Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

A two-year experience teaching computer literacy to first-year medical students using skill-based cohorts

Because it is widely accepted that providing information online will play a major role in both the teaching and practice of medicine in the near future, a short formal course of instruction in computer skills was proposed for the incoming class of students entering medical school at the State University of New York at Stony Brook. The syllabus was developed on the basis of a set of expected outcomes, which was accepted by the dean of medicine and the curriculum committee for classes beginning in the fall of 1997. Prior to their arrival, students were asked to complete a self-assessment survey designed to elucidate their initial skill base; the returned surveys showed students to have computer skills ranging from complete novice to that of a systems engineer. The classes were taught during the first three weeks of the semester to groups of students separated on the basis of their knowledge of and comfort with computers. Areas covered included computer basics, e-mail management, MEDLINE, and Internet search tools. Each student received seven hours of hands-on training followed by a test. The syllabus and emphasis of the classes were tailored to the initial skill base but the final test was given at the same level to all students. Student participation, test scores, and course evaluations indicated that this noncredit program was successful in achieving an acceptable level of comfort in using a computer for almost all of the student body.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.