In this paper we examine the selection.

A Glossary of Terms Used in Protein Crystallography.. - - - - A KNOWLEDGE-BASED SYSTEM FOR THE INTERPRETATION OF PROTEIN X-RAY CRYSTALLOGRAPHIC DATA ABSTRACT The broad goal of this project is to develop intelligent computational systems to infer the three-dimensional structures of proteins from x-ray crystallographic data. The computational systems under development use both formal and judgmental knowledge from experts to select appropriate procedures and to constrain the space of plausible protein structures. The hypothesis generating and testing procedures operate upon a variety of representations of the data, and work with several different descriptions of the structure being inferred. The system consists of a number of independent but cooperating knowledge sources which propose, augment and verify a sol.uticn to the problem as it is incrementally generated.

This article is concerned with the general question of how to represent biological knowledge in computers such that it may be used in multiple problem solving tasks. In particular, I present a model of a bacterial gene regulation system that is used by a program that simulates gene regulation experiments, and by a second program that formulates hypotheses to account for errors in predicted experiment outcomes. This article focuses on the issues of representation and simulation; for more information on the hypothesis formation task see (Karp, 1989; Karp, 1990). The bacterial gene regulation system of interest is the tryptophan (trp) operon of E. coli (Yanofsky, 1981). The genes that it contains code for enzymes that synthesize the amino acid tryptophan.

The use of computers in chemistry and biochemistry has been widespread for many years, with machines performing many complex numerical calculations, e.g.

This chapter will discuss one example of how AI techniques are being integrated with, and extending, existing molecular biology sequence analysis methods. AI ideas of complex representations, pattern recognition, search, and machine learning have been applied to the task of inferring and recognizing structural patterns associated with molecular function. We wish to construct such patterns, and to recognize them in unknown molecules, based on information inferred solely from protein primary (amino acid) sequences.

The RNA secondary structure prediction problem (2 RNA) is a critical one in molecular biology. Secondary structure can be determined directly by x-ray diffraction, but this is difficult, slow, and expensive. Moreover, it is currently impossible to crystallize most RNAs. Mathematical models for prediction have therefore been developed and these have led to serial (and some parallel) computer algorithms, but these too are expensive in terms of computation time. The general solution has asymptotic running time exponential in N (i.e., proportional to 2 N), where N is the length of the RNA sequence. Serial approximation algorithms which employ heuristics and make strong assumptions are significantly faster, on the order of N 3 or N 4, but their predictive success rates are low -- often less than 40 percent -- and even these algorithms can run for days when processing very long (thousands of bases) RNA sequences. Neural network algorithms that perform a multiple constraint satisfaction search using a massively parallel network of simple processors may provide accurate and very fast solutions.

The prediction of protein structure from amino acid sequence has become the Holy Grail of computational molecular biology. Since Anfinsen [1973] first noted that the information necessary for protein folding resides completely within the primary structure, molecular biologists have been fascinated with the possibility of obtaining a complete three-dimensional picture of a protein by simply applying the proper algorithm to a known amino acid sequence. The development of rapid methods of DNA sequencing coupled with the straightforward translation of the genetic code into protein sequences has amplified the urgent need for automated methods of interpreting these one-dimensional, linear sequences in terms of three-dimensional structure and function. Although improvements in computational capabilities, the development of area detectors, and the widespread use of synchrotron radiation have reduced the amount of time necessary to determine a protein structure by X-ray crystallography, a crystal structure determination may still require one or more man-years.

He also taught the biochemistry course that I finally took, two years after finishing my Ph.D. David J. States deserves much of the credit as well. In the three years we have been working together, he greatly extended my understanding of not only what biologists know, but how they think. He has read several drafts of this chapter and made helpful suggestions. David Landsman, Mark Boguski, Kalí Tal and Jill Shirmer have also read the chapter and made suggestions. Angel Lee graciously supplied the gel used in Figure 4. Of course, all remaining mistakes are my responsibility.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

Current research has succeeded in despite much work attempting to do so, human-- exploring a large number of domains and has explored machine communication is not yet sensitive to dialogue some nontraditional pedagogical strategies, such as partnering, context and to what is known or knowable about the student's mentoring, and scaffolding.