Protein modeling applications

Barr JR, Maggio VL, Patterson DG Jr, Cooper GR, Henderson LO, Turner WE, Smith SJ, Hannon WH, Needham LL, Sampson EJ. Isotope dilution—Mass spectrometric quantification of specific proteins Model application with apolipoprotein A-I. Clin Chem 1996 42(10) 1676-1682. 

Barr, J.R., Maggio, V.L., Patterson, D.G. Jr., et al. (1996) Isotope dilution—mass spectrometric quantification of specific proteins model application with apohpoprotein A-I. Clinical Chemistry, 42,1676-1682. 

The original bovine a-lactalbumin protein model was constructed before the availability of efficient sequence alignment programs and computational protein modeling applications. Instead of searching for similar sequences and structures using BLAST (because it did not exist), Browne et al. considered only four different solved protein structures as templates. To explore the... 

The first term represents the forces due to the electrostatic field, the second describes forces that occur at the boundary between solute and solvent regime due to the change of dielectric constant, and the third term describes ionic forces due to the tendency of the ions in solution to move into regions of lower dielectric. Applications of the so-called PBSD method on small model systems and for the interaction of a stretch of DNA with a protein model have been discussed recently ([Elcock et al. 1997]). This simulation technique guarantees equilibrated solvent at each state of the simulation and may therefore avoid some of the problems mentioned in the previous section. Due to the smaller number of particles, the method may also speed up simulations potentially. Still, to be able to simulate long time scale protein motion, the method might ideally be combined with non-equilibrium techniques to enforce conformational transitions. 

Abstract. Molecular dynamics (MD) simulations of proteins provide descriptions of atomic motions, which allow to relate observable properties of proteins to microscopic processes. Unfortunately, such MD simulations require an enormous amount of computer time and, therefore, are limited to time scales of nanoseconds. We describe first a fast multiple time step structure adapted multipole method (FA-MUSAMM) to speed up the evaluation of the computationally most demanding Coulomb interactions in solvated protein models, secondly an application of this method aiming at a microscopic understanding of single molecule atomic force microscopy experiments, and, thirdly, a new method to predict slow conformational motions at microsecond time scales. 

Mian, K Sjolander and D Haussler 1994. Hidden Markov Models in Computational Biology. Applications to Protein Modelling. Journal of Molecular Biology 235 1501-1531). 

The utility of a protein model depends upon the use to which it is put. In some cases, on< only interested in the general fold that the protein adopts and so a relatively low-resoluti structure is acceptable. For other applications, such as drug design, the model must be me more accurate, including the loops and side chains. In such cases, a poor model may often fa r worse than no model at all, as it can be seriously misleading. 

At this time, approximately one-half of all sequences are delectably related to at least one protein of known structure [8-11]. Because the number of known protein sequences is approximately 500,000 [12], comparative modeling could in principle be applied to over 200,000 proteins. This is an order of magnitude more proteins than the number of experimentally determined protein structures (—13,000) [13]. Furthermore, the usefulness of comparative modeling is steadily increasing, because the number of different structural folds that proteins adopt is limited [14,15] and because the number of experimentally determined structures is increasing exponentially [16]. It is predicted that in less than 10 years at least one example of most structural folds will be known, making comparative modeling applicable to most protein sequences [6]. 

A Krogh, M Brown, IS Mian, K Sjolander, D Haussler. Hidden Markov models m computational biology Applications to protein modeling. I Mol Biol 235 1501-1531, 1994. 

APPLICATIONS TO ENZYMATIC REACTIONS 2.3.1. Active-Site and Protein Models... 

Krogh, A., Larsson, B von Heijne, G., and Sonnhammer, E. L. (2001) Predicting transmembrane protein topology with a hidden Markov model application to complete genomes.. /. Mol. Biol. 305, 567-580. 

Abagyan, R., Totrov, M., and Kuznetsov, D. ICM - a new method for protein modeling and design applications to docking and stmcture prediction from the distorted native conformation./. Comput. Chem. 1994, 35, 488-506. 

Predicting binding affinities of protein ligands from three-dimensional models application to peptide binding to Class I major histocompatibility proteins. 

Krogh A, Brown M, Mian IS, Sjolander K, Haussler D (1994) Hidden Markov models in computational biology. Applications to protein modeling. J Mol Biol 235 1501-1531... 

This chapter focuses on the application of molecular modeling to calculate, manipulate, and predict protein structures and functions. Concepts of structure similarity/ overlap, homology modeling, and molecular docking, which are special concerns of protein biochemists, are considered. Approaches to protein modeling by the use of two programs (Swiss-Pdb Viewer and KineMage) and two online servers (B and CE) are described. 

Most comprehensive software programs suitable for protein modeling are commercial packages, some of which are listed in Table 15.2. The application of HyperChem in the biomolecular modeling has been described (Chapter 14) and can be extended to the modeling of protein structures. The aspects of protein modeling will be illustrated with two freeware programs and two online servers. 

Jean P, Pothier J, Dansette PM, et al. Automated multiple analysis of protein structures application to homology modeling of cytochromes P450. Proteins 1997 28 388-404. 

---