Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc.

The sequence, structural, and functional diversity within a protein family and implications for specificity and safety: The case for ETX_MTX2 insecticidal proteins.

The need for sustainable insect pest control is driving the investigation and discovery of insecticidal proteins outside of the typical 3-domain Cry protein family from Bacillus thuringiensis (Bt). Examples include Cry35 and Cry51 that belong to protein families (Toxin_10, ETX_MTX2) sharing a common ß-pore forming structure and function with known mammalian toxins such as epsilon toxin (ETX). Although ß-pore forming proteins are related to mammalian toxins, there are key differences in sequence and structure that lead to organism specificity that is useful in the weight-of-evidence approach for safety assessment. Despite low overall amino acid sequence identity among ETX_MTX2 proteins, sequence and structural similarities are found in the tail region responsible for the shared oligomerization and pore formation functions (causing the "relatedness"). Conversely, most of the sequence and structural diversity is located in the head region that is likely responsible for differential receptor binding and target species specificity (e.g., insecticidal vs. mammalian). Therefore, inclusion of a domain-based protein characterization approach that includes bioinformatic and functional comparisons of conserved and diverse domains will enhance the overall weight of evidence safety assessment of proteins including recently reported Cry51 protein variants (Cry51Aa1, Cry51Aa2, and Cry51Aa2.834_16).

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org .

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles.

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints.

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.

CASP10-BCL::Fold efficiently samples topologies of large proteins.

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some ß-strand proteins model refinement failed as ß-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein.

BCL::Fold--protein topology determination from limited NMR restraints.

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge-based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton-proton distances to Cß -Cß distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets.

BCL::MP-fold: folding membrane proteins through assembly of transmembrane helices.

Membrane protein structure determination remains a challenging endeavor. Computational methods that predict membrane protein structure from sequence can potentially aid structure determination for such difficult target proteins. The de novo protein structure prediction method BCL::Fold rapidly assembles secondary structure elements into three-dimensional models. Here, we describe modifications to the algorithm, named BCL::MP-Fold, in order to simulate membrane protein folding. Models are built into a static membrane object and are evaluated using a knowledge-based energy potential, which has been modified to account for the membrane environment. Additionally, a symmetry folding mode allows for the prediction of obligate homomultimers, a common property among membrane proteins. In a benchmark test of 40 proteins of known structure, the method sampled the correct topology in 34 cases. This demonstrates that the algorithm can accurately predict protein topology without the need for large multiple sequence alignments, homologous template structures, or experimental restraints.

BCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elements.

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and ß-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, ß-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.

BCL::Score--knowledge based energy potentials for ranking protein models represented by idealized secondary structure elements.

The topology of most experimentally determined protein domains is defined by the relative arrangement of secondary structure elements, i.e. α-helices and ß-strands, which make up 50-70% of the sequence. Pairing of ß-strands defines the topology of ß-sheets. The packing of side chains between α-helices and ß-sheets defines the majority of the protein core. Often, limited experimental datasets restrain the position of secondary structure elements while lacking detail with respect to loop or side chain conformation. At the same time the regular structure and reduced flexibility of secondary structure elements make these interactions more predictable when compared to flexible loops and side chains. To determine the topology of the protein in such settings, we introduce a tailored knowledge-based energy function that evaluates arrangement of secondary structure elements only. Based on the amino acid C(ß) atom coordinates within secondary structure elements, potentials for amino acid pair distance, amino acid environment, secondary structure element packing, ß-strand pairing, loop length, radius of gyration, contact order and secondary structure prediction agreement are defined. Separate penalty functions exclude conformations with clashes between amino acids or secondary structure elements and loops that cannot be closed. Each individual term discriminates for native-like protein structures. The composite potential significantly enriches for native-like models in three different databases of 10,000-12,000 protein models in 80-94% of the cases. The corresponding application, "BCL::ScoreProtein," is available at www.meilerlab.org.

Membrane protein structure determination using paramagnetic tags.

The combination of paramagnetic tagging strategies with NMR or EPR spectroscopic techniques can revolutionize de novo structure determination of helical membrane proteins. Leveraging the full potential of this approach requires optimal labeling strategies and prediction of membrane protein topology from sparse and low-resolution distance restraints, as addressed by Chen et al. (2011).

Structure of a DNA polymerase alpha-primase domain that docks on the SV40 helicase and activates the viral primosome.

DNA polymerase alpha-primase (pol-prim) plays a central role in DNA replication in higher eukaryotes, initiating synthesis on both leading and lagging strand single-stranded DNA templates. Pol-prim consists of a primase heterodimer that synthesizes RNA primers, a DNA polymerase that extends them, and a fourth subunit, p68 (also termed B-subunit), that is thought to regulate the complex. Although significant knowledge about single-subunit primases of prokaryotes has accumulated, the functions and regulation of pol-prim remain poorly understood. In the SV40 replication model, the p68 subunit is required for primosome activity and binds directly to the hexameric viral helicase T antigen, suggesting a functional link between T antigen-p68 interaction and primosome activity. To explore this link, we first mapped the interacting regions of the two proteins and discovered a previously unrecognized N-terminal globular domain of p68 (p68N) that physically interacts with the T antigen helicase domain. NMR spectroscopy was used to determine the solution structure of p68N and map its interface with the T antigen helicase domain. Structure-guided mutagenesis of p68 residues in the interface diminished T antigen-p68 interaction, confirming the interaction site. SV40 primosome activity of corresponding pol-prim mutants decreased in proportion to the reduction in p68N-T antigen affinity, confirming that p68-T antigen interaction is vital for primosome function. A model is presented for how this interaction regulates SV40 primosome activity, and the implications of our findings are discussed in regard to the molecular mechanisms of eukaryotic DNA replication initiation.

NMR analysis of the architecture and functional remodeling of a modular multidomain protein, RPA.

Modular proteins with multiple domains tethered by flexible linkers have variable global architectures. Using the eukaryotic ssDNA binding protein, Replication Protein A (RPA), we demonstrate that NMR spectroscopy is a powerful tool to characterize the remodeling of architecture in different functional states. The first direct evidence is obtained for the remodeling of RPA upon binding ssDNA, including an alteration in the availability of the RPA32N domain that may help explain its damage-dependent phosphorylation.

An iron-sulfur cluster in the C-terminal domain of the p58 subunit of human DNA primase.

DNA primase synthesizes short RNA primers that are required to initiate DNA synthesis on the parental template strands during DNA replication. Eukaryotic primase contains two subunits, p48 and p58, and is normally tightly associated with DNA polymerase alpha. Despite the fundamental importance of primase in DNA replication, structural data on eukaryotic DNA primase are lacking. The p48/p58 dimer was subjected to limited proteolysis, which produced two stable structural domains: one containing the bulk of p48 and the other corresponding to the C-terminal fragment of p58. These domains were identified by mass spectrometry and N-terminal sequencing. The C-terminal p58 domain (p58C) was expressed, purified, and characterized. CD and NMR spectroscopy experiments demonstrated that p58C forms a well folded structure. The protein has a distinctive brownish color, and evidence from inductively coupled plasma mass spectrometry, UV-visible spectrophotometry, and EPR spectroscopy revealed characteristics consistent with the presence of a [4Fe-4S] high potential iron protein cluster. Four putative cysteine ligands were identified using a multiple sequence alignment, and substitution of just one was sufficient to cause loss of the iron-sulfur cluster and a reduction in primase enzymatic activity relative to the wild-type protein. The discovery of an iron-sulfur cluster in DNA primase that contributes to enzymatic activity provides the first suggestion that the DNA replication machinery may have redox-sensitive activities. Our results offer new horizons in which to investigate the function of high potential [4Fe-4S] clusters in DNA-processing machinery.