Prof. Rita CASADIO - Eurasia Biochemical Approaches & Technologies Congress (EBAT)

Prof. Rita CASADIO



Prof. Rita CASADIO

University of Bologna


Educational Background: RC, after her degree in Physics at the University of Bologna, Italy, attended several courses both in Italy and abroad and acquired experience and theoretical background in different fields, such as Computer Science, Membrane and Protein Biophysics, Bioenergetics and Irreversible Thermodynamics.

Professional Experience: After working in Laboratories of Biophysics both in the United States and in Germany, in 1987 RC became Assistant Professor of Biophysics at the University of Bologna Italy. Since 1/10/2001 she is full professor of Biochemistry/Biophysics in the same University; RC has been working mainly in the fields of membrane and protein Biophysics (particularly with bacteriorhodopsin from Halobacterium Halobium and F1F0 ATPases from mesophilic organisms), both experimentally and theoretically. Presently she is interested in computer modelling of relevant biological processes, such as protein folding and modelling of their stability and interactions. One major field of research is the application of machine learning methods to the prediction of secondary and tertiary structure of proteins from their aminoacid residue sequences, particularly of membrane proteins and their transmembrane topology. one active research field is the annotation of mutations disease related and the prediction of their role at the level od structural systems biology.  Presently she is the group leader of the Biocoputing Unit of the Interdipartimental Centre for Biotechnological Researches (CIRB) of the University of Bologna, Italy and her researches are devoted to different aspects of protein modelling, including prediction of secondary and tertiary structures with neural networks, hidden Markov models, genetic algorithms, molecular docking, protein reaction mechanisms and de novo design of helical peptides (for details see At present the focus is mainly the implementation of integrated platforms for genome/proteome annotation, sequence annotation after alternative splicing in the human proteome, prediction of the relation of protein folding, misfolding and deseases in humans, search for SNPs in different genomes and their validation. RC is the author of about 300 scientific papers and presented her work at several (over 300) national and international meetings (for details see

Other Activities: RC with the Biocomputing Unit is active in organizing International Schools on Bioinformatics. She co-chaired WABI 2005, and act as Associate Editor for Advances in Bioinformatics, BMC Research Notes, she is in the Editorial board of BMC Bioinformatics and BioData Mining. She has been president of the Bologna International Master in Bioinformatics (2007-20015); she has been a  member of the board of directors of I.N.B.B, an Italian InterUniversity Consortium, acting as a representative of the Italian Minister of MIUR; of ISCB, the International Society of Computational Biology; of the Italian Society of Bioinformatics. She is correspondent member of the Accademia delle Scienze dell'Istituto di Bologna. Since 2011 she is joint professor of the Shangai Jiao Tong University

For detailing information

"EC Number Annotation: How to Predict the Enzyme Function"

Next-generation sequencing (NGS) technologies made available hundred million chains from different organism while the number of proteins known with atomic details and an experimentally characterized biochemical function is much smaller. Hence, the problem of functional annotation is one of outmost relevance to bridge the gap among poorly and well characterized sequences.

To tackle this problem, we developed The Bologna ENZyme Web Server (BENZ WS). BENZ annotates four-level Enzyme Commission numbers filtering a target sequence with a combined system of Hidden Markov Models, and modelling protein sequences annotated with the same molecular function, and Pfams, carrying along conserved protein domains. BENZ returns for submitted enzyme sequences an associated four-level EC number being able to annotate both monofunctional and polyfunctional enzymes.

We benchmarked BENZ on the Human Reference Proteomes to assess its performance. The analysis of false positive predictions (namely those proteins lacking an annotated EC number in UniProt but predicted as enzymes by BENZ) allowed us to provide functional annotation to 5,741 enzymes after an independent validation relying on GOtoEC and Pfam/InterPro mapping.