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In Leder, P., Clayton, D. A. and Rubenstein, E. (Ed.), Scientific American: Introduction to Molecular Medicine (pp. 153-168). New York NY: Scientific American Inc. 1994.

Understanding the Human Genome.

D. L. Brutlag

Department of Biochemistry, Stanford University School of Medicine, Stanford California 94305-5307.

To reap the most benefit from the human genome we will have be able to understand the meaning of the genetic sequences. The methods currently available for interpreting DNA and protein sequences largely utilize evolutionary homology. The consensus sequence method looks for highly conserved amino acids or bases in specific locations. The weight matrix or profile methods perform the same task quantitatively. Sequence alignments even attempt to recapitulate evolution by specifically postulating substitution, insertion and deletion events that occurred during the evolutionary process. Using these evolutionary based methods, this article has shown how much hypothetical information can be gained from the study of a single gene and protein molecule.

However, these evolutionary methods do not give much insight into the flow of genetic information from genes to structure and to phenotype as discussed in the introduction. What is truly needed are methods that can predict structure and function based on physical and chemical principles. Such methods will have to embody knowledge about how proteins fold, how they mediate catalysis, how they interact and how they determine phenotype. Research is ongoing along these lines. Molecular dynamics hopes to predict the structure of DNA, RNA, and proteins from physical principles. Automated learning methods including those that utilize neural networks are directed at discovering these physical principles based on the large amounts of sequence information that we now have. Probabilistic networks and other statistical methods may also reveal principles of physical structure and function based on examples in the growing public databases.

Even without these sophisticated informatic methods, we can still gain much from the sequences contained in the human genome. At the very least we will be able to design DNA diagnostic probes for many if not all inherited disease. Using the genome sequences coupled with recombinant DNA technology we will be able to synthesize and mass produce any human proteins with therapeutic value. With the the ability to decode the genetic information we will be able to understand the nature of the disease state and design more rational therapies in the short term and genetic therapies in the long term.

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