Current Protein and Peptide Science - Volume 10, Issue 3, 2009

The myelin basic protein (MBP) family comprises a variety of developmentally-regulated members arising from different transcription start sites, differential splicing, and post-translational modifications. The “classic” isoforms of MBP include the 18.5 kDa form, which predominates in adult human myelin and facilitates compaction of the mature myelin sheath in the central nervous system, thereby maintaining its structural integrity. In addition to membraneassociation, the 18.5 kDa and all other classic isoforms are able to interact with a multitude of proteins, including Ca2+- calmodulin, actin, tubulin, and SH3-domain containing proteins, and thus may be signalling linkers during myelin development and remodelling. All proteins in this family are intrinsically disordered, creating a large effective surface to facilitate multiple protein associations, and are post-translationally modified to various degrees by methylation, phosphorylation, and deimination. We have used spectroscopic (fluorescence, CD, EPR, and NMR) approaches to study MBP's conformational adaptability. A highly-conserved central domain presents an amphipathic α-helix in association with a phospholipid membrane, and contains a threonyl residue that is phosphorylated by MAP-kinases. In multiple sclerosis, this segment represents a primary immunodominant epitope. This helical structure is adjacent to a proline-rich region that presents a classic SH3-ligand, comprises a second MAP-kinase phosphorylation site, and forms a polyproline type II helix. This domain of the protein is thus essential to proper positioning of a protein-interaction motif, with the local conformation and accessibility being modulated by MAP-kinases. In addition, the C-terminus of 18.5 kDa MBP has been identified by NMR spectroscopy as a Ca2+-calmodulin-binding site, and is of note for having a high density of post-translational modifications (protein kinase C phosphorylation, and deimination). For the most part, any classic protein isoform functions as an entropic spring that interacts in its entirety with membranes and cytoskeletal proteins, but the central and Cterminal motifs may represent molecular switches.

Computational protein tertiary structure prediction has made significant progress over the last decade due to the advancement of techniques and the growth of sequence and structure databases. However, it is still not very easy to predict the accuracy of a given predicted structure. Predicting the accuracy, or quality assessment of a prediction model, is crucial for a practical use of the model such as biochemical experimental design and drug design. Recently several model quality assessment programs (MQAPs) have been proposed for assessing global and local accuracy of predicted structures. We will start with reviewing the current status of protein structure prediction methods with an emphasis on the source of errors. Then existing MQAPs are classified into several categories and each is discussed. The categories include methods which evaluate the quality of template-target alignments, those which evaluate stereochemical irregularities of prediction models, and methods which integrate several features into a composite quality assessment score.

The Helmholtz free energy, F and the entropy, S are related thermodynamic quantities with a special importance in structural biology. We describe the difficulties in calculating these quantities and review recent methodological developments. Because protein flexibility is essential for function and ligand binding, we discuss the related problems involved in the definition, simulation, and free energy calculation of microstates (such as the α-helical region of a peptide). While the review is broad, a special emphasize is given to methods for calculating the absolute F (S), where our HSMC(D) method is described in some detail.

Large volumes of protein sequence and structure data acquired by proteomic studies led to the development of computational bioinformatic techniques that made possible the functional annotation and structural characterization of proteins based on their primary structure. It has become evident from genome-wide analyses that many proteins in eukaryotic cells are either completely disordered or contain long unstructured regions that are crucial for their biological functions. The content of disorder increases with evolution indicating a possibly important role of disorder in the regulation of cellular systems. Transcription factors are no exception and several proteins of this class have recently been characterized as premolten/molten globules. Yet, mammalian cells rely on these proteins to control expression of their 30,000 or so genes. Basic region:leucine zipper (bZIP) DNA-binding proteins constitute a major class of eukaryotic transcriptional regulators. This review discusses how conformational flexibility “built” into the amino acid sequence allows bZIP proteins to interact with a large number of diverse molecular partners and to accomplish their manifold cellular tasks in a strictly regulated and coordinated manner.

Template based protein structure prediction (commonly referred to as homology or comparative modeling) uses knowledge of solved structures to model a protein sequence's native or true fold. First, a parent structure is found and then a template structure is built by mapping the target sequence onto the parent structure. This putative structure is refined using a combination of backbone moves, side-chain packing, and loop modeling. Template based protein structure prediction has always held great promise to produce atomically accurate models close to the native conformation based on two major assumptions. First, similar sequences exhibit similar protein folds. Second, soluble proteins populate a discrete fold space with many representatives already solved in our Protein Data Bank (PDB). Ironically, beginning so close to the native structure is also the primary source of problems confronting this method and is the reason for the lack of progress in this category of structure prediction. In this review, the general concepts and procedures for template based structure prediction are outlined based on the following topics: sequence alignment, parent structure selection, template structure building, refinement, evaluation, and final structure selection. Then, a description of established software and algorithms is provided where the advantages and limitations of the different methods will be pointed out. This is followed by a discussion of the developments in template based structure prediction up to the 7th Critical Assessment of Structure Prediction meeting. Lastly, we will address the increased difficulty in improving templates that start so close to the native structure, and discuss the improvements needed in this field.

Major Histo-Compatibility (MHC) class I molecules are major agents of the mammalian adaptive immune system. Class I molecules bind short antigenic peptides with a length of 8-10 residues in the Endoplasmatic Reticulum (ER) and after transport to the cell surface the peptides are presented to T-lymphocytes. The binding site of class I molecules is formed by a deep cleft between two α-helices at top of an extended β-sheet. Only tightly bound high-affinity peptides have a chance to reach the cell surface and trigger an immune response. It is therefore of great interest to identify possible high-affinity antigenic peptides that could be used as vaccines to help the immune system to detect viral infections or kill malignant cells. A large number of crystal structures of antigenic peptides in complex with class I alleles have been determined that allow to understand the structural details important for peptide binding. Biophysical and biochemical analysis of peptide-class I complexes has resulted in a number of rules concerning the selection of high-affinity peptides. However, an accurate prediction of allele specific peptide-binding is still not possible. This issue is currently addressed by various computational tools developed by the bioinformatics community. The computational efforts range from statistical analysis of peptide motifs stored in databases to application of neural network methods and support vector machine approaches. In addition, structure based approaches to predict class I binding specificity including molecular modeling and molecular dynamics (MD) simulations will also be presented.