Protein structure search and local structure characterization

<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p

Nitrate reductase transcript is expressed in the primary response of maize to environmental nitrate

The nitrate induction of NADH:nitrate reductase mRNA in maize roots, scutella and leaves was investigated in the presence and absence of inhibitors of protein synthesis. In the absence of inhibitors, nitrate treatment caused a fairly rapid (2 to 3 h) increase in the level of the nitrate reductase transcript in all tissues. When cytoplasmic protein synthesis was inhibited by cycloheximide, nitrate reductase mRNA was induced by nitrate in all tissues to levels equal to or greater than those found with nitrate treatment alone. Treatment of maize tissues with cycloheximide in the absence of nitrate had only a small effect on the accumulation of the nitrate reductase mRNA. Inhibition of organellar protein synthesis with chloramphenicol also had little or no effect on nitrate-induced nitrate reductase mRNA accumuiation in roots and scutella, but did appear to partially inhibit appearance of transcript in leaves. Excision of scutella in the absence of nitrate was sufficient to cause some accumulation of the nitrate reductase transcript. Since cytoplasmic protein synthesis was not required for expression of nitrate reductase transcripts, induction of these transcripts by nitrate is a primary response of maize to this environmental signal. Thus, it appears that the signal transduction system mediating this response is constitutively expressed in roots, scutella and leaves of maize. © 1992 Kluwer Academic Publishers