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No description available.

Functional characterization of biochemically-isolated proteins is a central task in the biochemical and genetic description of the biology of cells and tissues. Protein identification by mass spectrometry consists of associating an isolated protein with a specific gene or protein sequence in silico, thus inferring its specific biochemical function based upon previous characterizations of that protein or a similar protein having that sequence identity. By performing this analysis on a large scale in conjunction with biochemical experiments, novel biological knowledge can be developed. The study presented here focuses on mass spectrometry-based proteomics of organisms with unsequenced genomes and corresponding developments in biological sequence database searching with mass spectrometry data. Conventional methods to identify proteins by mass spectrometry analysis have employed proteolytic digestion, fragmentation of resultant peptides, and the correlation of acquired tandem mass spectra with database sequences, relying upon exact matching algorithms; i.e. the analyzed peptide had to previously exist in a database in silico to be identified. One existing sequence-similarity protein identification method was applied (MS BLAST, Shevchenko 2001) and one alternative novel method was developed (MultiTag), for searching protein and EST databases, to enable the recognition of proteins that are generally unrecognizable by conventional softwares but share significant sequence similarity with database entries (~60-90%). These techniques and available database sequences enabled the characterization of the Xenopus laevis microtubule-associated proteome and the Dunaliella salina soluble salt-induced proteome, both organisms with unsequenced genomes and minimal database sequence resources. These sequence-similarity methods extended protein identification capabilities by more than two-fold compared to conventional methods, making existing methods virtually superfluous. The proteomics of Dunaliella salina demonstrated the utility of MS BLAST as an indispensable method for characterization of proteins in organisms with unsequenced genomes, and produced insight into Dunaliella?s inherent resilience to high salinity. The Xenopus study was the first proteomics project to simultaneously use all three central methods of representation for peptide tandem mass spectra for protein identification: sequence tags, amino acids sequences, and mass lists; and it is the largest proteomics study in Xenopus laevis yet completed, which indicated a potential relationship between the mitotic spindle of dividing cells and the protein synthesis machinery. At the beginning of these experiments, the identification of proteins was conceptualized as using ?conventional? versus ?sequence-similarity? techniques, but through the course of experiments, a conceptual shift in understanding occurred along with the techniques developed and employed to encompass variations in mass spectrometry instrumentation, alternative mass spectrum representation forms, and the complexities of database resources, producing a more systematic description and utilization of available resources for the characterization of proteomes by mass spectrometry and advanced informatic approaches. The experiments demonstrated that proteomics technologies are only as powerful in the field of biology as the biochemical experiments are precise and meaningful.

Analyses of protein sequences from diverse genomes have revealed the ubiquitous nature of multi-domain proteins. They form up to 70% of proteomes of most eukaryotic organisms. Yet, our understanding of protein structure, folding and evolution has been dominated by extensive studies on single-domain proteins. We provide quantitative treatment and proof for prevailing intuitive ideas on the strategies employed by nature to stabilize otherwise unstable domains. We find that domains incapable of independent stability are stabilized by favourable interactions with tethered domains in the multi-domain context. Natural variations (nsSNPs) at these sites alter communication between domains and affect stability leading to disease manifestation. We emphasize this by using explicit all-atom molecular dynamics simulations to study the interface nsSNPs of human Glutathione S-transferase omega 1. We show that domain-domain interface interactions constrain inter-domain geometry (IDG) which is evolutionarily well conserved. The inter-domain linkers modulate the interactions by varying their lengths, conformations and local structure, thereby affecting the overall IDG. These findings led to the development of a method to predict interfacial residues in multi-domain proteins based on difference evolutionary information extracted from at least two diverse domain architectures (single and multi-domain). Our predictions are highly accurate (∼85%) and specific (∼95%). Using predicted residues to constrain domain–domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. Further, we developed and employed an alignment-free approach based on local amino-acid fragment matching to compare sequences of multi-domain proteins. This is especially effective in the absence of proper alignments, which is usually the case for multi-domain proteins. Using this, we were able to recreate the existing Hanks and Hunter classification scheme for protein kinases. We also showed functional relationships among Immunoglobulin sequences. The clusters obtained were functionally distinct and also showed unique domain-architectures. Our analysis provides guidelines toward rational protein and interaction design which have attractive applications in obtaining stable fragments and domain constructs essential for structural studies by crystallography and NMR. These studies enable a deeper understanding of rapport of protein domains in the multi-domain context.

Functional characterization of biochemically-isolated proteins is a central task in the biochemical and genetic description of the biology of cells and tissues. Protein identification by mass spectrometry consists of associating an isolated protein with a specific gene or protein sequence in silico, thus inferring its specific biochemical function based upon previous characterizations of that protein or a similar protein having that sequence identity. By performing this analysis on a large scale in conjunction with biochemical experiments, novel biological knowledge can be developed. The study presented here focuses on mass spectrometry-based proteomics of organisms with unsequenced genomes and corresponding developments in biological sequence database searching with mass spectrometry data. Conventional methods to identify proteins by mass spectrometry analysis have employed proteolytic digestion, fragmentation of resultant peptides, and the correlation of acquired tandem mass spectra with database sequences, relying upon exact matching algorithms; i.e. the analyzed peptide had to previously exist in a database in silico to be identified. One existing sequence-similarity protein identification method was applied (MS BLAST, Shevchenko 2001) and one alternative novel method was developed (MultiTag), for searching protein and EST databases, to enable the recognition of proteins that are generally unrecognizable by conventional softwares but share significant sequence similarity with database entries (~60-90%). These techniques and available database sequences enabled the characterization of the Xenopus laevis microtubule-associated proteome and the Dunaliella salina soluble salt-induced proteome, both organisms with unsequenced genomes and minimal database sequence resources. These sequence-similarity methods extended protein identification capabilities by more than two-fold compared to conventional methods, making existing methods virtually superfluous. The proteomics of Dunaliella salina demonstrated the utility of MS BLAST as an indispensable method for characterization of proteins in organisms with unsequenced genomes, and produced insight into Dunaliella?s inherent resilience to high salinity. The Xenopus study was the first proteomics project to simultaneously use all three central methods of representation for peptide tandem mass spectra for protein identification: sequence tags, amino acids sequences, and mass lists; and it is the largest proteomics study in Xenopus laevis yet completed, which indicated a potential relationship between the mitotic spindle of dividing cells and the protein synthesis machinery. At the beginning of these experiments, the identification of proteins was conceptualized as using ?conventional? versus ?sequence-similarity? techniques, but through the course of experiments, a conceptual shift in understanding occurred along with the techniques developed and employed to encompass variations in mass spectrometry instrumentation, alternative mass spectrum representation forms, and the complexities of database resources, producing a more systematic description and utilization of available resources for the characterization of proteomes by mass spectrometry and advanced informatic approaches. The experiments demonstrated that proteomics technologies are only as powerful in the field of biology as the biochemical experiments are precise and meaningful.

Methods for aproximate string matching of various sequences used in bioinformatics are crucial part of development in this branch. Tasks are of very large time complexity and therefore we want create a hardware platform for acceleration of these computations. Goal of this work is to design a generalized architecture based on FPGA technology, which can work with various types of sequences. Designed acceleration card will use especially dynamic algorithms like Needleman-Wunsch and Smith-Waterman.