Protein family classification and functional annotation

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.     

Determination of N-methylcarbamate insecticides in water samples using dispersive liquid–liquid microextraction and HPLC with the aid of experimental design and desirability function

A rapid and simple method for the extraction and preconcentration of N-methylcarbamates (NMCs) (carbofuran, carbaryl and promecarb) in water samples using dispersive liquid–liquid microextraction (DLLME) using chemometrics was developed. Influence variables such as volume of extracting (CHCl₃) and dispersing solvents (ACN), pH and ionic strength, extraction time and centrifugation time and speed were screened in a 2^7–4 Plackett–Burman design was investigated. The significant variables were optimized by using a central composite design (CCD) combined with desirability function (DF). At optimum conditions values of variables set as 126 μL chloroform, 1.5 mL acetonitrile, 1 min extraction time, 10 min centrifugation at 4000 rpm min⁻¹, natural pH, 4.7% (w/v) NaCl, the separation was reached in less than 14 min using a C₁₈ column and an isocratic binary mobile phase (acetonitrile: water (50:50, v/v)) with flow rate of 1.0 mL min⁻¹. At optimum conditions method has linear response over 0.001–10 μg mL⁻¹ with detection limit between 0.0001 and 0.0005 μg mL⁻¹ with relative standard deviations (RSDs) in the range 2.18–5.06% (n = 6).