A modular learning environment for protein modeling.
We propose in this paper a modular learning environment for protein modeling. In this system, the protein modeling problem is tackled in two successive phases. First, partial structural informations are determined via numerical learning techniques. Then, in the second phase, the multiple available informations are combined in pattern matching searches via dynamic programming. It is shown on real problems that various protein structure predictions can be improved in this way, such as secondary structure prediction, alignment of weakly homologous protein sequences or protein model evaluations.
Gracy
J
J
Laboratoire d'Informatique, de Robotique et de Micro-électronique de Montpellier, France.
Chiche
L
L
Sallantin
J
J
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D015704:CD4 Antigens / Q000737:chemistry; D003580:Cytochromes / Q000737:chemistry; D007136:Immunoglobulins / Q000737:chemistry; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D017433:Protein Structure, Secondary; D016415:Sequence Alignment / Q000379:methods; D017421:Sequence Analysis / Q000379:methods*; D014335:Tropomyosin / Q000737:chemistry
D015704:CD4 Antigens; D003580:Cytochromes; D007136:Immunoglobulins; D014335:Tropomyosin