Detection of Serum Protein Biomarkers for the Diagnosis and Staging of Hepatoblastoma

The present study aimed to identify serum biomarkers for the detection of hepatoblastoma (HB). Serum samples were collected from 71 HB patients (stage I, n = 19; stage II, n = 19, stage III, n = 19; and stage IV, n = 14) and 23 age- and sex-matched healthy children. Differential expression of serum protein markers were screened using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), and the target proteins were isolated and purified using HPLC and identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), SEQUEST, and bioinformatics analysis. Differential protein expression was confirmed by enzyme-linked immunosorbent analysis (ELISA). SELDI-TOF-MS screening identified a differentially expressed protein with an m/z of 9348 Da, which was subsequently identified as Apo A–I; its expression was significantly lower in the HB group as compared to the normal control group (1546.67 ± 757.81 vs. 3359.21 ± 999.36, respectively; p < 0.01). Although the expression level decreased with increasing disease stage, pair-wise comparison revealed significant differences in Apo A–I expression between the normal group and the HB subgroups (p < 0.01). ELISA verified the reduced expression of Apo A–I in the HB group. Taken together, these results suggest that Apo A–I may represent a serum protein biomarker of HB. Further studies will assess the value of using Apo A–I expression for HB diagnosis and staging.     

Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.     

A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the present work, we set up and applied a computational workflow for the identification of putative fragment binders in large virtual databases. To validate the method, the selected compounds were tested in vitro to assess the CK1δ inhibition.     

Molecular Basis of Multiple Mitochondrial Dysfunctions Syndrome 2 Caused by CYS59TYR BOLA3 Mutation

Multiple mitochondrial dysfunctions syndrome (MMDS) is a rare neurodegenerative disorder associated with mutations in genes with a vital role in the biogenesis of mitochondrial [4Fe–4S] proteins. Mutations in one of these genes encoding for BOLA3 protein lead to MMDS type 2 (MMDS2). Recently, a novel phenotype for MMDS2 with complete clinical recovery was observed in a patient containing a novel variant (c.176G > A, p.Cys59Tyr) in compound heterozygosity. In this work, we aimed to rationalize this unique phenotype observed in MMDS2. To do so, we first investigated the structural impact of the Cys59Tyr mutation on BOLA3 by NMR, and then we analyzed how the mutation affects both the formation of a hetero-complex between BOLA3 and its protein partner GLRX5 and the iron–sulfur cluster-binding properties of the hetero-complex by various spectroscopic techniques and by experimentally driven molecular docking. We show that (1) the mutation structurally perturbed the iron–sulfur cluster-binding region of BOLA3, but without abolishing [2Fe–2S]²⁺ cluster-binding on the hetero-complex; (2) tyrosine 59 did not replace cysteine 59 as iron–sulfur cluster ligand; and (3) the mutation promoted the formation of an aberrant apo C59Y BOLA3–GLRX5 complex. All these aspects allowed us to rationalize the unique phenotype observed in MMDS2 caused by Cys59Tyr mutation.     

Residue–Residue Interaction Prediction via Stacked Meta-Learning

Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta’s performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.     

Decreased Interactions between Calmodulin and a Mutant Huntingtin Model Might Reduce the Cytotoxic Level of Intracellular Ca2+: A Molecular Dynamics Study

Mutant huntingtin (m-HTT) proteins and calmodulin (CaM) co-localize in the cerebral cortex with significant effects on the intracellular calcium levels by altering the specific calcium-mediated signals. Furthermore, the mutant huntingtin proteins show great affinity for CaM that can lead to a further stabilization of the mutant huntingtin aggregates. In this context, the present study focuses on describing the interactions between CaM and two huntingtin mutants from a biophysical point of view, by using classical Molecular Dynamics techniques. The huntingtin models consist of a wild-type structure, one mutant with 45 glutamine residues and the second mutant with nine additional key-point mutations from glutamine residues into proline residues (9P(EM) model). Our docking scores and binding free energy calculations show higher binding affinities of all HTT models for the C-lobe end of the CaM protein. In terms of dynamic evolution, the 9P(EM) model triggered great structural changes into the CaM protein’s structure and shows the highest fluctuation rates due to its structural transitions at the helical level from α-helices to turns and random coils. Moreover, our proposed 9P(EM) model suggests much lower interaction energies when compared to the 45Qs-HTT mutant model, this finding being in good agreement with the 9P(EM)’s antagonistic effect hypothesis on highly toxic protein–protein interactions.     

Therapeutic Strategies to Reduce the Toxicity of Misfolded Protein Oligomers

The aberrant aggregation of proteins is implicated in the onset and pathogenesis of a wide range of neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases. Mounting evidence indicates that misfolded protein oligomers produced as intermediates in the aggregation process are potent neurotoxic agents in these diseases. Because of the transient and heterogeneous nature of these elusive aggregates, however, it has proven challenging to develop therapeutics that can effectively target them. Here, we review approaches aimed at reducing oligomer toxicity, including (1) modulating the oligomer populations (e.g., by altering the kinetics of aggregation by inhibiting, enhancing, or redirecting the process), (2) modulating the oligomer properties (e.g., through the size–hydrophobicity–toxicity relationship), (3) modulating the oligomer interactions (e.g., by protecting cell membranes by displacing oligomers), and (4) reducing oligomer toxicity by potentiating the protein homeostasis system. We analyze examples of these complementary approaches, which may lead to the development of compounds capable of preventing or treating neurodegenerative disorders associated with protein aggregation.     

Effect of pH on the Aggregation of α-syn12 Dimer in Explicit Water by Replica-Exchange Molecular Dynamics Simulation

The dimeric structure of the N-terminal 12 residues drives the interaction of α-synuclein protein with membranes. Moreover, experimental studies indicated that the aggregation of α-synuclein is faster at low pH than neutral pH. Nevertheless, the effects of different pH on the structural characteristics of the α-syn12 dimer remain poorly understood. We performed 500 ns temperature replica exchange molecular dynamics (T-REMD) simulations of two α-syn12 peptides in explicit solvent. The free energy surfaces contain ten highly populated regions at physiological pH, while there are only three highly populated regions contained at acidic pH. The anti-parallel β-sheet conformations were found as the lowest free energy state. Additionally, these states are nearly flat with a very small barrier which indicates that these states can easily transit between themselves. The dimer undergoes a disorder to order transition from physiological pH to acidic pH and the α-syn12 dimer at acidic pH involves a faster dimerization process. Further, the Lys6–Asp2 contact may prevent the dimerization.     

Promotion and Inhibition of Amyloid-β Peptide Aggregation: Molecular Dynamics Studies

Aggregates of amyloid-β (Aβ) peptides are known to be related to Alzheimer’s disease. Their aggregation is enhanced at hydrophilic–hydrophobic interfaces, such as a cell membrane surface and air-water interface, and is inhibited by polyphenols, such as myricetin and rosmarinic acid. We review molecular dynamics (MD) simulation approaches of a full-length Aβ peptide, Aβ40, and Aβ(16–22) fragments in these environments. Since these peptides have both hydrophilic and hydrophobic amino acid residues, they tend to exist at the interfaces. The high concentration of the peptides accelerates the aggregation there. In addition, Aβ40 forms a β-hairpin structure, and this structure accelerates the aggregation. We also describe the inhibition mechanism of the Aβ(16–22) aggregation by polyphenols. The aggregation of Aβ(16–22) fragments is caused mainly by the electrostatic attraction between charged amino acid residues known as Lys16 and Glu22. Since polyphenols form hydrogen bonds between their hydroxy and carboxyl groups and these charged amino acid residues, they inhibit the aggregation.     

Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein–ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.     

EAAC1 Gene Deletion Increases Neuronal Death and Blood Brain Barrier Disruption after Transient Cerebral Ischemia in Female Mice

EAAC1 is important in modulating brain ischemic tolerance. Mice lacking EAAC1 exhibit increased susceptibility to neuronal oxidative stress in mice after transient cerebral ischemia. EAAC1 was first described as a glutamate transporter but later recognized to also function as a cysteine transporter in neurons. EAAC1-mediated transport of cysteine into neurons contributes to neuronal antioxidant function by providing cysteine substrates for glutathione synthesis. Here we evaluated the effects of EAAC1 gene deletion on hippocampal blood vessel disorganization after transient cerebral ischemia. EAAC1^−/− female mice subjected to transient cerebral ischemia by common carotid artery occlusion for 30 min exhibited twice as much hippocampal neuronal death compared to wild-type female mice as well as increased reduction of neuronal glutathione, blood–brain barrier (BBB) disruption and vessel disorganization. Pre-treatment of N-acetyl cysteine, a membrane-permeant cysteine prodrug, increased basal glutathione levels in the EAAC1^−/− female mice and reduced ischemic neuronal death, BBB disruption and vessel disorganization. These findings suggest that cysteine uptake by EAAC1 is important for neuronal antioxidant function under ischemic conditions.     

Structural Characterization of Receptor–Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).     

Influences of Electromagnetic Energy on Bio-Energy Transport through Protein Molecules in Living Systems and Its Experimental Evidence

The influences of electromagnetic fields (EMFs) on bio-energy transport and its mechanism of changes are investigated through analytic and numerical simulation and experimentation. Bio-energy transport along protein molecules is performed by soliton movement caused by the dipole–dipole electric interactions between neighboring amino acid residues. As such, EMFs can affect the structure of protein molecules and change the properties of the bio-energy transported in living systems. This mechanism of biological effect from EMFs involves the amino acid residues in protein molecules. To study and reveal this mechanism, we simulated numerically the features of the movement of solitons along protein molecules with both a single chain and with three channels by using the Runge–Kutta method and Pang’s soliton model under the action of EMFs with the strengths of 25,500, 51,000, 76,500, and 102,000 V/m in the single-chain protein, as well as 17,000, 25,500, and 34,000 V/m in the three-chain protein, respectively. Results indicate that electric fields (EFs) depress the binding energy of the soliton, decrease its amplitude, and change its wave form. Also, the soliton disperses at 102,000 V/m in a single-chain protein and at 25,500 and 34,000 V/m in three-chain proteins. These findings signify that the influence of EMFs on the bio-energy transport cannot be neglected; however, these variations depend on both the strength and the direction of the EF in the EMF. This direction influences the biological effects of EMF, which decrease with increases in the angle between the direction of the EF and that of the dipole moment of amino acid residues; however, randomness at the macroscopic level remains. Lastly, we experimentally confirm the existence of a soliton and the validity of our conclusion by using the infrared spectra of absorption of the collagens, which is activated by another type of EF. Thus, we can affirm that both the described mechanism and the corresponding theory are correct and that EMFs or EFs can influence the features of energy transport in living systems and thus have certain biological effects.     

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.     

Lights,Camera, Interaction: Studying Protein–Protein Interactions of the ER Protein Translocase in Living Cells

Various landmark studies have revealed structures and functions of the Sec61/SecY complex in all domains of live demonstrating the conserved nature of this ancestral protein translocase. While the bacterial homolog of the Sec61 complex resides in the plasma membrane, the eukaryotic counterpart manages the transfer of precursor proteins into or across the membrane of the endoplasmic reticulum (ER). Sec61 complexes are accompanied by a set of dynamically recruited auxiliary proteins assisting the transport of certain precursor polypeptides. TRAP and Sec62/Sec63 are two auxiliary protein complexes in mammalian cells that have been characterized by structural and biochemical methods. Using these ER membrane protein complexes for our proof-of-concept study, we aimed to detect interactions of membrane proteins in living mammalian cells under physiological conditions. Bimolecular luminescence complementation and competition was used to demonstrate multiple protein–protein interactions of different topological layouts. In addition to the interaction of the soluble catalytic and regulatory subunits of the cytosolic protein kinase A, we detected interactions of ER membrane proteins that either belong to the same multimeric protein complex (intra-complex interactions: Sec61α–Sec61β, TRAPα–TRAPβ) or protein complexes in juxtaposition (inter-complex interactions: Sec61α–TRAPα, Sec61α–Sec63, and Sec61β–Sec63). In the process, we established further control elements like synthetic peptide complementation for expression profiling of fusion constructs and protease-mediated reporter degradation demonstrating the cytosolic localization of a reporter complementation. Ease of use and flexibility of the approach presented here will spur further research regarding the dynamics of protein–protein interactions in response to changing cellular conditions in living cells.     

Screening for Interacting Proteins with Peptide Biomarker of Blood–Brain Barrier Alteration under Inflammatory Conditions

Neurodegenerative diseases are characterized by increased permeability of the blood–brain barrier (BBB) due to alterations in cellular and structural components of the neurovascular unit, particularly in association with neuroinflammation. A previous screening study of peptide ligands to identify molecular alterations of the BBB in neuroinflammation by phage-display, revealed that phage clone 88 presented specific binding affinity to endothelial cells under inflammatory conditions in vivo and in vitro. Here, we aimed to identify the possible target receptor of the peptide ligand 88 expressed under inflammatory conditions. A cross-link test between phage-peptide-88 with IL-1β-stimulated human hCMEC cells, followed by mass spectrometry analysis, was used to identify the target of peptide-88. We modeled the epitope–receptor molecular interaction between peptide-88 and its target by using docking simulations. Three proteins were selected as potential target candidates and tested in enzyme-linked immunosorbent assays with peptide-88: fibronectin, laminin subunit α5 and laminin subunit β-1. Among them, only laminin subunit β-1 presented measurable interaction with peptide-88. Peptide-88 showed specific interaction with laminin subunit β-1, highlighting its importance as a potential biomarker of the laminin changes that may occur at the BBB endothelial cells under pathological inflammation conditions.     

Genome-Wide Identification and Analysis of CC-NBS-LRR Family in Response to Downy Mildew and Black Rot in Chinese Cabbage

The nucleotide-binding site–leucine-rich repeat (NBS–LRR) gene family is the largest group of plant disease resistance (R) genes widespread in response to viruses, bacteria, and fungi usually involved in effector triggered immunity (ETI). Forty members of the Chinese cabbage CC type NBS–LRR family were investigated in this study. Gene and protein characteristics, such as distributed locations on chromosomes and gene structures, were explored through comprehensive analysis. CC–NBS–LRR proteins were classified according to their conserved domains, and the phylogenetic relationships of CC–NBS–LRR proteins in Brassica rapa, Arabidopsis thaliana, and Oryza sativa were compared. Moreover, the roles of BrCC–NBS–LRR genes involved in pathogenesis-related defense were studied and analyzed. First, the expression profiles of BrCC–NBS–LRR genes were detected by inoculating with downy mildew and black rot pathogens. Second, sensitive and resistant Chinese cabbage inbred lines were screened by downy mildew and black rot. Finally, the differential expression levels of BrCC–NBS–LRR genes were monitored at 0, 1, 3, 6, 12 and 24 h for short and 0, 3, 5, 7, 10 and 14 days for long inoculation time. Our study provides information on BrCC–NBS–LRR genes for the investigation of the functions and mechanisms of CC-NBS-LRR genes in Chinese cabbage.     

Systematic Approach to Find the Global Minimum of Relaxation Dispersion Data for Protein-Induced B–Z Transition of DNA

Carr–Purcell–Meiboom–Gill (CPMG) relaxation dispersion spectroscopy is commonly used for quantifying conformational changes of protein in μs-to-ms timescale transitions. To elucidate the dynamics and mechanism of protein binding, parameters implementing CPMG relaxation dispersion results must be appropriately determined. Building an analytical model for multi-state transitions is particularly complex. In this study, we developed a new global search algorithm that incorporates a random search approach combined with a field-dependent global parameterization method. The robust inter-dependence of the parameters carrying out the global search for individual residues (GSIR) or the global search for total residues (GSTR) provides information on the global minimum of the conformational transition process of the Zα domain of human ADAR1 (hZα_ADAR1)–DNA complex. The global search results indicated that a α-helical segment of hZα_ADAR1 provided the main contribution to the three-state conformational changes of a hZα_ADAR1—DNA complex with a slow B–Z exchange process. The two global exchange rate constants, k_ex and k_ZB, were found to be 844 and 9.8 s⁻¹, respectively, in agreement with two regimes of residue-dependent chemical shift differences—the “dominant oscillatory regime” and “semi-oscillatory regime”. We anticipate that our global search approach will lead to the development of quantification methods for conformational changes not only in Z-DNA binding protein (ZBP) binding interactions but also in various protein binding processes.     

Rate and Product Studies with 1-Adamantyl Chlorothioformate under Solvolytic Conditions

A study was carried out on the solvolysis of 1-adamantyl chlorothioformate (1-AdSCOCl, 1) in hydroxylic solvents. The rate constants of the solvolysis of 1 were well correlated using the Grunwald–Winstein equation in all of the 20 solvents (R = 0.985). The solvolyses of 1 were analyzed as the following two competing reactions: the solvolysis ionization pathway through the intermediate (1-AdSCO)⁺ (carboxylium ion) stabilized by the loss of chloride ions due to nucleophilic solvation and the solvolysis–decomposition pathway through the intermediate 1-Ad⁺Cl⁻ ion pairs (carbocation) with the loss of carbonyl sulfide. In addition, the rate constants (k_exp) for the solvolysis of 1 were separated into k_1-Ad⁺Cl⁻ and k_{1-AdSCO⁺Cl⁻} through a product study and applied to the Grunwald–Winstein equation to obtain the sensitivity (m-value) to change in solvent ionizing power. For binary hydroxylic solvents, the selectivities (S) for the formation of solvolysis products were very similar to those of the 1-adamantyl derivatives discussed previously. The kinetic solvent isotope effects (KSIEs), salt effects and activation parameters for the solvolyses of 1 were also determined. These observations are compared with those previously reported for the solvolyses of 1-adamantyl chloroformate (1-AdOCOCl, 2). The reasons for change in reaction channels are discussed in terms of the gas-phase stabilities of acylium ions calculated using Gaussian 03.