Three-dimensional Structure Prediction Research Articles

The Enhanced Cytotoxic Effects of the p28-Apoptin Chimeric Protein As A Novel Anti-Cancer Agent on Breast Cancer Cell Lines.

Peptide-based drugs have shown promising results in overcoming the limitations of chemotherapeutic drugs by providing a targeted therapy approach to cancer. However, the response rate of targeted therapies is limited, in large part due to the intra- and inter-heterogeneity of tumors. In this study, we engineered a novel chimeric protein composed of the p28 peptide as a tumor-homing killer peptide and apoptin as a killer peptide. We evaluated its cytotoxicity against MCF7 and MDA-MB-231 breast cancer cells and HEK-293 normal cells by the MTT assay. Different linkers were evaluated when designing the chimeric protein. Three-dimensional structure predictions of chimeric proteins with different linkers were carried out by Modeller 9.19, and their validation and analysis were performed by RAMPAGE. Results showed that a cleavable linker, including furin cleavage sites, is preferred over other linkers. The chimeric protein was then successfully expressed in E. coli and purified by affinity chromatography under native conditions, then confirmed by SDS-PAGE and Western blot analysis. Compared with apoptin alone, the chimeric protein showed significantly higher toxicity against breast cancer cell lines in a dose-dependent manner. The IC50 values of the chimeric protein for MCF7 and MDA-MB-231 cells were 38.55 µg/mL and 43.11 µg/mL, respectively. There was no significant cytotoxic effect on the normal HEK293 cell line. This study demonstrates that fusion of p28 peptide to a potent protein could provide an effective method for tumor targeting. Further, in vitro and in vivo studies of this novel chimeric protein are underway.

Cadmium-induced conformational changes in type 2 metallothionein of medicinal plant Coptis japonica: insights from molecular dynamics studies of apo, partially and fully metalated forms

Plants play an important role in the removal of excess heavy metals from soil and water. Medicinal plants can also have non-traditional use in phytoremediation technologies. Among the heavy metals, Cadmium (Cd) is the most abundant and readily taken up by the crop plants. Plant metallothioneins (MTs) are small proteins having cysteine-rich residues and appear to play key roles in metal homoeostasis. Plant metallothionein 2 (MT 2) from Coptis japonica (Gold-thread; CjMT 2) is a typical member of this subfamily and features two cysteine-rich regions containing eight and six cysteine residues, respectively, separated by 42 amino acids long linker region. In-silico analysis of MT 2 protein sequences of C. japonica was performed. In this study, ab initio methods were utilised for the prediction of three-dimensional structure of CjMT 2. After structure validation, heavy metal-binding sites were predicted for the selected modelled structures of CjMT 2. To obtain Cdi-CjMT 2 (i = 1–7), metalated complex individual docking experiments were performed. The stability of the metalated docked structures was assessed by molecular dynamics (MD) simulation studies. Our study showed that CjMT 2 binds up to 4 Cd2+ ions in two distinct domains: a N-terminal β-domain that binds to 2 Cd2+ ions and a C-terminal α-domain that binds with 2 Cd2+ ions. Our analysis revealed that Cys residues of alpha and beta domain and some residues of spacer region of CjMT 2 protein might be important for the cadmium interaction. MD simulation studies provided insight into metal-induced conformational changes and mechanism of metalation of CjMT 2, an intrinsically disordered protein. This study provides useful insights into mechanism of cadmium-type 2 metallothionein interaction.

A Memetic Algorithm for 3-D Protein Structure Prediction Problem.

Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on nineteen protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.

Protein-altering variants of PTPN2 in childhood-onset Type 1A diabetes.

To examine the contribution of PTPN2 coding variants to the risk of childhood-onset Type 1A diabetes. PTPN2 mutation analysis was carried out for 169 unrelated Japanese people with childhood-onset Type 1A diabetes. We searched for coding variants that were absent or extremely rare in the general population and were scored as damaging by multiple in silico programs. We performed mRNA analysis and three-dimensional structural prediction of the detected variants, when possible. We also examined possible physical links between these variants and previously reported risk SNPs as well as clinical information from variant-positive children. One frameshift variant (p.Q286Yfs*24) and two probably damaging missense substitutions (p.C232W and p.R350Q) were identified in one child each. Of these, p.Q286Yfs*24 and p.C232W were hitherto unreported, while p.R350Q accounted for 2/121,122 alleles of the exome datasets. The p.Q286Yfs*24 variant did not encode stable mRNA, and p.C232W appeared to affect the structure of the tyrosine-protein phosphatase domain. The three variants were physically unrelated to known risk SNPs. The variant-positive children manifested Type 1A diabetes without additional clinical features and invariably carried risk human leukocyte antigen alleles. The results provide the first indication that PTPN2 variants contribute to the risk of Type 1A diabetes, independently of known risk SNPs. PTPN2 coding variants possibly induce non-specific Type 1A diabetes phenotypes in individuals with human leukocyte antigen-mediated disease susceptibility. Our findings warrant further validation.

Computational Methods Involved in Evaluating the Toxicity of the Reproductive Toxicants in Sertoli Cell.

The Sertoli cell, the somatic component of seminiferous tubule, provides nutritional support and immunological protection and supports overall growth and division of germ cells. Cytoskeletons, junction proteins, and kinases in Sertoli cells are prime targets for reproductive toxicants and other environmental contaminants. Among the varied targets, the kinases that are crucial for regulating varied activities in spermatogenesis such as assembly/disassembly of blood-testis barrier and apical ES and those that are involved in conferring polarity are highly targeted. In an attempt to study the effect of toxicants on these kinases, the present chapter deals with computational methodology concerning their three-dimensional structure prediction, identification of inhibitors, and understanding of conformational changes induced by these inhibitors.

Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates.

The current RNA structure prediction methods cannot keep up the pace of the rapidly increasing number of sequences and the emerging new functions of RNAs. Template-based RNA three-dimensional structure prediction methods are restricted by the limited number of known RNA structures, and traditional motif-based search for the templates does not always lead to successful results. Here we report a new template search and assembly algorithm, the hierarchical loop template-assembly method (VfoldLA). The method searches for templates for single strand loop/junctions instead of the whole motifs, which often renders no available templates, or short fragments (several nucleotides), which requires a long computational time to assemble and refine. The VfoldLA method has the advantage of accounting for local and nonlocal interloop interactions. Benchmark tests indicate that this new method can provide low-resolution predictions for RNA conformations at different levels of structural complexities. Furthermore, the VfoldLA-predicted conformations may also serve as reliable putative models for further structure prediction and refinements. VfoldLA is accessible at http://rna.physics.missouri.edu/vfoldLA .

Purification, characterization and application of a novel antimicrobial peptide from Andrias davidianus blood.

This is the pioneer study on screening and isolation of antimicrobial peptide from the blood of Andrias davidianus. Here, we have developed a novel method by combining magnetic liposomes adsorption with reversed-phase high-performance liquid chromatography to purify and screen the antimicrobial peptides. From this screen, we identified a novel antimicrobial peptide which we name as andricin B. Andricin B is unique as it checks the growth of both Gram-positive and Gram-negative bacteria as well as few fungal species.

Three-dimensional protein structure prediction based on memetic algorithms

Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as a NP-hard problem. In this paper, we proposed a first-principle method that makes use of a priori information about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multimodal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in the Protein Data Bank to guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach.

Pentapeptide-insertion scanning mutational analysis of turkey herpesvirus HVT063 reveals residues important for its RNA silencing suppression activity.

HVT063, an RNA-binding protein encoded by turkey herpesvirus, has been shown previously to suppress RNA silencing. Here, a scanning library produced by pentapeptide-insertion scanning mutagenesis was used to identify key residues associated with its RNA silencing suppressor (RSS) activity. Forty-two in-frame insertion mutants of HVT063 protein were evaluated for their RSS activity using the dual-luciferase transient expressing assay system. Sixteen mutations resulted in a loss of RSS activity, 20 mutations resulted in decreased RSS activity, and six mutations exhibited high RSS activity similar to wild-type HVT063. Based on a three-dimensional structure prediction, most of the loss-of-function mutations were located around a predominantly α-helical region at the C-terminal end of HVT063. In particular, a conserved domain in this region, named herpes_UL69, showed low tolerance for five-amino-acid insertions. Combined with the results of our previous studies, basic amino acids could play a key role in RSS activity. These results also demonstrate that pentapeptide-insertion scanning mutagenesis combined with dual-luciferase assays is an effective method to functionally characterize RSSs.

Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins

Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton’s equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10–46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10–34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

Small-Angle X-ray Scattering Data in Combination with RosettaDock Improves the Docking Energy Landscape.

We have performed a benchmark to evaluate the relative success of using small-angle X-ray scattering (SAXS) data as constraints (hereafter termed SAXSconstrain) in the RosettaDock protocol (hereafter termed RosettaDockSAXS). For this purpose, we have chosen 38 protein complex structures, calculated the theoretical SAXS data for the protein complexes using the program CRYSOL, and then used the SAXS data as constraints. We further considered a few examples where crystal structures and experimental SAXS data are available. SAXSconstrain were added to the protocol in the initial, low-resolution docking step, allowing fast rejection of complexes that violate the shape restraints imposed by the SAXS data. Our results indicate that the implementation of SAXSconstrain in general reduces the sampling space of possible protein-protein complexes significantly and can indeed increase the probability of finding near-native protein complexes. The methodology used is based on rigid-body docking and works for cases where no or minor conformational changes occur upon binding of the docking partner. In a wider perspective, the strength of RosettaDockSAXS lies in the combination of low-resolution structural information on protein complexes in solution from SAXS experiments with protein-protein interaction energies obtained from RosettaDock, which will allow the prediction of unknown three-dimensional atomic structures of protein-protein complexes.

Nuclear Magnetic Resonance Structure of an 8 × 8 Nucleotide RNA Internal Loop Flanked on Each Side by Three Watson-Crick Pairs and Comparison to Three-Dimensional Predictions.

The prediction of RNA three-dimensional structure from sequence alone has been a long-standing goal. High-resolution, experimentally determined structures of simple noncanonical pairings and motifs are critical to the development of prediction programs. Here, we present the nuclear magnetic resonance structure of the (5'CCAGAAACGGAUGGA)2 duplex, which contains an 8 × 8 nucleotide internal loop flanked by three Watson-Crick pairs on each side. The loop is comprised of a central 5'AC/3'CA nearest neighbor flanked by two 3RRs motifs, a known stable motif consisting of three consecutive sheared GA pairs. Hydrogen bonding patterns between base pairs in the loop, the all-atom root-mean-square deviation for the loop, and the deformation index were used to compare the structure to automated predictions by MC-sym, RNA FARFAR, and RNAComposer.

NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies.

The development of computational methods to accurately model three-dimensional protein structures from sequences of amino acid residues is becoming increasingly important to the structural biology field. This paper addresses the challenge of predicting the tertiary structure of a given amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We present a new method, namely NEAT-FLEX, based on NeuroEvolution of Augmenting Topologies (NEAT) to extract structural features from (ABS) proteins that are determined experimentally. The proposed method manipulates structural information from the Protein Data Bank (PDB) and predicts the conformational flexibility (FLEX) of residues of a target amino acid sequence. This information may be used in three-dimensional structure prediction approaches as a way to reduce the conformational search space. The proposed method was tested with 24 different amino acid sequences. Evolving neural networks were compared against a traditional error back-propagation algorithm; results show that the proposed method is a powerful way to extract and represent structural information from protein molecules that are determined experimentally.

Adding Dietary Green Beans to Formula Improves Stool Consistency in Neonates after Undergoing a Bowel Resection

Background: Fyn kinase involved in a variety of different signalling pathways. Their abnormal activity has been shown to be related to severe pathologies, such as Alzheimer’s and Atherosclerosis diseases making Fyn an attractive target for the discovery of potential novel therapeutics for these diseases. Given the lack of a specific Fyn inhibitors even though the great number of compounds active on other members of Src family or on different tyrosine kinases; we carried out a study to identify new Fyn kinase inhibitors by suggesting inhibitors scaffold more active and selective against Fyn kinase. Results: The first part of the study was devoted to the generation of two-dimensional (2D) quantitative structure-activity relationship (QSAR) models to compounds with known IC50 values. These molecules belong to type I and type II inhibitors. 2D-QSAR models were validated and were proved to be reliable models useful to predict activity enhancements for new compounds. Pharmacophore models were also created and the interaction between Fyn kinase and the most active inhibitors were studied by the docking-based virtual screening. Molecular docking related to the prediction of three-dimensional (3D) structure of Fyn kinase on DFG-out conformation and obtained conformation was evaluated by the principal methods of 3D-structure validation. Conclusion: These studies provide a basis for efficiently validate, design and screen a large number of most potent and selective inhibitors against Fyn kinase.

Identify High-Quality Protein Structural Models by EnhancedK-Means

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.

Three-dimensional Structure Prediction of the Human LMTK3 Catalytic Domain in DYG-in Conformation

Lemur Tyrosine Kinase 3 (LMTK3) plays a key role in the regulation of α Estrogen Receptor (ERα) activity. It has been defined as an essential actor involved in the endocrine resistance process in breast cancer patients accelerating the dispersion and invasion of tumor cells which are the first steps of the metastatic process. In the absence of a crystallized structure of LMTK3 and in order to study its inhibition, we generated its tridimensional structure. We constructed the LMTK3 kinase in its active state (DYG-in) using the homology modeling approach. The evaluation of the generated model by several tools indicated the reliability of the predicted 3D structure and the good quality of the stereochemical characteristics model were confirmed by the PROCHECK tool. In conclusion, the docking approach used to study LMTK3-ATP interaction allows us to determine key residues of the ATP binding site that may be useful in the design of potential competitive ATP inhibitors of human LMTK3.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82–84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88–90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Using 3dRPC for RNA-protein complex structure prediction.

3dRPC is a computational method designed for three-dimensional RNA–protein complex structure prediction. Starting from a protein structure and a RNA structure, 3dRPC first generates presumptive complex structures by RPDOCK and then evaluates the structures by RPRANK. RPDOCK is an FFT-based docking algorithm that takes features of RNA–protein interactions into consideration, and RPRANK is a knowledge-based potential using root mean square deviation as a measure. Here we give a detailed description of the usage of 3dRPC. The source code is available at http://biophy.hust.edu.cn/3dRPC.html.

SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks.

Predicting one-dimensional structure properties has played an important role to improve prediction of protein three-dimensional structures and functions. The most commonly predicted properties are secondary structure and accessible surface area (ASA) representing local and nonlocal structural characteristics, respectively. Secondary structure prediction is further complemented by prediction of continuous main-chain torsional angles. Here we describe a newly developed method SPIDER2 that utilizes three iterations of deep learning neural networks to improve the prediction accuracy of several structural properties simultaneously. For an independent test set of 1199 proteins SPIDER2 achieves 82 % accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively. The method provides state-of-the-art, all-in-one accurate prediction of local structure and solvent accessible surface area. The method is implemented, as a webserver along with a standalone package that are available in our website: http://sparks-lab.org .

Identification, characterization and bioactivity of tumor necrosis factor (TNF)-related apoptosis-inducing ligand from Equus caballus

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily and plays multifunctional roles in the immune system. In the present study, a homolog of TRAIL from the Mongolian horse (named ecTRAIL) was identified and characterized. The 870-bp open reading frame encodes a polypeptide of 289 amino acid residues with a predicted molecular weight of 33.47 kDa and pI of 8.47. The genomic structure of ecTRAIL shares a five-exon/six-intron arrangement similar to its orthologs. Multiple alignments show that ecTRAIL is a type II transmembrane protein with a typical transmembrane region, three conserved cysteine residues (Cys56, Cys77, Cys238) and a TNF family signature sequence ([LV]-x-[LIVM]-x(3)-G-[LIVMF]-Y-[LIVMFY](2)-x(2)-[QEKHL]-[LIVMGT]-x-[LIVMFY]). Three-dimensional structure prediction based on the same template revealed that the positional arrangement of the key amino acid residues, Cys238 and Cys230 in ecTRAIL and human TRAIL, respectively, is significantly conserved. Evolutionary analysis suggests that ecTRAIL is most closely related to its ortholog from pigs, with an identity of 83.99%. The solubilizing small ubiquitin-related modifier (SUMO) tag fused recombinant protein SUMO-ecsTRAIL was successfully expressed in E. coli and exhibited binding activity and cytotoxicity to HeLa cells in a cross-species manner in vitro. These results provide a better understanding of TRAILs in mammals and indicate that ecTRAIL may play an important role in the immune response in horses.