Methods to predict protein spatial structure

Modern methods of predicting protein spatial structure are reviewed. Numerical results of predicting the secondary structure of protein on the basis of Bayesian recognition procedures on nonstationary Markov chains are discussed. Complementary principles of encoding genetic information in DNA and proteins are presented.

[1]  A. M. Gupal,et al.  Protein Secondary Structure Recognition Procedure , 2007 .

[2]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[3]  Pierre Baldi,et al.  Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners , 2002, ISMB.

[4]  Huajun Chen,et al.  Introduction to semantic e-Science in biomedicine , 2007, BMC Bioinformatics.

[5]  Yves A. Lussier,et al.  Evaluation of high-throughput functional categorization of human disease genes , 2007, BMC Bioinformatics.

[6]  B Jayaram,et al.  A computational pathway for bracketing native-like structures fo small alpha helical globular proteins. , 2005, Physical chemistry chemical physics : PCCP.

[7]  K. Sharp,et al.  Potential energy functions for protein design. , 2007, Current opinion in structural biology.

[8]  A. M. Gupal,et al.  Prediction of Secondary Structure of Proteins on the Basis of Bayesian Recognition Procedures , 2007 .

[9]  I V Sergienko,et al.  [Statistical analysis of genome]. , 2004, TSitologiia i genetika.

[10]  Alexandra A. Vagis Complementarity Principles of Bases in DNA Chromosomes , 2005 .

[11]  M. Vassura,et al.  Reconstruction of 3D Structures From Protein Contact Maps , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  M. Karplus,et al.  Effective energy functions for protein structure prediction. , 2000, Current opinion in structural biology.

[13]  N. Grishin,et al.  Practical lessons from protein structure prediction , 2005, Nucleic acids research.

[14]  T. W. Anderson,et al.  Statistical Inference about Markov Chains , 1957 .

[15]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[16]  I. V. Sergienko,et al.  Predicting protein secondary structure based on Bayesian classification procedures on Markovian chains , 2007 .

[17]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[18]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP)—round IX , 2011, Proteins.

[19]  Krzysztof Fidelis,et al.  Progress from CASP6 to CASP7 , 2007, Proteins.

[20]  I V Sergienko,et al.  [Complementary principles of bases recoding along one chain of DNA]. , 2005, TSitologiia i genetika.

[21]  Pierre Baldi,et al.  Improved residue contact prediction using support vector machines and a large feature set , 2007, BMC Bioinformatics.

[22]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.