecules. Nature 326 347-352. 

A potentially general method of identifying a probe is, first, to purify a protein of interest by chromatography (qv) or electrophoresis. Then a partial amino acid sequence of the protein is deterrnined chemically (see Amino acids). The amino acid sequence is used to predict likely short DNA sequences which direct the synthesis of the protein sequence. Because the genetic code uses redundant codons to direct the synthesis of some amino acids, the predicted probe is unlikely to be unique. The least redundant sequence of 25—30 nucleotides is synthesized chemically as a mixture. The mixed probe is used to screen the Hbrary and the identified clones further screened, either with another probe reverse-translated from the known amino acid sequence or by directly sequencing the clones. Whereas not all recombinant clones encode the protein of interest, reiterative screening allows identification of the correct DNA recombinant. 

Although the techniques described have resulted in the determination of many protein stmctures, the number is only a small fraction of the available protein sequences. Theoretical methods aimed at predicting the 3-D stmcture of a protein from its sequence therefore form a very active area of research. This is important both to understanding proteins and to the practical appHcations in biotechnology and the pharmaceutical industries. 

At this time, approximately one-half of all sequences are delectably related to at least one protein of known structure [8-11]. Because the number of known protein sequences is approximately 500,000 [12], comparative modeling could in principle be applied to over 200,000 proteins. This is an order of magnitude more proteins than the number of experimentally determined protein structures (—13,000) [13]. Furthermore, the usefulness of comparative modeling is steadily increasing, because the number of different structural folds that proteins adopt is limited [14,15] and because the number of experimentally determined structures is increasing exponentially [16]. It is predicted that in less than 10 years at least one example of most structural folds will be known, making comparative modeling applicable to most protein sequences [6]. 

GJ Barton. Protein sequence alignment and database scanning. In MJE Sternberg, ed. Protein Structure Prediction A Practical Approach. Oxford, UK IRE Press at Oxford Univ Press, 1998. 

P Briffeuil, G Baudoux, C Lambert, X De Bolle, C Vmals, E Feytmans, E Depiereux. Comparative analysis of seven multiple protein sequence alignment servers Clues to enhance reliability of predictions. Biomformatics 14 357-366, 1998. 

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. 

Fasman, G.D., Gilbert, W.A. The prediction of transmembrane protein sequences and their conformation an evaluation. Trends Biochem. Sci. 15 89-95, 1990. 

Fasman, G. D., and Gilbert, W. A., 1990. The prediction of transmembrane protein sequences and dieir conformation An evaluation. Trends in Biochemical Sciences 15 89—92. 

Primary structure is determined, as we ve seen, by sequencing the pTotein. Secondary, tertiary, and quaternary structures are determined by X-ray crystallography (Chapter 22 Focus On) because it s not yet possible to predict computationally how a given protein sequence will fold. 

Protein-protein interactions predicted on the sequence level can be studied in more detail on the structure level. Single Nucleotide Polymorphisms can be mapped on 3D structures of proteins in order to elucidate specific structural causes of disease. 

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 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. 

Starting from the protein sequence (primary structure) several algorithms can be used to analyze the primary structure and to predict secondary structural elements like beta-strands, turns, and helices. The first algorithms from Chou and Fasman occurred already in 1978. The latest algorithms find e.g., that predictions of transmembrane... 

The protein structure prediction problem refers to the combinatorial problem to calculate the 3D structure of a protein from its sequence alone. It is one of the biggest challenges in structural bioinformatics. 

Threading techniques try to match a target sequence on a library of known 3D structures by threading the target sequence over the known coordinates. In this manner, threading tries to predict the 3D structure starting from a given protein sequence. It is sometimes successful when comparisons based on sequences or sequence profiles alone fail due to a too low sequence similarity. 

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