Current Protein and Peptide Science - Volume 9, Issue 1, 2008

This article gives an overview of recent progress in protein-protein docking and it identifies several directions for future research. Recent results from the CAPRI blind docking experiments show that docking algorithms are steadily improving in both reliability and accuracy. Current docking algorithms employ a range of efficient search and scoring strategies, including e.g. fast Fourier transform correlations, geometric hashing, and Monte Carlo techniques. These approaches can often produce a relatively small list of up to a few thousand orientations, amongst which a near-native binding mode is often observed. However, despite the use of improved scoring functions which typically include models of desolvation, hydrophobicity, and electrostatics, current algorithms still have difficulty in identifying the correct solution from the list of false positives, or decoys. Nonetheless, significant progress is being made through better use of bioinformatics, biochemical, and biophysical information such as e.g. sequence conservation analysis, protein interaction databases, alanine scanning, and NMR residual dipolar coupling restraints to help identify key binding residues. Promising new approaches to incorporate models of protein flexibility during docking are being developed, including the use of molecular dynamics snapshots, rotameric and off-rotamer searches, internal coordinate mechanics, and principal component analysis based techniques. Some investigators now use explicit solvent models in their docking protocols. Many of these approaches can be computationally intensive, although new silicon chip technologies such as programmable graphics processor units are beginning to offer competitive alternatives to conventional high performance computer systems. As cryo-EM techniques improve apace, docking NMR and X-ray protein structures into low resolution EM density maps is helping to bridge the resolution gap between these complementary techniques. The use of symmetry and fragment assembly constraints are also helping to make possible docking-based predictions of large multimeric protein complexes. In the near future, the closer integration of docking algorithms with protein interface prediction software, structural databases, and sequence analysis techniques should help produce better predictions of protein interaction networks and more accurate structural models of the fundamental molecular interactions within the cell.

Host cell entry by influenza and other enveloped viruses is well characterized, however, the manner in which non-enveloped viruses deliver their genome across host cell membranes in the absence of membrane fusion remains unresolved. The discovery of short, membrane altering, amphipathic or hydrophobic sequences in several non-enveloped virus capsid proteins such as the γ (gamma) peptide of nodaviruses and tetraviruses, VP4 and the N-terminal region of VP1 of picornaviruses, μ1N of reoviruses, and protein VI of adenoviruses suggests that these small peptides facilitate breaching of the host membrane and the delivery of the viral genome into the host cell. In spite of conspicuous differences in entry among non-enveloped virions, the short stretches of membrane active regions are associated with similar, entry-related events including: I) proteolytic cleavage of a precursor capsid protein resulting in increased dynamic character and/or accessibility of these peptides; II) structural changes in the virus capsid triggered by receptor binding and/or low pH in entry compartments, resulting in peptide exposure; III) externalized peptides interact with host membranes and disrupt them, facilitating delivery of the viral genome inside the host cell. Here we discuss the membrane alteration activity in nonenveloped viruses with reference to the γ peptide of nodaviruses. Virtually all of the characteristics of γ are shared by analogous peptides in other non-enveloped viruses, making it a simple prototype for comparative purposes.

The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.

Lantibiotics are a diverse family of bacterially synthesized antimicrobial peptides produced by gram-positive bacteria. They usually have a broad spectrum of targets, often including closely related strains. The production of lantibiotics must thus be coupled with a mechanism by which the producing strain can protect itself from the lethal action of its own antimicrobial compound. This mechanism is referred to as immunity. Lantibiotic immunity is usually provided by one, or both, of two methods i.e. by a specific immunity peptide (designated LanI) and/or a specialised ABC transporter system (designated LanFE(G)). Significantly, although the specific immunity peptides function in a similar manner, there is very little homology between them. This is reflected in the specific nature of the immunity provided. Finally, of equal importance is the manner in which these immunity determinants are regulated such that their expression is timed to occur with, or immediately prior to, lantibiotic production to ensure successful self-protection.

In living cells, membrane proteins are essential to signal transduction, nutrient use, and energy exchange between the cell and environment. Due to challenges in protein expression, purification and crystallization, deposition of membrane protein structures in the Protein Data Bank lags far behind existing structures for soluble proteins. This review describes recent advances in solution NMR allowing the study of a select set of peripheral and integral membrane proteins. Surface-binding proteins discussed include amphitropic proteins, antimicrobial and anticancer peptides, the HIV-1 gp41 peptides, human α-synuclein and apolipoproteins. Also discussed are transmembrane proteins including bacterial outer membrane β-barrel proteins and oligomeric α-helical proteins. These structural studies are possible due to solubilization of the proteins in membrane-mimetic constructs such as detergent micelles and bicelles. In addition to protein dynamics, protein-lipid interactions such as those between arginines and phosphatidylglycerols have been detected directly by NMR. These examples illustrate the unique role solution NMR spectroscopy plays in structural biology of membrane proteins.

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.

A group of serine peptidases, the prolyl oligopeptidase family, cannot hydrolyze proteins and peptides containing more than 30 residues. The crystal structure of prolyl oligopeptidase (POP) has shown that the enzyme is composed of a peptidase domain with an α/β hydrolase fold and a seven-bladed β-propeller domain. This domain covers the catalytic triad and excludes large, structured peptides from the active site. The mechanism of substrate selection has been reviewed, along with the binding mode of the substrate and the catalytic mechanism, which differ from that of the classical serine peptidases in several features. POP is essentially a cytosolic enzyme and has been shown to be involved in a number of biological processes, but its precise function is still unknown. Many reports addressed experimentally the possible role of POP in cognitive and psychiatric processes, its involvement in the inositol phosphate signaling pathway, and its ability to metabolize bioactive peptides. Inhibitors were designed to reveal the cellular functions of POP and to treat neurological disorders. Other studies concerned the cellular localization of POP, its presumed interaction with the cytoskeletal elements, and its involvement in peptide/protein transport/secretion processes. The possible role of POP in Alzheimer disease is an intriguing issue, which is still debated. Recently, recombinant bacterial POPs have been investigated as potential therapeutics for celiac sprue, an autoimmune disease of small intestine caused by the intake of gluten proteins.