The increasing popularity of 13 C techniques and the increasing bulk of available data have motivated us to develop a computer program which generates empirical 13 C NUR rules. A natural abundance 13 C NMR spectrum which is fully proton-- decoupled consists of a number of sharp peaks ccrresponding to the resonance frequencies in the applied magnetic field of the various types of carbon atoms present in the sample. A 13 C shift is the amount a peak position deviates from a reference peak of a standard compound usually tetramethylsilane (T1s). Methods for obtaining empirical rules which correlate 13 C shifts with local structural environments within a class of compounds are cited in the literature.

Almost one-half of the total cost of drugs spent in treating hospitalized patients is spend on antibiotics (1,2), and a significant part of this therapy is associated with serious misuse (2,3,4,5). One problem involves incorrect selection of a therapeutic regimen [4], while another involves the incorrect decision to administer any antibiotic (2,4,5). For example, one recent study concluded that one out of every four people in the United States was given penicillin during a recent year, and nearly 907. of these preL,criptions were oahccessary (6).

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.

ABSTRACT A computer program that models an expert in a given domain is more likek to be accepted by experts in that domain, and by non-experts seeking its gavice. An explanation capability not only adds to the system's credibility, but also enables the non-expert user to learn from it. Furthermore, clear explanations allow an expert to check the system's "reasoning", possibly discovering the need for refinements and additions to the svstem's knowledge base. In a developing system, an explanation capability can be used as a debugging aid to verify that additions to the system are working as'hey should. The explanation facility in MYCIN is discussed as an illustration of how the various problems might be approached.

This chapter describes the design of a prototype knowledge-based system for crystal and molecular structure determination from diffraction data. This system enhances current methods for the determination and interpretation of protein structures by incorporating direct methods probabilistic strategies, experience accumulated in the crystallographic databases, and knowledge representation and reasoning techniques for machine imagery.

Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and AI gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.

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.

Our understanding of any process can be measured by the extent to which a simulation we create mimics the real behavior of that process. Deviations of a simulation indicate either limitations or errors in our knowledge. In addition, these observed differences often suggest verifiable experimental hypotheses to extend our knowledge. The biochemical approach to understanding biological processes is essentially one of simulation. A biochemist typically prepares a cell-free extract that can mediate a well-described physiological process. The extract is then fractionated to purify the components that catalyze individual reactions.

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 protein folding problem has been attacked from many directions. One set of approaches tries to find out correlations between short subsequences of proteins and the structures they form, using empirical information from crystallographic databases. AI research has repeatedly demonstrated the importance of representation in making these kinds of inferences. In this chapter, we describe an attempt to find a good representation for protein substructure. Our goal is to represent protein structures in such a way that they can, on the hand, reflect the enormous complexity and variety of different protein structures, and yet on the other hand facilitate the identification of similar substructures across different proteins.