Adding backbone to protein folding: why proteins are polypeptides

It is argued that the chemical nature of the polypeptide backbone is the central determinant of the three-dimensional structures of proteins. The requirement that buried polar groups form intramolecular hydrogen bonds limits the fold of the backbone to the well known units of secondary structure while the amino acid sequence chooses among the set of conformations available to the backbone. ‘Sidechain-only’ models, based for example on hydrophobicity patterns, fail to account for the properties of the backbone and thus will have difficulty capturing essential features of a folding pathway. This is evident from the incorrect predictions they make for the conformations of the limiting cases of all-hydrophobic or all-polar sequences

Poisson-Boltzmann Calculations of Nonspecific Salt Effects on Protein-Protein Binding Free Energies

The salt dependence of the binding free energy of five protein-protein hetero-dimers and two homo-dimers/tetramers was calculated from numerical solutions to the Poisson-Boltzmann equation. Overall, the agreement with experimental values is very good. In all cases except one involving the highly charged lactoglobulin homo-dimer, increasing the salt concentration is found both experimentally and theoretically to decrease the binding affinity. To clarify the source of salt effects, the salt-dependent free energy of binding is partitioned into screening terms and to self-energy terms that involve the interaction of the charge distribution of a monomer with its own ion atmosphere. In six of the seven complexes studied, screening makes the largest contribution but self-energy effects can also be significant. The calculated salt effects are found to be insensitive to force-field parameters and to the internal dielectric constant assigned to the monomers. Nonlinearities due to high charge densities, which are extremely important in the binding of proteins to negatively charged membrane surfaces and to nucleic acids, make much smaller contributions to the protein-protein complexes studied here, with the exception of highly charged lactoglobulin dimers. Our results indicate that the Poisson-Boltzmann equation captures much of the physical basis of the nonspecific salt dependence of protein-protein complexation

Electrostatic interaction of myristoylated proteins with membranes: simple physics, complicated biology

AbstractCell membrane association by several important peripheral proteins, such as Src, MARCKS, HIV-1 Gag, and K-Ras, requires nonspecific electrostatic interactions between a cluster of basic residues on the protein and acidic phospholipids in the plasma membrane. A simple theoretical model based on the nonlinear Poisson–Boltzmann equation describes well the experimentally measured electrostatic association between such proteins and the cell membrane

Loop modeling: Sampling, filtering, and scoring

We describe a fast and accurate protocol, LoopBuilder, for the prediction of loop conformations in proteins. The procedure includes extensive sampling of backbone conformations, side chain addition, the use of a statistical potential to select a subset of these conformations, and, finally, an energy minimization and ranking with an all-atom force field. We find that the Direct Tweak algorithm used in the previously developed LOOPY program is successful in generating an ensemble of conformations that on average are closer to the native conformation than those generated by other methods. An important feature of Direct Tweak is that it checks for interactions between the loop and the rest of the protein during the loop closure process. DFIRE is found to be a particularly effective statistical potential that can bias conformation space toward conformations that are close to the native structure. Its application as a filter prior to a full molecular mechanics energy minimization both improves prediction accuracy and offers a significant savings in computer time. Final scoring is based on the OPLS/SBG-NP force field implemented in the PLOP program. The approach is also shown to be quite successful in predicting loop conformations for cases where the native side chain conformations are assumed to be unknown, suggesting that it will prove effective in real homology modeling applications. Proteins 2008. © 2007 Wiley-Liss, Inc

PrePPI: a structure-informed database of protein–protein interactions

PrePPI (http://bhapp.c2b2.columbia.edu/PrePPI) is a database that combines predicted and experimentally determined protein–protein interactions (PPIs) using a Bayesian framework. Predicted interactions are assigned probabilities of being correct, which are derived from calculated likelihood ratios (LRs) by combining structural, functional, evolutionary and expression information, with the most important contribution coming from structure. Experimentally determined interactions are compiled from a set of public databases that manually collect PPIs from the literature and are also assigned LRs. A final probability is then assigned to every interaction by combining the LRs for both predicted and experimentally determined interactions. The current version of PrePPI contains ∼2 million PPIs that have a probability more than ∼0.1 of which ∼60 000 PPIs for yeast and ∼370 000 PPIs for human are considered high confidence (probability greater than 0.5). The PrePPI database constitutes an integrated resource that enables users to examine aggregate information on PPIs, including both known and potentially novel interactions, and that provides structural models for many of the PPIs

A computational interactome and functional annotation for the human proteome

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function

Kupe Virus, a New Virus in the Family Bunyaviridae, Genus Nairovirus, Kenya

One-sentence summary for table of contents: A new nairovirus isolated from ticks collected from cattle hides was characterized

Gene-Wise Association of Variants in Four Lysosomal Storage Disorder Genes in Neuropathologically Confirmed Lewy Body Disease

Objective Variants in GBA are associated with Lewy Body (LB) pathology. We investigated whether variants in other lysosomal storage disorder (LSD) genes also contribute to disease pathogenesis. Methods We performed a genetic analysis of four LSD genes including GBA, HEXA, SMPD1, and MCOLN1 in 231 brain autopsies. Brain autopsies included neuropathologically defined LBD without Alzheimer Disease (AD) changes (n = 59), AD without significant LB pathology (n = 71), Alzheimer disease and lewy body variant (ADLBV) (n = 68), and control brains without LB or AD neuropathology (n = 33). Sequencing of HEXA, SMPD1, MCOLN1 and GBA followed by ‘gene wise’ genetic association analysis was performed. To determine the functional effect, a biochemical analysis of GBA in a subset of brains was also performed. GCase activity was measured in a subset of brain samples (n = 64) that included LBD brains, with or without GBA mutations, and control brains. A lipidomic analysis was also performed in brain autopsies (n = 67) which included LBD (n = 34), ADLBV (n = 3), AD (n = 4), PD (n = 9) and control brains (n = 17), comparing GBA mutation carriers to non-carriers. Results In a ‘gene-wise’ analysis, variants in GBA, SMPD1 and MCOLN1 were significantly associated with LB pathology (p range: 0.03–4.14 x10-5). Overall, the mean levels of GCase activity were significantly lower in GBA mutation carriers compared to non-carriers (p<0.001). A significant increase and accumulation of several species for the lipid classes, ceramides and sphingolipids, was observed in LBD brains carrying GBA mutations compared to controls (p range: p<0.05-p<0.01). Interpretation Our study indicates that variants in GBA, SMPD1 and MCOLN1 are associated with LB pathology. Biochemical data comparing GBA mutation carrier to non-carriers support these findings, which have important implications for biomarker development and therapeutic strategies