Protein sequence-structure methods

G Vriend, C Sander, PFW Stouten. A novel search method for protein sequence-structure relations using property profiles. Protein Eng 7 23-29, 1994. 

To gain the most predictive utility as well as conceptual understanding from the sequence and structure data available, careful statistical analysis will be required. The statistical methods needed must be robust to the variation in amounts and quality of data in different protein families and for structural features. They must be updatable as new data become available. And they should help us generate as much understanding of the determinants of protein sequence, structure, dynamics, and functional relationships as possible. 

Evaluation of protein sequence analysis methods based on the use of PSSMs in benchmarking experiments and in a number of test cases shows that these methods are capable of systematically detecting relationships between proteins that previously have been deemed tractable only at the structure-comparison level. Clearly, however, there is still a lot of room for improvement, as many automated procedures missed subtle connections that subsequendy have been revealed on a case-by-case basis, in part thanks to a careful choice of starting points for the PSSM construction. An exhaustive exploration of the sequence space by recursive iterative searching is likely to yield additional, on many occasions unexpected, links between proteins and, in particular, is expected to increase the rate of structure prediction. 

Structural proteomics aims the determination of three-dimensional protein structures in order to better understand the relationship between protein sequence, structure, and function. NMR and x-ray crystallography have been significant methods and indispensable tools to determine the structure of macromolecules, especially proteins. Many biotechnology companies have been using these two techniques for enlightening protein structure (Table 5.5). 

Another recent trend is to show the importance of hydrophobic profiles rather than molecular hydrophobicity. Giuliani et al. (2002) suggested nonlinear signal analysis methods in the elucidation of protein sequence-structure relationships. The major algorithm used for analyzing hydrophobicity sequences or profiles was recurrence quantification analysis (RQA), in which a recurrence plot depicted a single trajectory as a two-dimensional representation of experimental time-series data. Examples of the global properties used in this... 

Giuliani, A., Benigni, R., Zbilut, J.P., Webber, C.L. Jr, Sirabella, P and Colosimo, A. 2002. Nonlinear signal analysis methods in the elucidation of protein sequence-structure relationships. Chem. Rev. 102 1471-1492. 

There is a considerable impetus to predict accurately protein structures from sequence information because of the protein sequence/structure deficit as a consequence of the genome and full-length cDNA sequencing projects. The molecular mechanical (MM) approach to modeling of protein structures has been discussed in section 9.2, and the protein secondary structure prediction from sequence by statistical methods has been treated in section 9.5. The prediction of protein structure using bioinformatic resources will be described in this subsection. The approaches to protein structure predictions from amino acid sequences (Tsigelny, 2002 Webster, 2000) include ... 

Fig, 10.25 The six environment categories used by the 3D profiles method. (Figure adapted from Bowie j U, R Liith. and D Eisenberg 1991. A Method to Identify Protein Sequences That Fold into a Known Three-Dunensinnal Structure. Science 253 164-170.)... 

JU Bowie, R Liithy, D Eisenberg. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253 164-170, 1991. 

Mann, M., and Wilm, M., 1995. Electro.spray ma.ss. spectrometry for protein characterization. Trends in Biochemical Sciences 20 219-224. A review of die ba.sic application of ma.ss. spectrometric methods to the analysis of protein. sequence and. structure. 

Moreover, molecular modeling is one key method of a wide range of computer-assisted methods to analyze and predict relationships between protein sequence, 3D-molecular structure, and biological function (sequence-structure-function relationships). In molecular pharmacology these methods focus predominantly on analysis of interactions between different proteins, and between ligands (hormones, drugs) and proteins as well gaining information at the amino acid and even to atomic level. 

The molecular replacement method assumes similarity of the unknown structure to a known one. This is the most rapid method but requires the availability of a homologous protein s structure. The method relies on the observation that proteins which are similar in their amino acid sequence (homologous) will have very similar folding of their polypeptide chains. This method also relies on the use of Patterson functions. As the number of protein structure determinations increases rapidly, the molecular replacement method becomes extremely useful for determining protein phase angles. 

The wide structural application of dipolar couplings is demonstrated by its use to validate models built by sequence homology methods. Additionally, dipolar couplings have been shown to reduce the RMSD between these models and the target structure. One example is the work reported by Chou et al., in which the RMSD of sequence homology models of the protein calmodulin, built from the structure of recoverin and parvalbumin, is reduced using heteronuclear dipolar couplings [110]. 

Protein domains are the common currency of protein structure and function. Protein domains are discrete structural units that fold up to form a compact globular shape. Experiments on protein structure and function have been greatly aided by consideration of the modular nature of proteins. This has allowed very large proteins to be studied. The expression of individual domains has allowed the intractable giant muscle protein titin to be structurally studied (Pfuhl and Pastore, 1995). Protein domains can be found in a variety of contexts, (Fig. 1), in association with a range of unrelated domains and in a variety of orders. Ultimately protein domains are defined at the level of three-dimensional structure however, many protein domains have been described at the level of sequence. The success of sequence-based methods has been demonstrated by numerous confirmations, by elucidation of the three-dimensional structure of the domain. 

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