A modular learning environment for protein modeling.

J Gracy; L Chiche; J Sallantin

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PubMed · 7584330

A modular learning environment for protein modeling.

J Gracy·L Chiche·J Sallantin

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Abstract

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.

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Amino Acid Sequence Artificial Intelligence CD4 Antigens Cytochromes Immunoglobulins Models, Molecular Molecular Sequence Data Neural Networks, Computer Pattern Recognition, Automated Protein Structure, Secondary Sequence Alignment Sequence Analysis Tropomyosin Comparative Study Journal Article Research Support, Non-U.S. Gov't

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J Gracy, L Chiche, J Sallantin. 1993. A modular learning environment for protein modeling.. https://pubmed.ncbi.nlm.nih.gov/7584330/

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