Structure prediction

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Although there has been an exponential growth of computational organotin studies in the last 30 years, a substantial portion of the current publications combine both computational and experimental techniques. This chapter includes examples of this type along with those based on only computational methods. While the majority of the current literature is included, in depth discussion is reserved for the most intriguing studies to give an overview of the current state of computational organotin chemistry. 

Ideally, computational methods would include both electron correlation and relativistic effects. Instead, some studies address ether electron correlation or relativistic effects, but not both.  

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Returning now to the issue of the accuracy of various electronic structure predictions, it is natural to ask why... 

Unfortunately, the approach of determining empirical potentials from equilibrium data is intrinsically limited, even if we assume complete knowledge of all equilibrium geometries and their energies. It is obvious that statistical potentials cannot define an energy scale, since multiplication of a potential by a positive, constant factor does not alter its global minimizers. But for the purpose of tertiary structure prediction by global optimization, this does not not matter. 

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P. Ulrich, W. Scott, W.F. van Gunsteren and A. Torda, Protein structure prediction force 6elds parametrization with quasi Newtonian dynamics. Proteins 27 (1997), 367-384. 

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Protein Structure Prediction, Sequence Analysis and Protein Folding... 

Rule-based Approaches Using Secondary Structure Prediction... 

Ihe rule-based approach to protein structure prediction is obviously very reliant on th quality of the initial secondary structure prediction, which may not be particularly accurate The method tends to work best if it is known to which structural class the protein belongs this can sometimes be deduced from experimental techniques such as circular dichroism... 

A Comparison of Protein Structure Prediction Pilethocls CASP-... 

CASP stands for Critical Assessment of techniques for protein Structure Prediction. 

Barton G J1996. Protein Sequence Alignment and Database Scanning. In Sternberg M E (Editor) Prote Structure Prediction - A Practical Approach. Oxford, IRL Press, pp. 31-63. 

Cuff IA and G J Barton 1999. Evaluation and Improvement of Multiple Sequence Methods for P Secondary Structure Prediction. Proteins Structure, Function and Genetics 34 508-519. 

Moult J, T Hubbard, K Fldelis and J T Pedersen 1999. Critical Assessment of Methods of Protein Structure Prediction (CASP) Round III. Proteins Structure, Function and Genetics Suppl. 3 2-6. 

GJ Barton. Protein sequence alignment and database scanning. In MJE Sternberg, ed. Protein Structure Prediction A Practical Approach. Oxford, UK IRE Press at Oxford Univ Press, 1998. 

I Wojcik, I-P Mornon, I Chomilier. New efficient statistical sequence-dependent structure prediction of short to medium-sized protein loops based on an exhaustive loop classification. I Mol Biol 289 1469-1490, 1999. 

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MJE Sternberg, PA Bates, LA Kelley, RM MacCallum. Progress m protein structure prediction Assessment of CASP3. Curr Opm Struct Biol 9 368-373, 1999. 

DM Standley, JR Gunn, RA Friesner, AE McDermott. Tertiary structure prediction of mixed alpha/beta proteins via energy minimization. Proteins 33 240-252, 1998. 

J Moult, T Flubbard, SFI Bryant, K Fidelis, JT Pedersen. Critical assessment of methods of protein structure prediction (CASP) Round II. Proteins Suppl 1 2-6, 1997. 

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For example, Stolorz et al. [88] derived a Bayesian formalism for secondary structure prediction, although their method does not use Bayesian statistics. They attempt to find an expression for / ( j. seq) = / (seq j.)/7( j.)//7(seq), where J. is the secondary structure at the middle position of seq, a sequence window of prescribed length. As described earlier in Section II, this is a use of Bayes rule but is not Bayesian statistics, which depends on the equation p(Q y) = p(y Q)p(Q)lp(y), where y is data that connect the parameters in some way to observables. The data are not sequences alone but the combination of sequence and secondary structure that can be culled from the PDB. The parameters we are after are the probabilities of each secondary structure type as a function of the sequence in the sequence window, based on PDB data. The sequence can be thought of as an explanatory variable. That is, we are looking for... 

Thompson and Goldstein [89] improve on the calculations of Stolorz et al. by including the secondary structure of the entire window rather than just a central position and then sum over all secondary strucmre segment types with a particular secondary structure at the central position to achieve a prediction for this position. They also use information from multiple sequence alignments of proteins to improve secondary structure prediction. They use Bayes rule to fonnulate expressions for the probability of secondary structures, given a multiple alignment. Their work describes what is essentially a sophisticated prior distribution for 6 i(X), where X is a matrix of residue counts in a multiple alignment in a window about a central position. The PDB data are used to form this prior, which is used as the predictive distribution. No posterior is calculated with posterior = prior X likelihood. 

TJ Hubbard, J Park. Eold recognition and ab initio structure predictions using hidden Markov models and (I-strand pair potentials. Proteins Struct Eunct Genet 23 398-402, 1995. 

JE Gibrat, J Garnier, B Robson. Eurther developments of protein secondary structure prediction using information theory. New parameters and consideration of residue pairs. J Mol Biol 198 425-443, 1987. 

LH Holley, M Karplus. Protein secondary structure prediction with a neural network. Proc Natl Acad Sci USA 86 152-156, 1989. 

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