Statistical measures on residue-level protein structural properties

The atomic-level structural properties of proteins, such as bond lengths, bond angles, and torsion angles, have been well studied and understood based on either chemistry knowledge or statistical analysis. Similar properties on the residue-level, such as the distances between two residues and the angles formed by short sequences of residues, can be equally important for structural analysis and modeling, but these have not been examined and documented on a similar scale. While these properties are difficult to measure experimentally, they can be statistically estimated in meaningful ways based on their distributions in known proteins structures. Residue-level structural properties including various types of residue distances and angles are estimated statistically. A software package is built to provide direct access to the statistical data for the properties including some important correlations not previously investigated. The distributions of residue distances and angles may vary with varying sequences, but in most cases, are concentrated in some high probability ranges, corresponding to their frequent occurrences in either α-helices or β-sheets. Strong correlations among neighboring residue angles, similar to those between neighboring torsion angles at the atomic-level, are revealed based on their statistical measures. Residue-level statistical potentials can be defined using the statistical distributions and correlations of the residue distances and angles. Ramachandran-like plots for strongly correlated residue angles are plotted and analyzed. Their applications to structural evaluation and refinement are demonstrated. With the increase in both number and quality of known protein structures, many structural properties can be derived from sets of protein structures by statistical analysis and data mining, and these can even be used as a supplement to the experimental data for structure determinations. Indeed, the statistical measures on various types of residue distances and angles provide more systematic and quantitative assessments on these properties, which can otherwise be estimated only individually and qualitatively. Their distributions and correlations in known protein structures show their importance for providing insights into how proteins may fold naturally to various residue-level structures.     

Coarse Grained Normal Mode Analysis vs. Refined Gaussian Network Model for Protein Residue-Level Structural Fluctuations

We investigate several approaches to coarse grained normal mode analysis on protein residual-level structural fluctuations by choosing different ways of representing the residues and the forces among them. Single-atom representations using the backbone atoms C _α , C, N, and C _β are considered. Combinations of some of these atoms are also tested. The force constants between the representative atoms are extracted from the Hessian matrix of the energy function and served as the force constants between the corresponding residues. The residue mean-square-fluctuations and their correlations with the experimental B-factors are calculated for a large set of proteins. The results are compared with all-atom normal mode analysis and the residue-level Gaussian Network Model. The coarse-grained methods perform more efficiently than all-atom normal mode analysis, while their B-factor correlations are also higher. Their B-factor correlations are comparable with those estimated by the Gaussian Network Model and in many cases better. The extracted force constants are surveyed for different pairs of residues with different numbers of separation residues in sequence. The statistical averages are used to build a refined Gaussian Network Model, which is able to predict residue-level structural fluctuations significantly better than the conventional Gaussian Network Model in many test cases.     

Prediction of hydrodynamic and other solution properties of rigid proteins from atomic- and residue-level models

Here we extend the ability to predict hydrodynamic coefficients and other solution properties of rigid macromolecular structures from atomic-level structures, implemented in the computer program HYDROPRO, to models with lower, residue-level resolution. Whereas in the former case there is one bead per nonhydrogen atom, the latter contains one bead per amino acid (or nucleotide) residue, thus allowing calculations when atomic resolution is not available or coarse-grained models are preferred. We parameterized the effective hydrodynamic radius of the elements in the atomic- and residue-level models using a very large set of experimental data for translational and rotational coefficients (intrinsic viscosity and radius of gyration) for >50 proteins. We also extended the calculations to very large proteins and macromolecular complexes, such as the whole 70S ribosome. We show that with proper parameterization, the two levels of resolution yield similar and rather good agreement with experimental data. The new version of HYDROPRO, in addition to considering various computational and modeling schemes, is far more efficient computationally and can be handled with the use of a graphical interface.     

Cloning and characterization of a gene from Rhizobium melilotii 2011 coding for ribosomal protein S1.

A 7 kb chromosomal DNA fragment from R. melilotii was cloned, which complemented temperature-sensitivity of an E. coli amber mutant in rpsA, the gene for ribosomal protein S1 (ES1). From complementation and maxicell analysis a 58 kd protein was identified as the homolog of protein S1 (RS1). DNA sequence analysis of the R. melilotii rpsA gene identified a protein of 568 amino acids, which showed 47% identical amino acid homology to protein S1 from E. coli. The RS1 protein lacked the two Cys residues which had been reported to play an important role for the function of ES1. Two repeats containing Shine-Dalgarno sequences were identified upstream of the structural gene. Binding studies with RNA polymerase from E. coli and Pseudomonas putida located one RNA-polymerase binding site close to the RS1 gene and another one several hundred basepairs upstream. One possible promoter was also identified by DNA sequence comparison with the corresponding E. coli promoter.     

Probing protein mechanics: residue-level properties and their use in defining domains

It is becoming clear that, in addition to structural properties, the mechanical properties of proteins can play an important role in their biological activity. It nevertheless remains difficult to probe these properties experimentally. Whereas single-molecule experiments give access to overall mechanical behavior, notably the impact of end-to-end stretching, it is currently impossible to directly obtain data on more local properties. We propose a theoretical method for probing the mechanical properties of protein structures at the single-amino acid level. This approach can be applied to both all-atom and simplified protein representations. The probing leads to force constants for local deformations and to deformation vectors indicating the paths of least mechanical resistance. It also reveals the mechanical coupling that exists between residues. Results obtained for a variety of proteins show that the calculated force constants vary over a wide range. An analysis of the induced deformations provides information that is distinct from that obtained with measures of atomic fluctuations and is more easily linked to residue-level properties than normal mode analyses or dynamic trajectories. It is also shown that the mechanical information obtained by residue-level probing opens a new route for defining so-called dynamical domains within protein structures.     

Peptide design and structural characterization of a GPCR loop mimetic

G protein-coupled receptors (GPCRs) control fundamental aspects of human physiology and behaviors. Knowledge of their structures, especially for the loop regions, is limited and has principally been obtained from homology models, mutagenesis data, low resolution structural studies, and high resolution studies of peptide models of receptor segments. We developed an alternate methodology for structurally characterizing GPCR loops, using the human S1P(4) first extracellular loop (E1) as a model system. This methodology uses computational peptide designs based on transmembrane domain (TM) model structures in combination with CD and NMR spectroscopy. The characterized peptides contain segments that mimic the self-assembling extracellular ends of TM 2 and TM 3 separated by E1, including residues R3.28(121) and E3.29(122) that are required for sphingosine 1-phosphate (S1P) binding and receptor activation in the S1P(4) receptor. The S1P(4) loop mimetic peptide interacted specifically with an S1P headgroup analog, O-phosphoethanolamine (PEA), as evidenced by PEA-induced perturbation of disulfide cross-linked coiled-coil first extracellular loop mimetic (CCE1a) (1)H and (15)N backbone amide chemical shifts. CCE1a was capable of weakly binding PEA near biologically relevant residues R29 and E30, which correspond to R3.28 and E3.29 in the full-length S1P(4) receptor, confirming that it has adopted a biologically relevant conformation. We propose that the combination of coiled-coil TM replacement and conformational stabilization with an interhelical disulfide bond is a general design strategy that promotes native-like structure for loops derived from GPCRs.     

On statistical energy functions for biomolecular modeling and design

Statistical energy functions are general models about atomic or residue-level interactions in biomolecules, derived from existing experimental data. They provide quantitative foundations for structural modeling as well as for structure-based protein sequence design. Statistical energy functions can be derived computationally either based on statistical distributions or based on variational assumptions. We present overviews on the theoretical assumptions underlying the various types of approaches. Theoretical considerations underlying important pragmatic choices are discussed.     

The determinants of bond angle variability in protein/peptide backbones: A comprehensive statistical/quantum mechanics analysis

The elucidation of the mutual influence between peptide bond geometry and local conformation has important implications for protein structure refinement, validation, and prediction. To gain insights into the structural determinants and the energetic contributions associated with protein/peptide backbone plasticity, we here report an extensive analysis of the variability of the peptide bond angles by combining statistical analyses of protein structures and quantum mechanics calculations on small model peptide systems. Our analyses demonstrate that all the backbone bond angles strongly depend on the peptide conformation and unveil the existence of regular trends as function of ψ and/or φ. The excellent agreement of the quantum mechanics calculations with the statistical surveys of protein structures validates the computational scheme here employed and demonstrates that the valence geometry of protein/peptide backbone is primarily dictated by local interactions. Notably, for the first time we show that the position of the H^α hydrogen atom, which is an important parameter in NMR structural studies, is also dependent on the local conformation. Most of the trends observed may be satisfactorily explained by invoking steric repulsive interactions; in some specific cases the valence bond variability is also influenced by hydrogen‐bond like interactions. Moreover, we can provide a reliable estimate of the energies involved in the interplay between geometry and conformations. Proteins 2015; 83:1973–1986. © 2015 Wiley Periodicals, Inc.     

N–H bond cleavage of ammonia on graphene-like B36 borophene: DFT studies

The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers—decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron—were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.

     

Phi X174 E complements lambda S and R dysfunction for host cell lysis.

Hybrid lambda phages which have the E lysis gene of the bacteriophage phi X174 in cis to defective nonsense and deletion alleles of the normal lambda lysis genes S and R have been constructed and shown to be fully competent for plaque-forming ability, which demonstrates that the single-gene, lysozyme-independent lysis system of phi X174 and related phages can serve the lytic function for large complex phages. These hybrid phages are unable to form plaques on a slyD host. Moreover, plaque morphology indicates that in E-mediated lysis the soluble lambda R endolysin can participate in lysis, indicating that the protein E-mediated lesions are not completely sealed off from the periplasm.     

qNABpredict: Quick,accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/ . This new tool should be particularly useful to predict details of protein–NA interactions for large protein families and proteomes.     

Protein structural homology: a metric approach

The flexibility of the polypeptide fold of proteins is essentially due to the rotational freedom about the main chain bonds involving C alpha atoms. The polypeptide fold can therefore be represented by virtual bonds joining consecutive C alpha atoms. The ordered sequence of virtual torsion and bond angles involving these bonds can be used to specify the fold. Such representations can then be compared to reveal structural similarities using the Needleman & Wünsch algorithm, which has been developed for comparison of amino acid sequences. Such an approach is presented and illustrated with examples. The method is suitable for detecting structural similarities that extend over 7 or more residues.     

E. coli 4.5S RNA is part of a ribonucleoprotein particle that has properties related to signal recognition particle

E. coli 4.5S RNA and P48 have been shown to be homologous to SRP7S RNA and SRP54, respectively. Here we report that expression of human SRP7S in E. coli can suppress the lethality caused by depletion of 4.5S RNA. In E. coli, both RNAs are associated with P48. In vitro, both E. coli P48 and SRP54 specifically bind to 4.5S RNA. Strains depleted of 4.5S RNA strongly accumulate pre-beta-lactamase and fail to accumulate maltose binding protein. These effects commence well before any growth defect is observed and are suppressed by expression of human SRP7S. Strains overproducing P48 also accumulate pre-beta-lactamase. 4.5S RNA and P48 are components of a ribonucleoprotein particle that we propose to be required for the secretion of some proteins.     

Adopting selected hydrogen bonding and ionic interactions from Aspergillus fumigatus phytase structure improves the thermostability of Aspergillus niger PhyA phytase

Although it has been widely used as a feed supplement to reduce manure phosphorus pollution of swine and poultry, Aspergillus niger PhyA phytase is unable to withstand heat inactivation during feed pelleting. Crystal structure comparisons with its close homolog, the thermostable Aspergillus fumigatus phytase (Afp), suggest associations of thermostability with several key residues (E35, S42, R168, and R248) that form a hydrogen bond network in the E35-to-S42 region and ionic interactions between R168 and D161 and between R248 and D244. In this study, loss-of-function mutations (E35A, R168A, and R248A) were introduced singularly or in combination into seven mutants of Afp. All seven mutants displayed decreases in thermostability, with the highest loss (25% [P<0.05]) in the triple mutant (E35A R168A R248A). Subsequently, a set of corresponding substitutions were introduced into nine mutants of PhyA to strengthen the hydrogen bonding and ionic interactions. While four mutants showed improved thermostability, the best response came from the quadruple mutant (A58E P65S Q191R T271R), which retained 20% greater (P<0.05) activity after being heated at 80 degrees C for 10 min and had a 7 degrees C higher melting temperature than that of wild-type PhyA. This study demonstrates the functional importance of the hydrogen bond network and ionic interaction in supporting the high thermostability of Afp and the feasibility of adopting these structural units to improve the thermostability of a homologous PhyA phytase.     

Redox-dependent structural reorganization in putidaredoxin, a vertebrate-type [2Fe-2S] ferredoxin from Pseudomonas putida

Putidaredoxin (Pdx), a vertebrate-type [2Fe-2S] ferredoxin from Pseudomonas putida, transfers electrons from NADH-putidaredoxin reductase to cytochrome P450cam. Pdx exhibits redox-dependent binding affinities for P450cam and is thought to play an effector role in the monooxygenase reaction catalyzed by this hemoprotein. To understand how the reduced form of Pdx is stabilized and how reduction of the [2Fe-2S] cluster affects molecular properties of the iron-sulfur protein, crystal structures of reduced C73S and C73S/C85S Pdx were solved to 1.45 angstroms and 1.84 angstroms resolution, respectively, and compared to the corresponding 2.0 angstroms and 2.03 angstroms X-ray models of the oxidized mutants. To prevent photoreduction, the latter models were determined using in-house radiation source and the X-ray dose received by Pdx crystals was significantly decreased. Structural analysis showed that in reduced Pdx the Cys45-Ala46 peptide bond flip initiates readjustment of hydrogen bonding interactions between the [2Fe-2S] cluster, the Sgamma atoms of the cysteinyl ligands, and the backbone amide nitrogen atoms that results in tightening of the Cys39-Cys48 metal cluster binding loop around the prosthetic group and shifting of the metal center toward the Cys45-Thr47 peptide. From the metal center binding loop, the redox changes are transmitted to the linked Ile32-Asp38 peptide triggering structural rearrangement between the Tyr33-Asp34, Ser7-Asp9 and Pro102-Asp103 fragments of Pdx. The newly established hydrogen bonding interactions between Ser7, Asp9, Tyr33, Asp34, and Pro102, in turn, not only stabilize the tightened conformation of the [2Fe-2S] cluster binding loop but also assist in formation of a specific structural patch on the surface of Pdx that can be recognized by P450cam. This redox-linked change in surface properties is likely to be responsible for different binding affinity of oxidized and reduced Pdx to the hemoprotein.     

Real-SPINE: an integrated system of neural networks for real-value prediction of protein structural properties

Proteins can move freely in three-dimensional space. As a result, their structural properties, such as solvent accessible surface area, backbone dihedral angles, and atomic distances, are continuous variables. However, these properties are often arbitrarily divided into a few classes to facilitate prediction by statistical learning techniques. In this work, we establish an integrated system of neural networks (called Real-SPINE) for real-value prediction and apply the method to predict residue-solvent accessibility and backbone psi dihedral angles of proteins based on information derived from sequences only. Real-SPINE is trained with a large data set of 2640 protein chains, sequence profiles generated from multiple sequence alignment, representative amino-acid properties, a slow learning rate, overfitting protection, and predicted secondary structures. The method optimizes more than 200,000 weights and yields a 10-fold cross-validated Pearson's correlation coefficient (PCC) of 0.74 between predicted and actual solvent accessible surface areas and 0.62 between predicted and actual psi angles. In particular, 90% of 2640 proteins have a PCC value greater than 0.6 between predicted and actual solvent-accessible surface areas. The results of Real-SPINE can be compared with the best reported correlation coefficients of 0.64-0.67 for solvent-accessible surface areas and 0.47 for psi angles. The real-SPINE server, executable programs, and datasets are freely available on http://sparks.informatics.iupui.edu.     

Amino acid substitution within the S2 and S4 transmembrane segments in Shaker potassium channel modulates channel gating

To investigate of the gating properties in the voltage-activated potassium channel, we have mutated a variety of S2 and S4 residues in the Shaker potassium protein. Results showed that the R365C and R368C, but not the E283C, R362C, R365S, R368S or the ShB-IR, were sensitive to micromolar concentrations of Cd(2+) ions. This indicates that R365 and R368 play a crucial role in the channel gating due to a conformational modulation of the channel structure. Doubly mutated channels of the E283C/R365E and E283C/R368E caused a transient increase in current amplitude, which reached a peak within a few seconds and then decreased toward initial levels, despite the continual presence of Cd(2+). Taken together, our results suggest that E283, R365, and R368 form a network of strong, local, and electrostatic interactions that relate closely to the mechanism of the channel gating.