Neural networks predict protein folding and structure: artificial intelligence faces biomolecular complexity.
In the genomic era DNA sequencing is increasing our knowledge of the molecular structure of genetic codes from bacteria to man at a hyperbolic rate. Billions of nucleotides and millions of aminoacids are already filling the electronic files of the data bases presently available, which contain a tremendous amount of information on the most biologically relevant macromolecules, such as DNA, RNA and proteins. The most urgent problem originates from the need to single out the relevant information amidst a wealth of general features. Intelligent tools are therefore needed to optimise the search. Data mining for sequence analysis in biotechnology has been substantially aided by the development of new powerful methods borrowed from the machine learning approach. In this paper we discuss the application of artificial feedforward neural networks to deal with some fundamental problems tied with the folding process and the structure-function relationship in proteins.
Casadio
R
R
Laboratory of Biocomputing, Centro Interdipartimentale per le Ricerche Biotecnologiche (CIRB), University of Bologna, Italy. casadio@kaiser.alma.unibo.it
Compiani
M
M
Fariselli
P
P
Jacoboni
I
I
Martelli
P L
PL
D016208:Databases, Factual; D005544:Forecasting; D006801:Humans; D008967:Molecular Biology / Q000639:trends; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D013329:Structure-Activity Relationship