Elucidation of Short Linear Motif-Based Interactions of the FERM Domains of Ezrin, Radixin, Moesin, and Merlin

The ERM (ezrin, radixin, and moesin) family of proteins and the related protein merlin participate in scaffolding and signaling events at the cell cortex. The proteins share an N-terminal FERM [band four-point-one (4.1) ERM] domain composed of three subdomains (F1, F2, and F3) with binding sites for short linear peptide motifs. By screening the FERM domains of the ERMs and merlin against a phage library that displays peptides representing the intrinsically disordered regions of the human proteome, we identified a large number of novel ligands. We determined the affinities for the ERM and merlin FERM domains interacting with 18 peptides and validated interactions with full-length proteins through pull-down experiments. The majority of the peptides contained an apparent Yx[FILV] motif; others show alternative motifs. We defined distinct binding sites for two types of similar but distinct binding motifs (YxV and FYDF) using a combination of Rosetta FlexPepDock computational peptide docking protocols and mutational analysis. We provide a detailed molecular understanding of how the two types of peptides with distinct motifs bind to different sites on the moesin FERM phosphotyrosine binding-like subdomain and uncover interdependencies between the different types of ligands. The study expands the motif-based interactomes of the ERMs and merlin and suggests that the FERM domain acts as a switchable interaction hub

Encounter complexes and dimensionality reduction in protein-protein association

An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition

Evolution of a fluorinated green fluorescent protein

The fluorescence of bacterial cells expressing a variant (GFPm) of the green fluorescent protein (GFP) was reduced to background levels by global replacement of the leucine residues of GFPm by 5,5,5-trifluoroleucine. Eleven rounds of random mutagenesis and screening via fluorescence-activated cell sorting yielded a GFP mutant containing 20 amino acid substitutions. The mutant protein in fluorinated form showed improved folding efficiency both in vivo and in vitro, and the median fluorescence of cells expressing the fluorinated protein was improved {approx}650-fold in comparison to that of cells expressing fluorinated GFPm. The success of this approach demonstrates the feasibility of engineering functional proteins containing many copies of abiological amino acid constituents

Molecular pathogenesis of human CD59 deficiency

Objective To characterize all 4 mutations described for CD59 congenital deficiency. Methods The 4 mutations, p.Cys64Tyr, p.Asp24Val, p.Asp24Valfs*, and p.Ala16Alafs*, were described in 13 individuals with CD59 malfunction. All 13 presented with recurrent Guillain-Barré syndrome or chronic inflammatory demyelinating polyneuropathy, recurrent strokes, and chronic hemolysis. Here, we track the molecular consequences of the 4 mutations and their effects on CD59 expression, localization, glycosylation, degradation, secretion, and function. Mutants were cloned and inserted into plasmids to analyze their expression, localization, and functionality. Results Immunolabeling of myc-tagged wild-type (WT) and mutant CD59 proteins revealed cell surface expression of p.Cys64Tyr and p.Asp24Val detected with the myc antibody, but no labeling by anti-CD59 antibodies. In contrast, frameshift mutants p.Asp24Valfs* and p.Ala16Alafs* were detected only intracellularly and did not reach the cell surface. Western blot analysis showed normal glycosylation but mutant-specific secretion patterns. All mutants significantly increased MAC-dependent cell lysis compared with WT. In contrast to CD59 knockout mice previously used to characterize phenotypic effects of CD59 perturbation, all 4 hCD59 mutations generate CD59 proteins that are expressed and may function intracellularly (4) or on the cell membrane (2). None of the 4 CD59 mutants are detected by known anti-CD59 antibodies, including the 2 variants present on the cell membrane. None of the 4 inhibits membrane attack complex (MAC) formation. Conclusions All 4 mutants generate nonfunctional CD59, 2 are expressed as cell surface proteins that may function in non–MAC-related interactions and 2 are expressed only intracellularly. Distinct secretion of soluble CD59 may have also a role in disease pathogenesis

BIOINFORMATICS Learning MHC I-peptide binding

ABSTRACT Motivation and results: Motivated by the ability of a simple threading approach to predict MHC I-peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were used in the original threading approach, we included three other sources of information on peptide-MHC binding: (1) MHC class I sequences; (2) known binding energies for a large number of MHC-peptide complexes; and (3) an even larger binary dataset that contains information about strong binders (epitopes) and non-binders (peptides that have a low affinity for a particular MHC molecule). Our model significantly outperforms the standard threading approach in binding energy prediction. In our approach, which we call adaptive double threading, the parameters of the threading model are learnable, and both MHC and peptide sequences can be threaded onto structures of other alleles. These two properties make our model appropriate for predicting binding for alleles for which very little data (if any) is available beyond just their sequence, including prediction for alleles for which 3D structures are not available. The ability of our model to generalize beyond the MHC types for which training data is available also separates our approach from epitope prediction methods which treat MHC alleles as symbolic types, rather than biological sequences. We used the trained binding energy predictor to study viral infections in 246 HIV patients from the West Australian cohort, and over 1000 sequences in HIV clade B from Los Alamos National Laboratory database, capturing the course of HIV evolution over the last 20 years. Finally, we illustrate short-, medium-, and long-term adaptation of HIV to the human immune system

Ground state and glass transition of the RNA secondary structure

RNA molecules form a sequence-specific self-pairing pattern at low temperatures. We analyze this problem using a random pairing energy model as well as a random sequence model that includes a base stacking energy in favor of helix propagation. The free energy cost for separating a chain into two equal halves offers a quantitative measure of sequence specific pairing. In the low temperature glass phase, this quantity grows quadratically with the logarithm of the chain length, but it switches to a linear behavior of entropic origin in the high temperature molten phase. Transition between the two phases is continuous, with characteristics that resemble those of a disordered elastic manifold in two dimensions. For designed sequences, however, a power-law distribution of pairing energies on a coarse-grained level may be more appropriate. Extreme value statistics arguments then predict a power-law growth of the free energy cost to break a chain, in agreement with numerical simulations. Interestingly, the distribution of pairing distances in the ground state secondary structure follows a remarkable power-law with an exponent -4/3, independent of the specific assumptions for the base pairing energies

Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles

The stability, activity, and solubility of a protein sequence are determined by a delicate balance of molecular interactions in a variety of conformational states. Even so, most computational protein design methods model sequences in the context of a single native conformation. Simulations that model the native state as an ensemble have been mostly neglected due to the lack of sufficiently powerful optimization algorithms for multistate design. Here, we have applied our multistate design algorithm to study the potential utility of various forms of input structural data for design. To facilitate a more thorough analysis, we developed new methods for the design and high-throughput stability determination of combinatorial mutation libraries based on protein design calculations. The application of these methods to the core design of a small model system produced many variants with improved thermodynamic stability and showed that multistate design methods can be readily applied to large structural ensembles. We found that exhaustive screening of our designed libraries helped to clarify several sources of simulation error that would have otherwise been difficult to ascertain. Interestingly, the lack of correlation between our simulated and experimentally measured stability values shows clearly that a design procedure need not reproduce experimental data exactly to achieve success. This surprising result suggests potentially fruitful directions for the improvement of computational protein design technology

Using inferred residue contacts to distinguish between correct and incorrect protein models

Motivation: The de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure

The RosettaDock server for local protein–protein docking

The RosettaDock server (http://rosettadock.graylab.jhu.edu) identifies low-energy conformations of a protein–protein interaction near a given starting configuration by optimizing rigid-body orientation and side-chain conformations. The server requires two protein structures as inputs and a starting location for the search. RosettaDock generates 1000 independent structures, and the server returns pictures, coordinate files and detailed scoring information for the 10 top-scoring models. A plot of the total energy of each of the 1000 models created shows the presence or absence of an energetic binding funnel. RosettaDock has been validated on the docking benchmark set and through the Critical Assessment of PRedicted Interactions blind prediction challenge