Sugar recognition and protein–protein interaction of mammalian lectins conferring diverse functions

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Construction and evaluation of a novel bifunctional phenylalanine–formate dehydrogenase fusion protein for bienzyme system with cofactor regeneration

Phenylalanine dehydrogenase (PheDH) plays an important role in enzymatic synthesis of l-phenylalanine for aspartame (sweetener) and detection of phenylketonuria (PKU), suggesting that it is important to obtain a PheDH with excellent characteristics. Gene fusion of PheDH and formate dehydrogenase (FDH) was constructed to form bifunctional multi-enzymes for bioconversion of l-phenylalanine coupled with coenzyme regeneration. Comparing with the PheDH monomer from Microbacterium sp., the bifunctional PheDH–FDH showed noteworthy stability under weakly acidic and alkaline conditions (pH 6.5–9.0). The bifunctional enzyme can produce 153.9 mM l-phenylalanine with remarkable performance of enantiomers choice by enzymatic conversion with high molecular conversion rate (99.87 %) in catalyzing phenylpyruvic acid to l-phenylalanine being 1.50-fold higher than that of the separate expression system. The results indicated the potential application of the PheDH and PheDH–FDH with coenzyme regeneration for phenylpyruvic acid analysis and l-phenylalanine biosynthesis in medical diagnosis and pharmaceutical field.     

MolTalk – a programming library for protein structures and structure analysis

Background  

Two of the mostly unsolved but increasingly urgent problems for modern biologists are a) to quickly and easily analyse protein structures and b) to comprehensively mine the wealth of information, which is distributed along with the 3D co-ordinates by the Protein Data Bank (PDB). Tools which address this issue need to be highly flexible and powerful but at the same time must be freely available and easy to learn.     

Synthesis and biological evaluation of novel antiviral agents as protein–protein interaction inhibitors

Abstract

In continuation of our research efforts toward the identification and optimization for novel inhibitors of interaction between human immunodeficiency virus type 1 integrase and cellular cofactor LEDGF/p75, we designed and synthesized a new series of 4-benzylindole derivatives. Most of the title compounds proved to be able to block this protein–protein interaction (PPI), with a percentage ranging from 30% to 90% at 100?µM. The most promising derivative was compound 10b showing IC₅₀ value of 6.41?µM. The main structure–activity relationships (SAR) are discussed and rationalized by docking studies.     

Comparing predictive measures and model functions for estimating plant biomass: lessons from a sagebrush–rabbitbrush community

Plant Ecology - Biologists often use predictive models to estimate plant biomass non-destructively. These models take many forms and employ a variety of predictor measures; however, it is...     

Peptides and peptidomimetics as inhibitors of protein–protein interactions involving β-sheet secondary structures

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Cloning and identifying of a novel protein binding with death domain of the death receptor 4

DR4 (Death Receptor 4) belongs to the tumor necrosis factor (TNF) receptor gene family, which is defined by similar, cysteine-rich extracellular domain and a homologous cytoplasmic sequence termed as "death domain". DR4 can transmit apoptosis signal initiated by Apo2L/TRAIL (TNF-related apoptosis inducing ligand). It can activate caspases within seconds of ligand binding and cause an apoptotic demise of the cell within hours. Despite several investigations, the mechanisms of apoptosis initiation by Apo2L/TRAIL remain unclear.     

IRAA: A statistical tool for investigating a protein–protein interaction interface from multiple structures

Understanding protein–protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.     

Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

Background

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well.

Results

We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly.Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation.

Conclusion

Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.

     

Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation

We present a new method for predicting protein–ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.     

In vivo imaging of protein–protein and RNA–protein interactions using novel far-red fluorescence complementation systems

Imaging of protein–protein and RNA–protein interactions in vivo, especially in live animals, is still challenging. Here we developed far-red mNeptune-based bimolecular fluorescence complementation (BiFC) and trimolecular fluorescence complementation (TriFC) systems with excitation and emission above 600 nm in the ‘tissue optical window’ for imaging of protein–protein and RNA–protein interactions in live cells and mice. The far-red mNeptune BiFC was first built by selecting appropriate split mNeptune fragments, and then the mNeptune-TriFC system was built based on the mNeptune-BiFC system. The newly constructed mNeptune BiFC and TriFC systems were verified as useful tools for imaging protein–protein and mRNA–protein interactions, respectively, in live cells and mice. We then used the new mNeptune-TriFC system to investigate the interactions between human polypyrimidine-tract-binding protein (PTB) and HIV-1 mRNA elements as PTB may participate in HIV mRNA processing in HIV activation from latency. An interaction between PTB and the 3′long terminal repeat region of HIV-1 mRNAs was found and imaged in live cells and mice, implying a role for PTB in regulating HIV-1 mRNA processing. The study provides new tools for in vivo imaging of RNA–protein and protein–protein interactions, and adds new insight into the mechanism of HIV-1 mRNA processing.     

Diacylglycerol kinase γ is a novel anionic phospholipid binding protein with a selective binding preference

There are ten isozymes of diacylglycerol kinase (DGK), and they regulate diverse patho-physiological functions. Here, we investigated the lipid-binding properties of DGK isozymes using protein–lipid overlay and liposome-binding assays. DGKγ showed a strong binding activity compared with other DGK isozymes for phosphatidic acid (PA) among the various glycerophospholipids tested. However, DGKγ failed to interact with DG and lyso-PA. Moreover, the isozyme was capable of binding to ceramide-1-phosphate but not to ceramide or sphingosine-1-phosphate. The isozyme bound more strongly to PA containing unsaturated fatty acid than to PA having only saturated fatty acid. An analysis using a series of deletion mutants of DGKγ revealed that the N-terminal region, which contains a recoverin homology domain and EF-hand motifs, is responsible for the PA binding activity of DGKγ. Taken together, these results indicate that DGKγ is an anionic phospholipid binding protein that preferably interacts with a small highly charged head group that is very close to the glycerol or sphingosine backbone.     

Detection of protein–protein interactions by a combination of a novel cytoplasmic membrane targeting system of recombinant proteins and fluorescence resonance energy transfer

A novel protein molecular targeting system was created using a cytoplasmic face of a plasma membrane-targeting system in Saccharomyces cerevisiae. The technique involves a molecular display for the creation of a novel reaction site and interaction sites in the field of biotechnology. In a model system, a fluorescent protein was targeted as a reporter to the cytoplasmic face of the plasma membrane. The C-terminal transmembrane domain (CTM) of Ras2p and Snc2p was examined as the portions with anchoring ability to the cytoplasmic face of the plasma membrane. We found that the CTM of Snc2p targeted the enhanced cyan fluorescent protein (ECFP)–protein A fusion protein on the cytoplasmic face of the plasma membrane more strongly than that of Ras2p. To develop it for use as a detection system for protein–protein interactions, the Fc fragment of IgG (Fc) was genetically fused with the enhanced yellow fluorescent protein (EYFP) and expressed in the cytoplasm of the ECFP–protein A-anchored cell. A microscopic analysis showed that fluorescence resonance energy transfer (FRET) between ECFP–protein A and EYFP–Fc occurred, and the change in fluorescence was observed on the cytoplasmic face of the plasma membrane. The detection of protein–protein interactions at the cytoplasmic face of a plasma membrane using FRET combined with a cytoplasmic face-targeting system for proteins provides a novel method for examining the molecular interactions of cytoplasmic proteins, in addition to conventional methods, such as the two-hybrid method in the nuclei. S. Shibasaki and K. Kuroda equally contributed to this work     

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets

Background  

In structural genomics, an important goal is the detection and classification of protein–protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein–protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue–residue interactions and a stepwise distance-dependence. We used increased computational ressources, however, constructing 290,000 decoys for 219 protein–protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment.     

TRP channel–associated factors are a novel protein family that regulates TRPM8 trafficking and activity

TRPM8 is a cold sensor that is highly expressed in the prostate as well as in other non-temperature-sensing organs, and is regulated by downstream receptor–activated signaling pathways. However, little is known about the intracellular proteins necessary for channel function. Here, we identify two previously unknown proteins, which we have named “TRP channel–associated factors” (TCAFs), as new TRPM8 partner proteins, and we demonstrate that they are necessary for channel function. TCAF1 and TCAF2 both bind to the TRPM8 channel and promote its trafficking to the cell surface. However, they exert opposing effects on TRPM8 gating properties. Functional interaction of TCAF1/TRPM8 also leads to a reduction in both the speed and directionality of migration of prostate cancer cells, which is consistent with an observed loss of expression of TCAF1 in metastatic human specimens, whereas TCAF2 promotes migration. The identification of TCAFs introduces a novel mechanism for modulation of TRPM8 channel activity.