A protein structure predictor based on an energy model with learned parameters

Abstract A new protein folding model for obtaining low-level structure information from sequence is constructed. Its form is related both to a parameterized energy function to represent the folding problem and a feed-back “neural network”. The values of unknown physical quantities appear as free parameters in this potential function. Ideas from the study of neural network models are used to develop a learning algorithm that finds values for the free parameters by using the database of known protein structures. This algorithm can be implemented in parallel on a multicomputer. The ideas are illustrated on a simple model of α-helix formation and prediction and used to investigate the role of hydrophobic forces in stabilizing helix hydrogen bonds.

[1]  M. Levitt.  A simplified representation of protein conformations for rapid simulation of protein folding. , 1976, Journal of molecular biology.

[2]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[3]  Charis Ghélis,et al.  Protein Folding , 1982 .

[4]  G. Schulz,et al.  Structural rules for globular proteins. , 1977, Angewandte Chemie.

[5]  Frederic M. Richards,et al.  Packing of α-helices: Geometrical constraints and contact areas☆ , 1978 .

[6]  Stephen Taylor,et al.  Parallel logic programming techniques , 1989 .

[7]  H. Scheraga,et al.  Energy parameters in polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids , 1975 .

[8]  D. Eisenberg,et al.  Hydrophobic moments and protein structure , 1982 .

[9]  B. Zimm,et al.  Theory of the Phase Transition between Helix and Random Coil in Polypeptide Chains , 1959 .

[10]  T. Sejnowski,et al.  Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.

[11]  Halbert White,et al.  Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.

[12]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[13]  L. Pauling,et al.  The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain. , 1951, Proceedings of the National Academy of Sciences of the United States of America.

[14]  M. Karplus,et al.  Protein secondary structure prediction with a neural network. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[15]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[16]  P. Wolynes,et al.  Toward Protein Tertiary Structure Recognition by Means of Associative Memory Hamiltonians , 1989, Science.

[17]  L. Pauling,et al.  Configurations of Polypeptide Chains With Favored Orientations Around Single Bonds: Two New Pleated Sheets. , 1951, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Ian Foster,et al.  Strand: New Concepts in Parallel Programming , 1990 .

[19]  H. Kramers,et al.  Statistics of the Two-Dimensional Ferromagnet. Part II , 1941 .

[20]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[21]  R. Hill,et al.  THE EVOLUTIONARY ORIGINS OF THE IMMUNOGLOBULINS* , 1966, Proceedings of the National Academy of Sciences of the United States of America.

[22]  V. Lim.  Algorithms for prediction of α-helical and β-structural regions in globular proteins , 1974 .

[23]  F E Cohen,et al.  Protein folding: evaluation of some simple rules for the assembly of helices into tertiary structures with myoglobin as an example. , 1979, Journal of molecular biology.

[24]  D. Eisenberg,et al.  The hydrophobic moment detects periodicity in protein hydrophobicity. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[25]  J. Richardson,et al.  The anatomy and taxonomy of protein structure. , 1981, Advances in protein chemistry.

[26]  H. Scheraga,et al.  Prediction of the native conformation of a polypeptide by a statistical‐mechanical procedure. I. Backbone structure of enkephalin , 1985, Biopolymers.

[27]  O. Ptitsyn,et al.  Why do globular proteins fit the limited set of folding patterns? , 1987, Progress in biophysics and molecular biology.

[28]  H. Scheraga,et al.  Energy parameters in polypeptides. 9. Updating of geometrical parameters, nonbonded interactions, and hydrogen bond interactions for the naturally occurring amino acids , 1983 .

[29]  G. C. Fox,et al.  Solving Problems on Concurrent Processors , 1988 .

[30]  J. Garnier,et al.  Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. , 1978, Journal of molecular biology.

[31]  J J Hopfield,et al.  Learning algorithms and probability distributions in feed-forward and feed-back networks. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[32]  P. Y. Chou,et al.  Prediction of protein conformation. , 1974, Biochemistry.

[33]  P J Flory,et al.  Conformational energies and configurational statistics of copolypeptides containing L-proline. , 1968, Journal of molecular biology.

[34]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[35]  M Karplus,et al.  Dynamics of proteins: elements and function. , 1983, Annual review of biochemistry.

[36]  H A Scheraga,et al.  Statistical mechanical treatment of protein conformation. I. Conformational properties of amino acids in proteins. , 1976, Macromolecules.

[37]  W. Thiemann,et al.  Stereoselectivity of β irradiation of D,L-tryptophan in aqueous solution , 1976, Nature.

[38]  P. Y. Chou,et al.  Conformational parameters for amino acids in helical, beta-sheet, and random coil regions calculated from proteins. , 1974, Biochemistry.

[39]  S. Brunak,et al.  Protein secondary structure and homology by neural networks The α‐helices in rhodopsin , 1988 .

[40]  Harold A. Scheraga,et al.  Calculation of the Zimm-Bragg cooperativity parameter σ from a simple model of the nucleation process , 1983 .

[41]  K. Wüthrich.  Protein structure determination in solution by nuclear magnetic resonance spectroscopy. , 1989, Science.

[42]  Elliott W. Montroll,et al.  Statistical Mechanics of Nearest Neighbor Systems , 1941 .

[43]  M. Levitt,et al.  Computer simulation of protein folding , 1975, Nature.

[44]  C. Chothia,et al.  Structural patterns in globular proteins , 1976, Nature.

[45]  J. Mccammon,et al.  Dynamics of Proteins and Nucleic Acids , 2018 .

[46]  C. Chothia,et al.  Hydrophobic bonding and accessible surface area in proteins , 1974, Nature.

[47]  O. Ptitsyn,et al.  A model of myoglobin self-organization. , 1975, Biophysical chemistry.

[48]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[49]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.