323 results found
King RD, Muggleton SH, Srinivasan A, et al., 1996, Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 93, Pages: 438-442, ISSN: 0027-8424
King RD, Srinivasan A, Sternberg MJE, 1996, Relating chemical activity to structure: An examination of ILP successes (vol 13, pg 411, 1995), NEW GENERATION COMPUTING, Vol: 14, Pages: 109-109, ISSN: 0288-3635
Bates PA, Jackson RM, Sternberg MJE, 1996, Prediction of protein structures and their docking, 7th SmithKline-Beecham International Symposium on Genomes, Molecular Biology and Drug Discovery, Publisher: ACADEMIC PRESS LTD, Pages: 73-86
Sternberg MJE, 1996, Protein Structure Prediction - A Practical Approach, Oxford, Publisher: Oxford University Press
ELLIS SW, HAYHURST GP, SMITH G, et al., 1995, EVIDENCE THAT ASPARTIC-ACID-301 IS A CRITICAL SUBSTRATE-CONTACT RESIDUE IN THE ACTIVE-SITE OF CYTOCHROME-P450 2D6, JOURNAL OF BIOLOGICAL CHEMISTRY, Vol: 270, Pages: 29055-29058, ISSN: 0021-9258
DOIG AJ, STERNBERG MJE, 1995, SIDE-CHAIN CONFORMATIONAL ENTROPY IN PROTEIN-FOLDING, PROTEIN SCIENCE, Vol: 4, Pages: 2247-2251, ISSN: 0961-8368
CHICKOS JS, STERNBERG MJE, 1995, A TEST OF THE APPLICABILITY OF SMALL-MOLECULE GROUP ADDITIVITY PARAMETERS IN THE ESTIMATION OF FUSION ENTROPIES OF MACROMOLECULES, THERMOCHIMICA ACTA, Vol: 264, Pages: 13-26, ISSN: 0040-6031
JACKSON RM, STERNBERG MJE, 1995, A CONTINUUM MODEL FOR PROTEIN-PROTEIN INTERACTIONS - APPLICATION TO THE DOCKING PROBLEM, JOURNAL OF MOLECULAR BIOLOGY, Vol: 250, Pages: 258-275, ISSN: 0022-2836
ISLAM SA, LUO JC, STERNBERG MJE, 1995, IDENTIFICATION AND ANALYSIS OF DOMAINS IN PROTEINS, PROTEIN ENGINEERING, Vol: 8, Pages: 513-525, ISSN: 0269-2139
RUSSELL RB, STERNBERG MJE, 1995, STRUCTURE PREDICTION - HOW GOOD ARE WE, CURRENT BIOLOGY, Vol: 5, Pages: 488-490, ISSN: 0960-9822
STERNBERG MJE, MUGGLETON S, SHRINIVASAN A, 1995, DRUG DESIGN BY MACHINE LEARNING, ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, Vol: 209, Pages: 165-COMP, ISSN: 0065-7727
KING RD, HIRST JD, STERNBERG MJE, 1995, COMPARISON OF ARTIFICIAL-INTELLIGENCE METHODS FOR MODELING PHARMACEUTICAL QSARS, APPLIED ARTIFICIAL INTELLIGENCE, Vol: 9, Pages: 213-233, ISSN: 0883-9514
KING RD, STERNBERG MJE, SRINIVASAN A, 1995, RELATING CHEMICAL ACTIVITY TO STRUCTURE - AN EXAMINATION OF ILP SUCCESSES, NEW GENERATION COMPUTING, Vol: 13, Pages: 411-433, ISSN: 0288-3635
Sternberg MJ, Hegyi H, Islam SA, et al., 1995, Towards an intelligent system for the automatic assignment of domains in globular proteins., Proc Int Conf Intell Syst Mol Biol, Vol: 3, Pages: 376-383, ISSN: 1553-0833
The automatic identification of protein domains from coordinates is the first step in the classification of protein folds and hence is required for databases to guide structure prediction. Most algorithms encode a single concept based and sometimes do not yield assignments that are consistent with the generally accepted perception. Our development of an automatic approach to identify reliably domains from protein coordinates is described. The algorithm is benchmarked against a manual identification of the domains in 284 representative protein chains. The first step is the domain assignment by distance (DAD) algorithm that considers the density of inter-residue contacts represented in a contact matrix. The algorithm yields 85% agreement with the manual assignment. The paper then considers how the reliability of these assignments could be evaluated. Finally the use of structural comparisons using the STAMP algorithm to validate domain assignment is reported on a test case.
HARRISON PM, STERNBERG MJE, 1994, ANALYSIS AND CLASSIFICATION OF DISULFIDE CONNECTIVITY IN PROTEINS - THE ENTROPIC EFFECT OF CROSS-LINKAGE, JOURNAL OF MOLECULAR BIOLOGY, Vol: 244, Pages: 448-463, ISSN: 0022-2836
ADZHUBEI AA, STERNBERG MJE, 1994, CONSERVATION OF POLYPROLINE-II HELICES IN HOMOLOGOUS PROTEINS - IMPLICATIONS FOR STRUCTURE PREDICTION BY MODEL-BUILDING, PROTEIN SCIENCE, Vol: 3, Pages: 2395-2410, ISSN: 0961-8368
KING RD, CLARK DA, SHIRAZI J, et al., 1994, ON THE USE OF MACHINE LEARNING TO IDENTIFY TOPOLOGICAL RULES IN THE PACKING OF BETA-STRANDS, PROTEIN ENGINEERING, Vol: 7, Pages: 1295-1303, ISSN: 0269-2139
STERNBERG MJE, MUGGLETON S, SHRINIVASAN A, 1994, DRUG DESIGN BY MACHINE LEARNING, ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, Vol: 208, Pages: 138-COMP, ISSN: 0065-7727
HIRST JD, KING RD, STERNBERG MJE, 1994, QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS BY NEURAL NETWORKS AND INDUCTIVE LOGIC PROGRAMMING .1. THE INHIBITION OF DIHYDROFOLATE-REDUCTASE BY PYRIMIDINES, JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, Vol: 8, Pages: 405-420, ISSN: 0920-654X
HIRST JD, KING RD, STERNBERG MJE, 1994, QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS BY NEURAL NETWORKS AND INDUCTIVE LOGIC PROGRAMMING .2. THE INHIBITION OF DIHYDROFOLATE-REDUCTASE BY TRIAZINES, JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, Vol: 8, Pages: 421-432, ISSN: 0920-654X
STERNBERG MJE, KING RD, LEWIS RA, et al., 1994, APPLICATION OF MACHINE LEARNING TO STRUCTURAL MOLECULAR-BIOLOGY, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES, Vol: 344, Pages: 365-371, ISSN: 0962-8436
JACKSON RM, STERNBERG MJE, 1994, APPLICATION OF SCALED PARTICLE THEORY TO MODEL THE HYDROPHOBIC EFFECT - IMPLICATIONS FOR MOLECULAR ASSOCIATION AND PROTEIN STABILITY, PROTEIN ENGINEERING, Vol: 7, Pages: 371-383, ISSN: 0269-2139
STERNBERG MJE, CHICKOS JS, 1994, PROTEIN SIDE-CHAIN CONFORMATIONAL ENTROPY DERIVED FROM FUSION DATA - COMPARISON WITH OTHER EMPIRICAL SCALES, PROTEIN ENGINEERING, Vol: 7, Pages: 149-155, ISSN: 0269-2139
SAQI MAS, STERNBERG MJE, 1994, IDENTIFICATION OF SEQUENCE MOTIFS FROM A SET OF PROTEINS WITH RELATED FUNCTION, PROTEIN ENGINEERING, Vol: 7, Pages: 165-171, ISSN: 0269-2139
, 1994, Application of machine learning to protein structure prediction and drug design, ISSN: 0963-3308
The use of inductive-based logic programming (ILP) in predicting protein structure and drug design was discussed in this article. In the study of alpha and beta fields with alternating alpha-helices and beta-sheet strands, ILP program GOLEM was employed. GOLEM was capable of generating an inductive hypothesis based on coded facts and negative counter-example and chemical background knowledge. However, for drug design machine learning program PROLOG was applied. PROLOG can comprehend the properties of bonds and atoms. It can also give insight in the chemical principles of aromatic and heteroaromatic nitro compounds. In conclusion, machine learning ILP programs can greatly enhance biochemical developments.
King RD, Clark DA, Shirazi J, et al., 1994, Inductive logic programming used to discover topological constraints in protein structures., Proc Int Conf Intell Syst Mol Biol, Vol: 2, Pages: 219-226, ISSN: 1553-0833
This paper describes the application of the Inductive Logic Programming (ILP) program GOLEM to the discovery of constraints in the packing of beta-sheets in alpha/beta proteins. These constraints (rules) have a role in understanding the protein folding problem. Constraints were learnt for four features of beta-sheet packing: the winding direction of two sequential strands, whether two consecutive strands pack parallel or anti-parallel, whether two strands pack adjacently, and whether a beta-strand is at an edge. Investigation of the learnt constraints revealed interesting patterns, some of which were previously known, others that were novel. Novel features include the discovery: that the relationship between pairs of sequential strands is in general one of decreasing size, and that more sequential pairs of strands wind in the direction out than the direction in. We conclude that machine learning has a useful place in molecular biology as a pattern discovery tool.
JACKSON RM, STERNBERG MJE, 1993, PROTEIN SURFACE-AREA DEFINED, NATURE, Vol: 366, Pages: 638-638, ISSN: 0028-0836
, 1993, New approaches to QSAR: Neural networks and machine learning, Perspectives in Drug Discovery and Design, Vol: 1, Pages: 279-290, ISSN: 0928-2866
Neural networks and machine learning are two methods that are increasingly being used to model QSARs. They make few statistical assumptions and are nonlinear and nonparametric. We describe back-propagation from the field of neural networks, and GOLEM from machine learning, and illustrate their learning mechanisms using a simple expository problem. Back-propagation and GOLEM are then compared with multiple linear regression (using the parameters and their squares) on two real drug design problems: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines and the inhibition of rat/mouse tumour DHFR by triazines. © 1993 ESCOM Science Publishers B.V.
LOFTS FJ, HURST HC, STERNBERG MJE, et al., 1993, SPECIFIC SHORT TRANSMEMBRANE SEQUENCES CAN INHIBIT TRANSFORMATION BY THE MUTANT NEU GROWTH-FACTOR RECEPTOR IN-VITRO AND IN-VIVO, ONCOGENE, Vol: 8, Pages: 2813-2820, ISSN: 0950-9232
BAX B, BLABER M, FERGUSON G, et al., 1993, PREDICTION OF THE 3-DIMENSIONAL STRUCTURES OF THE NERVE GROWTH-FACTOR AND EPIDERMAL GROWTH-FACTOR BINDING-PROTEINS (KALLIKREINS) AND AN HYPOTHETICAL STRUCTURE OF THE HIGH-MOLECULAR-WEIGHT COMPLEX OF EPIDERMAL GROWTH-FACTOR WITH ITS BINDING-PROTEIN, PROTEIN SCIENCE, Vol: 2, Pages: 1229-1241, ISSN: 0961-8368
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.