Check out our New Publishers' Select for Free Articles

BBA - Proteins and Proteomics (v.1824, #12)

Editorial Board (pp. i).

Kinetic intermediates of amyloid fibrillation studied by hydrogen exchange methods with nuclear magnetic resonance by Young-Ho Lee; Yuji Goto (pp. 1307-1323).

Amyloid fibrils with an ordered cross-β structure are one form of protein aberrant aggregates. Fibrils themselves and on-pathway small aggregates are involved in many neurodegenerative diseases and amylodoses. Over the past decade, much has been learned about the conformation of amyloid fibrils by using various biochemical and biophysical approaches. Amyloid fibrils accommodate rigid core structures composed of regular intra- and intermolecular non-covalent bonds such as hydrogen bonds, and disordered flexible regions exposed to solvents. In contrast to the improved understanding of fibril structures, few studies have investigated the short-living monomeric intermediates which interact with amyloid fibrils for elongation and the self-associated intermediates in the course of amyloidogenesis at the residue level. To study static fibrillar structures and kinetic intermediates, hydrogen/deuterium exchange (HD_ex) coupled with solution-state NMR spectroscopy is one of the most powerful methods with a high time and atomic resolution. Here, we review studies on the structural properties of amyloid fibrils based on a combination of dimethylsulfoxide-quenched HD_ex and NMR spectroscopy. Recent studies on transient kinetic intermediates during fibril growth by means of pulse-labeling HD_ex aided by a quenched-flow apparatus and NMR spectroscopy are focused on.► Short-lived monomeric intermediates during fibril growth are still largely unknown. ► Pulse-labeling hydrogen exchange is useful for characterizing amyloid fibrillation. ► Kinetic intermediates of β2-m fibrillation were characterized at a residue level. ► Transient intermediates of β2-m monomers limited the kinetics of fibril elongation. ► A kinetic intermediate for fibril extension showed a largely unfolded conformation.

Keywords: Abbreviations; AFM; atomic force microscopy; β2-m; β; ₂; -microglobulin; CD; circular dichroism; DMSO; dimethylsulfide; EM; electron microscopy; ESR; electron spin resonance; FTIR; Fourier transform infrared spectroscopy; HMQC; heteronuclear multiple quantum coherence; HSQC; heteronuclear single quantum coherence; HD; _ex; hydrogen/deuterium exchange; MS; mass spectrometry; NMR; nuclear magnetic resonance; pD; ^⁎; the pH meter electrode reading without correction for the isotope effect; QCM; quartz crystal microbalance; SPR; surface plasmon resonance; TIRFM; total internal reflection fluorescence microscopy; TOCSY; total correlation spectroscopyAmyloid fibril; Amyloidgenic intermediate; β; ₂; -microglobulin; Hydrogen/deuterium exchange; Nuclear magnetic resonance; Pulse-labeling

Proteomic analysis on salicylic acid-induced salt tolerance in common wheat seedlings ( Triticum aestivum L.) by Guozhang Kang; Gezi Li; Beibei Zheng; Qiaoxia Han; Chenyang Wang; Yunji Zhu; Tiancai Guo (pp. 1324-1333).

The influence of salicylic acid (SA) on the salt tolerance mechanism in seedlings of common wheat ( Triticum aestivum L.) was investigated using physiological measurements combined with global expression profiling (proteomics). In the present study, 0.5mM SA significantly reduced NaCl-induced growth inhibition in wheat seedlings, manifesting as increased fresh weights, dry weights, and photosynthetic pigments, but decreased lipid peroxidation. Two-week-old wheat seedlings treated with 0.5mM SA, 250mM NaCl and 250mM NaCl+0.5mM SA for 3days were used for the proteomic analyses. In total, 39 proteins differentially regulated by both salt and SA were revealed by 2D PAGE, and 38 proteins were identified by MALDI-TOF/TOF MS. The identified proteins were involved in various cellular responses and metabolic processes including signal transduction, stress defense, energy, metabolism, photosynthesis, and others of unknown function. All protein spots involved in signal transduction and the defense response were significantly upregulated by SA under salt stress, suggesting that these proteins could play a role in the SA-induced salt resistance in wheat seedlings.► We analyzed proteomic changes regulated by SA in salt-stressed wheat seedlings. ► Proteins differentially induced by SA under salt stress were identified. ► Differential proteins involved in signal and stress defense were up-regulated by SA. ► Comparison was made between salt and biotic studies on proteomes regulated by SA.

Keywords: Wheat (; Triticum aestivum; L.); Proteome; Salicylic acid; Salt tolerance

Filamin isoforms in molluscan smooth muscle by Mendez-Lopez Lucía Méndez-López; Ulf Hellman; Izaskun Ibarguren; Villamarin J. Antonio Villamarín (pp. 1334-1341).

The role of filamin in molluscan catch muscles is unknown. In this work three proteins isolated from the posterior adductor muscle of the sea mussel Mytilus galloprovincialis were identified by MALDI-TOF/TOF MS as homologous to mammalian filamin. They were named FLN-270, FLN-230 and FLN-105, according to their apparent molecular weight determined by SDS-PAGE: 270kDa, 230kDa and 105kDa, respectively. Both FLN-270 and FLN-230 contain the C-terminal dimerization domain and the N-terminal actin-binding domain typical of filamins. These findings, together with the data from peptide mass fingerprints, indicate that FLN-270 and FLN-230 are different isoforms of mussel filamin, with FLN-230 being the predominant isoform in the mussel catch muscle. De novo sequencing data revealed structural differences between both filamin isoforms at the rod 2 segment, the one responsible for the interaction of filamin with the most of its binding partners. FLN270 but not FLN230 was phosphorylated in vitro by cAMP-dependent protein kinase. As for the FLN-105, it would be an N-terminal proteolytic fragment generated from the FLN-270 isoform or a C-terminally truncated variant of filamin. On the other hand, a 45-kDa protein that copurifies with mussel catch muscle filamins was identified as the mussel calponin-like protein. The fact that this protein coelutes with the FLN-270 isoform from a gel filtration chromatography suggests a specific interaction between both proteins.Display Omitted► Three proteins from catch muscle were identified as homologous to mammalian filamin. ► FLN-270 and FLN-230 show structural differences at the rod 2 segment. ► FLN-270 but not FLN-230 was phosphorylated in vitro by PKA. ► FLN-230 was the predominant isoform in catch muscle. ► A protein identified as the mussel calponin-like protein copurified with FLN-270.

Keywords: Abbreviations; PAM; posterior adductor muscle; PKA; cAMP-dependent protein kinase; C; catalytic subunit of PKA; R; regulatory subunit of PKAFilamin isoforms; Catch muscle; Proteolysis; Mass spectrometry; Mollusc; Mytilus

Quantitative proteomic profiling of the promastigotes and the intracellular amastigotes of Leishmania donovani isolates identifies novel proteins having a role in Leishmania differentiation and intracellular survival by Neha Biyani; Rentala Madhubala (pp. 1342-1350).

Protozoan parasites of the genus Leishmania are important human pathogens that cycle between an extracellular promastigote stage residing in the sandflies and an intracellular amastigote stage colonizing the phagolysosomal compartment of the mammalian macrophages. Here, we used the isobaric tagging method to quantify the global proteomic differences between the promastigotes and the intracellular amastigotes of three different Leishmania donovani clones derived from the THP-1 human macrophage cell line. We identified a substantial number of differentially modulated proteins involved in nutrient acquisition and energy metabolism, cell motility and cytoskeleton, transport, cell signaling and stress response. Proteins involved in vesicular trafficking and endocytosis like the rab7 GTP binding protein, GTP-binding proteins of the Ras superfamily and developmentally regulated GTP-binding protein 1 revealed enhanced expression and also a putative dynein heavy chain protein was found to be up-regulated in the amastigotes and it probably has a role in cargo transport inside the vesicles. Significantly, in the amastigotes the expression of a protein involved in glucose transport was increased eight to fifteen-fold, whereas concentrations of several proteins associated with cell motility and cytoskeleton were reduced. Thus, the quantitative proteomic analysis of L. donovani isolates sheds light on some novel proteins that may have a role in Leishmania differentiation and intracellular survival.► Quantitative proteomics of promastigote and amastigote Leishmania isolates was done. ► Amastigotes had elevated expression of a protein involved in glucose transport. ► Down-regulation of proteins associated with cell motility and cytoskeleton. ► Modification of expression of proteins involved in key metabolic pathways. ► Modification of proteins involved in vesicular trafficking and endocytosis.

Keywords: Proteome; Intracellular amastigotes; THP-1 cells; Promastigotes; Drug resistance

Compatible solutes contribute to heat resistance and ribosome stability in Escherichia coli AW1.7 by Aaron Pleitner; Yong Zhai; Roland Winter; Lifang Ruan; Lynn M. McMullen; Ganzle Michael G. Gänzle (pp. 1351-1357).

This study investigated the mechanisms of heat resistance in Escherichia coli AW1.7 by quantification of cytoplasmic solutes, determination of ribosome denaturation, and by determination of protein denaturation. To assess the contribution of heat shock proteins and compatible solutes, experiments were conducted after exposure to sublethal heat shock, and with cultures grown at NaCl concentrations ranging from 0 to 6%. Heat resistance of E. coli AW1.7 was compared to the heat sensitive E. coli GGG10 and a plasmid-cured, heat sensitive derivative of E. coli AW1.7 named E. coli AW1.7ΔpHR1. Sublethal heat shock improved survival at 60°C of E. coli GGG10 and AW1.7ΔpHR1 but not of E. coli AW1.7. Addition of NaCl increased the heat resistance of all three strains, but only E. coli AW1.7 exhibited high heat resistance when grown in NaCl concentrations ranging from 2 to 6%. E. coli AW1.7 and GGG10 accumulated 16.1±0.8 and 8.8±0.8mmolL−¹ amino acids when grown at 0% NaCl, and 1.47±0.07 and 0.78±0.06mmolL−¹ carbohydrates when grown at 6% NaCl, respectively. Ribosome denaturation was determined by differential scanning calorimetry. After growth in the presence of 0% NaCl, the 30S subunit denatured at 63.7±0.8°C and 60.7±0.3°C in E. coli AW1.7 and GGG10, respectively. Fourier-transformed-infrared-spectroscopy did not indicate differences in protein denaturation between the strains during heating. In conclusion, heat resistance in E. coli AW1.7 correlates to ribosome stability at 60°C and is dependent on accumulation of cytoplasmic solutes.► NaCl increased heat resistance in E. coli. ► E. coli AW1.7 exhibited high heat resistance when grown in the presence of 2–6% NaCl. ► E. coli AW1.7 differs from heat sensitive strains by accumulation of osmolytes. ► Heat resistance in E. coli AW1.7 correlates to ribosome stability.

Keywords: Abbreviations; DSC; Differential scanning calorimetry; FTIR; Fourier-transformed-infrared Escherichia coli; Heat resistance; Compatible solute; Trehalose; Ribosome denaturation; Differential scanning calorimetry

Isolation, functional characterization and crystallization of Aq_1259, an outer membrane protein with porin features, from Aquifex aeolicus by Tao Wang; Julian D. Langer; Guohong Peng; Hartmut Michel (pp. 1358-1365).

The “hypothetical protein” Aq_1259 was identified by mass spectrometry and purified from native membranes of Aquifex aeolicus. It is a 49.4kDa protein, highly homologous (>52% identity) to several conserved hypothetical proteins from other bacteria. However, none of these proteins has been characterized using biochemical or electrophysiological techniques. Based on the sequence and circular dichroism spectroscopy, the structure of Aq_1259 is predicted to be a β-barrel with 16 β-strands. The strands with loops and turns are distributed evenly through the entire sequence. The function of Aq_1259 was analyzed after incorporation into a lipid bilayer. Electrophysiological measurements revealed a pore that has a basic stationary conductance of 0.48±0.038nS in a buffer with 0.5M NaH₂PO₄ at pH 6.5 and 0.2±0.015nS in a buffer with 0.5M NaCl at pH 6.5. Superimposed on this is a fluctuating conductance of similar amplitude. Aq_1259 could be crystallized. The crystals diffract to a resolution of 3.4Å and belong to space group I222 with cell dimensions of a=138.3Å, b=144.6Å, c=151.8Å.► “Hypothetical protein” Aq_1259 was identified and purify to homogenous. ► Bioinformatics analysis revealed that it was a β-barrel with 16 β-strands. ► The β-barrel structure was confirmed by circular dichroism spectroscopy. ► Its function was analyzed in a lipid bilayer which showed porin-like features.

Keywords: Aq_1259; Porin; Aquifex aeolicus; Hypothetical protein

Investigation on PLK2 and PLK3 substrate recognition by M. Salvi; E. Trashi; G. Cozza; C. Franchin; G. Arrigoni; L.A. Pinna (pp. 1366-1373).

Analyses of human phosphoproteome based on primary structure of the aminoacids surrounding the phosphor Ser/Thr suggest that a significant proportion of phosphosites is generated by a restricted number of acidophilic kinases, among which protein kinase CK2 plays a prominent role. Recently, new acidophilic kinases belonging to the Polo like kinase family have been characterized, with special reference to PLK1, PLK2, and PLK3 kinases. While some progress has been made in deciphering the PLK1-dependent phosphoproteome, very little is known about the targets of PLK2 and PLK3 kinases. In this report by using an in vitro approach, consisting of cell lysate phosphorylation, phosphoprotein separation by 2D gel electrophoresis and mass spectrometry, we describe the identification of new potential substrates of PLK2 and PLK3 kinases. We have identified and validated as in vitro PLK2 and PLK3 substrates HSP90, GRP-94, β-tubulin, calumenin, and 14-3-3 epsilon. The phosphosites generated by PLK3 in these proteins have been identified by mass spectrometry analysis to get new insights about PLKs specificity determinants. These latter have been further corroborated by an in silico analysis of the PLKs substrate binding region.► PLK2 and PLK3 recognize the same specific determinants. ► HSP90, GRP-94, β-tubulin, calumenin, and 14-3-3ε are PLK2 /3 in vitro substrates. ► PLK2/3 specific determinants are significantly different from those of PLK1 and CK2.

Keywords: PLK2; PLK3; CK2; PLK1; Kinase

Prediction of heparin binding sites in bone morphogenetic proteins (BMPs) by Neha S. Gandhi; Ricardo L. Mancera (pp. 1374-1381).

Heparin is a glycosaminoglycan known to bind bone morphogenetic proteins (BMPs) and the growth and differentiation factors (GDFs) and has strong and variable effects on BMP osteogenic activity. In this paper we report our predictions of the likely heparin binding sites for BMP-2 and 14. The N-terminal sequences upstream of TGF-β-type cysteine-knot domains in BMP-2, 7 and 14 contain the basic residues arginine and lysine, which are key components of the heparin/HS-binding sites, with these residues being highly non-conserved. Importantly, evolutionary conserved surfaces on the beta sheets are required for interactions with receptors and antagonists. Furthermore, BMP-2 has electropositive surfaces on two sides compared to BMP-7 and BMP-14. Molecular docking simulations suggest the presence of high and low affinity binding sites in dimeric BMP-2. Histidines were found to play a role in the interactions of BMP-2 with heparin; however, a p K_a analysis suggests that histidines are likely not protonated. This is indicative that interactions of BMP-2 with heparin do not require acidic pH. Taken together, non-conserved amino acid residues in the N-terminus and residues protruding from the beta sheet (not overlapping with the receptor binding sites and the dimeric interface) and not C-terminal are found to be important for heparin–BMP interactions.► BMPs induce osteoblast differentiation in mesenchymal cells. ► BMPs have been shown to be heparin-binding proteins. ► Molecular modelling techniques for the prediction of the heparin binding sites ► Heparin–BMP complex alternative source of drug delivery

Keywords: Heparin; Bone morphogenetic protein (BMP); Growth and differentiation factor (GDF); Transforming growth factor-β (TGF-β); Docking

Profound conformational changes of PED/PEA-15 in ERK2 complex revealed by NMR backbone dynamics by Edward C. Twomey; Dana F. Cordasco; Yufeng Wei (pp. 1382-1393).

PED/PEA-15 is a small, non-catalytic, DED containing protein that is widely expressed in different tissues and highly conserved among mammals. PED/PEA-15 has been found to interact with several protein targets in various pathways, including FADD and procaspase-8 (apoptosis), ERK1/2 (cell cycle entry), and PLD1/2 (diabetes). In this research, we have studied the PED/PEA-15 in a complex with ERK2, a MAP kinase, using NMR spectroscopic techniques. MAP Kinase signaling pathways are involved in the regulation of many cellular functions, including cell proliferation, differentiation, apoptosis and survival. ERK1/2 are activated by a variety of external stimuli, including growth factors, hormones and neurotransmitters. Inactivated ERK2 is primarily found in the cytosol. Once the ERK/MAPK cascade is initiated, ERK2 is phosphorylated and stimulated, allowing it to redistribute in the cell nucleus and act as a transcription factor. Previous studies have shown that PED/PEA-15 complexes with ERK2 in the cytoplasm and prevents redistribution into the nucleus. Although the NMR structure and dynamics of PED/PEA-15 in the free form have been documented recently, no detailed structural and dynamic information for the ERK2-bound form is available. Here we report NMR chemical shift perturbation and backbone dynamic studies at the fast ps–ns timescale of PED/PEA-15, in its free form and in the complex with ERK2. These analyses characterize motions and conformational changes involved in ERK2 recognition and binding that orchestrate the reorganization of the DED and immobilization of the C-terminal tail. A new induced fit binding model for PED/PEA-15 is proposed.► We obtained chemical shift perturbation profile for PED/PEA-15 in ERK2 complex. ► We studied¹⁵N NMR backbone dynamics for both free and ERK2-bound PED/PEA-15. ► PED/PEA-15 DED undergoes significant conformational change upon binding ERK2. ► Key residues on PED/PEA-15 in binding ERK2 are not located in the binding interface.

Keywords: Abbreviations; PED/PEA-15; phosphoprotein enriched in diabetes/phosphoprotein enriched in astrocytes, 15; kDa; ERK; extracellular regulated kinase; DED; death effector domain; CSP; chemical shift perturbation; TROSY; transverse relaxation optimized spectroscopyNMR dynamics; PED/PEA-15; ERK2; MAP kinase; Death effector domain

Biochemical properties and catalytic domain structure of the CcmH protein from Escherichia coli by Xue-Ming Zheng; Jing Hong; Hai-Yin Li; Dong-Hai Lin; Hong-Yu Hu (pp. 1394-1400).

In the Gram-negative bacterium of Escherichia coli, eight genes organized as a ccm operon ( ccmABCDEFGH) are involved in the maturation of c-type cytochromes. The proteins encoded by the last three genes ccmFGH are believed to form a lyase complex functioning in the reduction of apocytochrome c and haem attachment. Among them, CcmH is a membrane-associated protein; its N-terminus is a catalytic domain with the active CXXC motif and the C-terminus is predicted as a TPR-like domain with unknown function. By using SCAM (scanning cysteine accessibility mutagenesis) and Gaussia luciferase fusion assays, we provide experimental evidence for the entire topological structure of E. coli CcmH. The mature CcmH is a periplasm-resident oxidoreductase anchored to the inner membrane by two transmembrane segments. Both N- and C-terminal domains are located and function in the periplasmic compartment. Moreover, the N-terminal domain forms a monomer in solution, while the C-terminal domain is a compact fold with helical structures. The NMR solution structure of the catalytic domain in reduced form exhibits mainly a three-helix bundle, providing further information for the redox mechanism. The redox potential suggests that CcmH exhibits a strong reductase that may function in the last step of reduction of apocytochrome c for haem attachment.► CcmH is a periplasmic redox protein in cytochrome maturation. ► SCAM and Gaussia luciferase fusion assays were used to analyze the topological structure. ► We have elucidated the topology, catalytic domain structure and redox potential of CcmH. ► CcmH is a strong reductase for haem attachment.

Keywords: Abbreviations; AMS; 4-acetamido-4′-maleimidylstilbene-2,2′-disulfonic acid; CcmH; cytochrome; c; maturation protein H; CD; circular dichroism; CTZ; coelenterazine; DTT; dithiothreitol; Gluc; luciferase from; Gaussia princeps; GSH; glutathione; MPB; N; -(3-maleimidopropionyl) biocytin; NMR; nuclear magnetic resonance; SCAM; scanning cysteine accessibility mutagenesis; TPR; tetratricopeptide repeatCcmH protein; topology; solution structure; redox potential; cytochrome; c; maturation; periplasm

Solution structure and biophysical properties of MqsA, a Zn-containing antitoxin from Escherichia coli by Evangelos Papadopoulos; Jean-Francois Collet; Vukojevic Vladana Vukojević; Martin Billeter; Arne Holmgren; Graslund Astrid Gräslund; Alexios Vlamis-Gardikas (pp. 1401-1408).

The gene ygiT ( mqsA) of Escherichia coli encodes MqsA, the antitoxin of the motility quorum sensing regulator (MqsR). Both proteins are considered to form a DNA binding complex and to be involved in the formation of biofilms and persisters. We have determined the three‐dimensional solution structure of MqsA by high‐resolution NMR. The protein comprises a well‐defined N-terminal domain with a Zn finger motif usually found in eukaryotes, and a defined C-terminal domain with a typical prokaryotic DNA binding helix-turn-helix motif. The two well-defined domains of MqsA have almost identical structure in solution and in the two published crystal structures of dimeric MqsA bound to either MqsR or DNA. However, the connection of the two domains with a flexible linker yields a large variety of possible conformations in solution, which is not reflected in the crystal structures. MqsA binds Zn with all four cysteines, a stoichiometry of 1:1 and a femtomolar affinity ( K_a≥10¹⁷M^–1 at 23°C, pH 7.0).► The two domains of MqsA are connected by a flexible linker. ► The solution structure shows the existence of multiple conformations including a subset corresponding to the described crystal structures. ► Zn binds very tightly to MqsA.

Keywords: Abbreviations; CSI; chemical shift index; DTT; dithiothreitol; HTH; helix turn helix; MqsA; antitoxin encoded by the gene; mqsA; (; ygiT; ); MqsR; motility quorum sensing regulator encoded by the gene; mqsRAntitoxin structure; MqsA; NMR; solution structure; Toxin–antitoxin; Zn fingers

Design and biophysical characterization of a monomeric four-alpha-helix bundle protein Aα₄ with affinity for the volatile anesthetic halothane by Lucia Morstadt; Qing Cheng Meng; Jonas S. Johansson (pp. 1409-1415).

A monomeric four-α-helix bundle protein Aα₄ was designed as a step towards investigating the interaction of volatile general anesthetics with their putative membrane protein targets. The alpha helices, connected by glycine loops, have the sequence A, B, B′, A′. The DNA sequence was designed to make the helices with the same amino acid sequences (helix A and A′, B and B′, respectively) as different as possible, while using codons which are favorable for expression in E. coli. The protein was bacterially expressed and purified to homogeneity using reversed-phase HPLC. Protein identity was verified using MALDI–TOF mass spectrometry. Far-UV circular dichroism spectroscopy confirmed the predominantly alpha-helical nature of the protein Aα₄. Guanidinium chloride induced denaturation showed that the monomeric four-α-helix bundle protein Aα₄ is considerably more stable compared to the dimeric di-α-helical protein (Aα₂-L38M)₂. The sigmoidal character of the unfolding reaction is conserved while the sharpness of the transition is increased 1.8-fold. The monomeric four-α-helix bundle protein Aα₄ bound halothane with a dissociation constant (K_d) of 0.93±0.02mM, as shown by both tryptophan fluorescence quenching and isothermal titration calorimetry. This monomeric four-α-helix bundle protein can now be used as a scaffold to incorporate natural central nervous system membrane protein sequences in order to examine general anesthetic interactions with putative targets in detail.► We designed a monomeric four-α-helix bundle protein Aα₄ with affinity for halothane. ► We expressed, purified, and characterized Aα₄ biophysically. ► Guanidinium chloride denaturation and circular dichroism showed stability of Aα_4. ► Fluorescence quenching spectroscopy and ITC gave K_d=0.9mM for halothane binding. ► Aα₄ can be a tool to investigate anesthetic interaction with native target proteins.

Keywords: Abbreviations; Aα; ₄; monomeric four-α-helix bundle protein; (Aα; ₂; -L38M); ₂; dimeric di-α-helical protein; CD; circular dichroism; GndCl; guanidinium chloride; HPLC; high performance liquid chromatography; ITC; isothermal titration calorimetry; MALDI–TOF; Matrix-assisted laser desorption/ionization time of flightFour-alpha-helix bundle; Volatile anesthetic; Protein design; Circular dichroism; Tryptophan fluorescence quenching; Isothermal titration calorimetry

Computational methods for protein interaction and structural prediction by Yi-Ping Phoebe Chen (pp. 1416-1417).

Protein complex prediction based on maximum matching with domain–domain interaction by Wenji Ma; Craig McAnulla; Lusheng Wang (pp. 1418-1424).

With the development of high-throughput methods for identifying protein–protein interactions, large scale interaction networks are available. Computational methods to analyze the networks to detect functional modules as protein complexes are becoming more important. However, most of the existing methods only make use of the protein–protein interaction networks without considering the structural limitations of proteins to bind together. In this paper, we design a new protein complex prediction method by extending the idea of using domain–domain interaction information. Here we formulate the problem into a maximum matching problem (which can be solved in polynomial time) instead of the binary integer linear programming approach (which can be NP-hard in the worst case). We also add a step to predict domain–domain interactions which first searches the database Pfam using the hidden Markov model and then predicts the domain–domain interactions based on the database DOMINE and InterDom which contain confirmed DDIs. By adding the domain–domain interaction prediction step, we have more edges in the DDI graph and the recall value is increased significantly (at least doubled) comparing with the method of Ozawa et al. (2010) [1] while the average precision value is slightly better. We also combine our method with three other existing methods, such as COACH, MCL and MCODE. Experiments show that the precision of the combined method is improved. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► Domain–domain interaction data are used to predict protein complexes. ► A polynomial time algorithm for maximum matching is used. ► We have an additional domain–domain interaction prediction step. ► Combined with the current methods, the precision is significantly improved.

Keywords: Abbreviations; DDI; domain–domain interaction; HMM; hidden Markov model; PPI; protein–protein interaction; MCL; Markov clusteringDomain–domain interaction; Maximum matching; Protein complex; Protein–protein interaction

MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria by Marcin Magnus; Marcin Pawlowski; Janusz M. Bujnicki (pp. 1425-1433).

Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. The experimental determination of subcellular localization is laborious. Thus, a number of computational methods have been developed to predict the protein location from sequence. However predictions made by different methods often disagree with each other and it is not always clear which algorithm performs best for the given cellular compartment. We benchmarked primary subcellular localization predictors for proteins from Gram-negative bacteria, PSORTb3, PSLpred, CELLO, and SOSUI-GramN, on a common dataset that included 1056 proteins. We found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This motivated us to develop a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. MetaLocGramN runs the primary methods, and based on their output classifies protein sequences into one of five major localizations of the Gram-negative bacterial cell: cytoplasm, plasma membrane, periplasm, outer membrane, and extracellular space. MetaLocGramN achieves the average Matthews correlation coefficient of 0.806, i.e. 12% better than the best individual primary method. MetaLocGramN is a meta-predictor specialized in predicting subcellular localization for proteins from Gram-negative bacteria. According to our benchmark, it performs better than all other tools run independently. MetaLocGramN is a web and SOAP server available for free use by all academic users at the URLhttp://iimcb.genesilico.pl/MetaLocGramN. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► Subcellular localization is a key functional characteristic of proteins. ► We benchmarked subcellular localization predictors for Gram-negative bacteria. ► We developed a meta-predictor MetaLocGramN. ► Our method outperforms all the primary subcellular localization predictors. ► The web/soap server is freely available athttp://iimcb.genesilico.pl/MetaLocGramN/.

Keywords: Subcellular localization; Gram-negative bacteria; Meta-server; Linear regression; Machine learning; Artificial Neural Network

Issues impacting genetic network reverse engineering algorithm validation using small networks by Nguyen Xuan Vinh; Madhu Chetty; Ross Coppel; Pramod P. Wangikar (pp. 1434-1441).

Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth. In this study, we highlight a major issue that the validation of reverse engineering algorithms on small benchmark networks very often results in networks which are not statistically better than a randomly picked network. Another important issue highlighted is that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, we have selected as our case study the IRMA in-vivo synthetic yeast network recently published in Cell. Using Fisher's exact test, we show that many results reported in the literature on reverse-engineering this network are not significantly better than random. The discussion is further extended to some other networks commonly used for validation purposes in the literature. The results presented in this study emphasize that studies carried out using small genetic networks are likely to be trivial, making it imperative that larger real networks be used for validating and benchmarking purposes. If smaller networks are considered, then the results should be interpreted carefully to avoid over confidence. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.►Reverse engineering algorithms often perform no better than random on small networks. ►Small variation in data pre-processing might yield large differences in inferred networks. ►Statistical test of significance should be used for benchmarking algorithms.

Keywords: Abbreviations; BN; Bayesian network; DBN; Dynamic Bayesian network; MI; Mutual Information; GRN; Gene regulatory network; DE; Differential equationBayesian network; Microarray; Normalized mutual information; Gene regulatory network; Statistical test

Glycogen is the primary source of glucose during the lag phase of E. coli proliferation by Tomoaki Yamamotoya; Hitomi Dose; Zhongyuan Tian; Faure Adrien Fauré; Yoshihiro Toya; Masayuki Honma; Kaori Igarashi; Kenji Nakahigashi; Tomoyoshi Soga; Hirotada Mori; Hiroshi Matsuno (pp. 1442-1448).

In the studies of Escherichia coli ( E. coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, we performed a profiling of concentration of metabolites by using batch culture. As a first step, we focused on glucose uptake and the behavior of the first metabolite, G6P (glucose-6-phosphate). A computational formula was derived to express the glucose uptake rate by a single cell from two kinds of experimental data, extracellular glucose concentration and cell growth, being simulated by Cell Illustrator. In addition, average concentration of G6P has been measured by CE-MS. The existence of another carbon source was suggested from the computational result. After careful comparison between cell growth, G6P concentration, and the computationally obtained curve of glucose uptake rate, we predicted the consumption of glycogen in lag phase and its accumulation as an energy source in an E. coli cell for the next proliferation. We confirmed our prediction experimentally. This behavior indicates the importance of glycogen participation in the lag phase for the growth of E. coli. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.Display Omitted► Glycogen acts as the main sugar source in the lag phase of E. coli proliferation. ► Glycogen starts its accumulation from the onset of the log phase. ► G6P concentration has two peaks, caused by intracellular glycogen and extracellular glucose. ► A computational model of carbohydrate metabolism around G6P was constructed.

Keywords: Abbreviations; 3PG; 3-phosphoglycerate; 6PG; 6-phosphogluconate; CSML; cell system markup language; F6P; fructose-6-phosphate; G1P; glucose-1-phosphate; G6P; glucose-6-phosphate; HFPN; hybrid functional Petri net; OD; optical density; PEP; phosphoenolpyruvate; PPP; pentosephosphate pathway; PTS; phosphotransferase system; TCA; tricarboxylic acidCell growth; Computational simulation; E. coli; metabolism; Glycogen

Studies on Shigella boydii infection in Caenorhabditis elegans and bioinformatics analysis of immune regulatory protein interactions by Periyanaina Kesika; Krishnaswamy Balamurugan (pp. 1449-1456).

Shigella boydii causes bacillary dysentery or shigellosis and generates a significant burden in the developing nations. S. boydii-mediated infection assays were performed at both physiological and molecular levels using Caenorhabditis elegans as a host. Continuous exposure of worms to S. boydii showed a reduced life span indicating the pathogenicity of Shigella. Quantitative Real-Time PCR analysis was performed to analyze the expression and regulation of host specific candidate-antimicrobial genes ( clec-60, clec-87, lys-7), which were expressed significantly during early infection, but weakened during the latter hours. Increased mortality of mutant RB1285 by S. boydii and Shigella flexneri indicated the role of lys-7 during Shigella infection. Protein–protein interactions (PPIs) database was used to analyze the interaction of immune proteins in both C. elegans and humans. In addition, the expression and regulation were revealed about immune genes ( clec-61, clec-62, clec-63, F54D5.3 and ZK1320.2), which encode several intermediate immune protein partners (CLEC-61, CLEC-62, CLEC-63, F54D5.3, ZK1320.2, W03D2.6 and THN-2) that interact with LYS-7 and CLEC-60 and were found to play a role in C. elegans immune defense against S. boydii infections. Similarly, the immune genes that are specific to the human defense system, which encode IGHV4-39, A2M, LTF, and CD79A, were predicted to be expressed with LYZ and MBL2, thus indicating their regulation during Shigella infections. Our results using the lowest eukaryotic model system and human database indicated that the major players involved in immunity-related processes appear to be common in cases of Shigella sp. mediated immune responses. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► S. boydii infection in C. elegans at both physiological and molecular level. ► S. boydii infects C. elegans by colonization and affects the egg development. ► Interacting immune regulator of C. elegans during S. boydii infection were analysed. ► Expression levels of immune genes that encode the respective players were analysed.

Keywords: Gene expression; Innate immunity; lys-7; clec-60; clec-87

Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state by Qian Liu; Limsoon Wong; Jinyan Li (pp. 1457-1467).

Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. However, there are few studies to assess the biological significance of binding hot spots. We introduce the notion of biological significance of a contact residue for capturing the probability of the residue occurring in or contributing to protein binding interfaces. We take a statistical Z-score approach to the assessment of the biological significance. The method has three main steps. First, the potential score of a residue is defined by using a knowledge-based potential function with relative accessible surface area calculations. A null distribution of this potential score is then generated from artifact crystal packing contacts. Finally, the Z-score significance of a contact residue with a specific potential score is determined according to this null distribution. We hypothesize that residues at binding hot spots have big absolute values of Z-score as they contribute greatly to binding free energy. Thus, we propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► We design Z-score to capture biological significance of residues in interfaces. ► We calculate residue's potential scores using a knowledge-based potential function. ► Residue's potentials are integrated by ASA and with crystal packing as background. ► We define Z-score based on a null distribution of potentials from crystal packing. ► Z-score is superior to existing methods in classification of binding hot spots.

Keywords: Binding hot spots; Crystal packing; Z-score biological significance

Augmented transitive relationships with high impact protein distillation in protein interaction prediction by Yi-Tsung Tang; Hung-Yu Kao (pp. 1468-1475).

Predicting new protein–protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein–protein interactions through biological experiments is still difficult. Therefore, it is increasingly important to discover new protein interactions. Many studies have predicted protein–protein interactions, using biological features such as Gene Ontology (GO) functional annotations and structural domains of two proteins. In this paper, we propose an augmented transitive relationships predictor (ATRP), a new method of predicting potential protein interactions using transitive relationships and annotations of protein interactions. In addition, a distillation of virtual direct protein–protein interactions is proposed to deal with unbalanced distribution of different types of interactions in the existing protein–protein interaction databases. Our results demonstrate that ATRP can effectively predict protein–protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein–protein interactions. Using the generated benchmark datasets from KUPS to evaluate of all types of the protein–protein interaction, ATRP achieved a 93% precision, a 49% recall and a 64% F-measure in average rate. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► We use a model of augmented transitive relationship to predict protein interaction. ► We propose a distillation method to deal with incomplete interaction data. ► Augmented Transitive Relationship Predictor achieves a good prediction rate. ► The proposed distillation method will improve the recall rate.

Keywords: Abbreviations; ATF; Augmented transitive feature; ATRP; Augmented transitive relationship predictor; PF; –; IYF; Protein frequency–inverse year frequency; GO; Gene Ontology; PPIs; Protein–protein interactions; SVM; Support vector machines; PSI-MI; PSI molecular interactionProtein–protein interaction; Transitive relationship

A leishmaniasis study: Structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase by Abhinav Grover; Shashank Prakash Katiyar; Sanjeev Kumar Singh; Vikash Kumar Dubey; Durai Sundar (pp. 1476-1483).

Protozoa Leishmania donovani (Ld) is the main cause of the endemic disease leishmaniasis. Spermidine synthase (SS), an important enzyme in the synthetic pathway of polyamines in Ld, is an essential element for the survival of this protozoan. Targeting SS may provide an important aid for the development of drugs against Ld. However, absence of tertiary structure of spermidine synthase of Leishmania donovani (LSS) limits the possibilities of structure based drug designing. Presence of the same enzyme in the host itself further challenges the drug development process. We modeled the tertiary structure of LSS using homology modeling approach making use of homologous X-ray crystallographic structure of spermidine synthase of Trypanosoma cruzi (TSS) (2.5Å resolution). The modeled structure was stabilized using Molecular Dynamics simulations. Based on active site structural differences between LSS and human spermidine synthase (HSS), we screened a large dataset of compounds against modeled protein using Glide virtual screen docking and selected two best inhibitors based on their docking scores (−10.04 and −13.11 respectively) with LSS and having least/no binding with the human enzyme. Finally Molecular Dynamics simulations were used to assess the dynamic stability of the ligand bound structures and to elaborate on the binding modes. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► Spermidine Synthase is an essential enzyme for the survival of Leishmania donovani ► We modeled the tertiary structure of leishmanial spermidine synthase using homology based protein modeling technique ► Comparative structural differences in the active site of spermidine synthase from leishmanial and human origin were revealed ► A large dataset of compounds was screened based on these structural differences between the two enzymes ► We report two potent compounds which significantly inhibit leishmanial spermidine synthase along with least/no inhibition of human spermidine synthase

Keywords: Abbreviations; Ld; Leishmania donovani; SS; Spermidine synthase; LSS; Spermidine synthase of; Leishmania donovani; TSS; Spermidine synthase of; Trypanosoma cruzi; HSS; Spermidine synthase of human; DFMO; d; ,; l; -α-difluoromethylornithine; ODC; Ornithine decarboxylase; MD; Molecular dynamics; HTVS; High throughput virtual screening; RMSD; Root mean square deviation; ROG; Radius of gyrationLeishmania; Spermidine synthase; Inhibitors; Docking; Molecular dynamics simulations

Structure-based rebuilding of coevolutionary information reveals functional modules in rhodopsin structure by Keunwan Park; Dongsup Kim (pp. 1484-1489).

Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. We present a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into a protein structure network. A path-based network model is adapted to describe information transfer between residues, and the statistical significance is estimated by network shuffling. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues. In addition, coevolutionary coupling is statistically controlled to remove the structural effects. When applied to the rhodopsin structure, the SCMA method identified a much higher percentage of functional residues than the typical coevolution score (61% vs. 22%). In addition, statistically significant residues are used to construct the coevolved residue–residue subnetwork. The network has one highly connected node (retinal bound Lys296), indicating that Lys296 can induce and regulate most other coevolved residues in a variety of locations. The coevolved network consists of a few modular clusters which have distinct functional roles. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.► We develop the method that extracts functional co-evolutionary interactions only. ► Path-based network model describes information transfer between residues. ► The functional coevolution network has a hub acting as a mutation regulator. ► The network reveals functional modules by network clustering.

Keywords: Correlated mutation analysis; Coevolution; Conservation; Rhodopsin; Functional site; Functional module

Sections

Personal tools

BBA - Proteins and Proteomics (v.1824, #12)

Sections

Personal tools

Local resources

BBA - Proteins and Proteomics (v.1824, #12)