Prediction of RNA binding sites in proteins from amino acid sequence
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The Department of Biochemistry, Biophysics, and Molecular Biology was founded to give students an understanding of life principles through the understanding of chemical and physical principles. Among these principles are frontiers of biotechnology such as metabolic networking, the structure of hormones and proteins, genomics, and the like.
History
The Department of Biochemistry and Biophysics was founded in 1959, and was administered by the College of Sciences and Humanities (later, College of Liberal Arts & Sciences). In 1979 it became co-administered by the Department of Agriculture (later, College of Agriculture and Life Sciences). In 1998 its name changed to the Department of Biochemistry, Biophysics, and Molecular Biology.
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1959–present
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- Department of Biochemistry and Biophysics (1959–1998)
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- College of Agriculture and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
The Department of Genetics, Development, and Cell Biology seeks to teach subcellular and cellular processes, genome dynamics, cell structure and function, and molecular mechanisms of development, in so doing offering a Major in Biology and a Major in Genetics.
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The Department of Genetics, Development, and Cell Biology was founded in 2005.
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- College of Agriculture and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
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Abstract
RNA–protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA–protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA–protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA–protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA–protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)
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This article is from RNA 12 (2006): 1450, doi: 10.1261/rna.2197306. Posted with permission.