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Protein-protein interactions (PPIs) play a crucial role in various biological processes. To better comprehend the pathogenesis and treatments of various diseases, it is necessary to learn the detail of these interactions. However, the current experimental method still has many false-positive and false-negative problems. Computational prediction of protein-protein interaction has become a more important prediction method which can overcome the obstacles of the experimental method. In this work, we proposed a novel computational domain-based method for PPI prediction, and an SVM model for the prediction was built based on the physicochemical property of the domain. The outcomes of SVM and the domain-domain score were used to construct the prediction model for protein-protein interaction. The predicted results demonstrated the domain-based research can enhance the ability to predict protein interactions.

Background Mismatch negativity (MMN) is a typical event-related potential component reflecting pre-attentive processing. MMN impairment, especially reduced duration-related MMN (dMMN), has been suggested as a potential predictive biomarker for the onset of schizophrenia. Objective This study attempts to manipulate specific MMN activities using advanced neuroimaging-guided intermittent theta-burst stimulations (iTBS), which will be helpful to uncover the sources of MMN generation and contribute to the development of new clinical treatments. Methods Twenty-four healthy volunteers were recruited and participated two-session modulations consisting of active and sham iTBS. ITBS was precisely delivered over individual right posterior superior temporal cortex (pSTG). Before and after each iTBS session, two MMN components evoked by duration and frequency deviants were quantified respectively. Results A significant interaction of time and iTBS was observed on dMMN amplitudes, but not frequency-related MMN amplitudes. dMMN only decreased after active precise iTBS intervention, but did not after sham iTBS. The post effect of iTBS on dMMN was found in 16 of 20 subjects, suggesting a robust effect even at individual level. Furthermore, sLORETA analysis showed that the lateralization of STG activation was reversed after the active iTBS. Conclusions We applied a precise strategy for neuroimaging-guided iTBS modulation over the right pSTG, which is promising in selectively modulating MMN for specific deviants.

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model.

The hard and corrosion resistant coatings of Mo2NiB2 cermet were prepared by the laser cladding technique. The influences of the Mo:B ratio and the laser scanning speed on the microstructure and property of the Mo2NiB2 cermet coatings were investigated. The results showed that the laser scanning speed of 1.5 mm/s and the Mo:B ratio of 1 were more beneficial to the formation of Mo2NiB2 cermet than 2.0 mm/s and 0.8, 1.2, respectively. The amount of the Mo2NiB2 ceramic phases were decreased from the top layer to the bottom layer of the coating. The changes of microstructure and composition led to the changes of hardness and corrosion resistance of the Mo2NiB2 cermet coatings. The coating prepared at the Mo:B ratio of 1 and the scanning speed of 1.5 mm/s possessed the highest hardness, and the hardness gradually decreased from the top layer to the bottom layer of the coating. The formation of Mo2NiB2 and {FeM} phases led to the enhanced corrosion resistance of the Mo2NiB2 cermet coatings, and the coating prepared at the Mo:B ratio of 0.8 possessed the best corrosion resistance and the minimum corrosion current.

WW domain proteins are usually regarded as simple models for understanding the folding mechanism of β-sheet. CC45 is an artificial protein that is capable of folding into the same structure as WW domain. In this article, the replica exchange molecular dynamics simulations are performed to investigate the folding mechanism of CC45. The analysis of thermal stability shows that β-hairpin 1 is more stable than β-hairpin 2 during the unfolding process. Free energy analysis shows that the unfolding of this protein substantially proceeds through solvating the smaller β-hairpin 2, followed by the unfolding of β-hairpin 1. We further propose the unfolding process of CC45 and the folding mechanism of two β-hairpins. These results are similar to the previous folding studies of formin binding protein 28 (FBP28). Compared with FBP28, it is found that CC45 has more aromatic residues in N-terminal loop, and these residues contact with C-terminal loop to form the outer hydrophobic core, which increases the stability of C...

Vinculin is an important protein for the linkage between adhesion molecules and the actin cytoskeleton. The activation mechanism of vinculin is still controversial. In order to provide useful information for a better understanding of its activation, we analyze the motion mode of vinculin with elastic network model in this work. The results show that, to some extent, the five domains will present structural rigidity in the motion process. The differences between the structure fluctuations of these domains are significant. When vinculin interacted with other partners, the central long alpha-helix of the first domain becomes bent. This bending deformation can weaken the interaction between the first domain and the tail domain. This motion mode of the first domain is in good agreement with the information extracted from some realistic complex structures. With the aid of the anisotropy elastic network mode, we analyze the motion directions of these domains. The fourth domain has a rotational motion. This rotation is favorable for the releasing of the tail domain from the pincer-like clamp, which is formed by the first and the third domain. All these motion modes are an inherent feature of the structure, and these modes mainly depend on the topology character of the structure.

Zinc-fingers play crucial roles in regulating gene expression and mediating protein-protein interactions. In this article, two different proteins (Sp1f2 and FSD-1) are investigated using the Gaussian network model and anisotropy elastic network model. By using these simple coarse-grained methods, we analyze the structural stabilization and establish the unfolding pathway of the two different proteins, in good agreement with related experimental and molecular dynamics simulation data. From the analysis, it is also found that the folding process of the zinc-finger motif is predominated by several factors. Both the zinc ion and C-terminal loop affect the folding pathway of the zinc-finger motif. Knowledge about the stability and folding behavior of zinc-fingers may help in understanding the folding mechanisms of the zinc-finger motif and in designing new zinc-fingers. Meanwhile, these simple coarse-grained analyses can be used as a general and quick method for mechanistic studies of metalloproteins.

Residue networks are constructed by defining the residues as the vertices and atom contacts between them as the edges. The residue network of a protein complex is divided into two types of networks, i.e. the hydrophobic and the hydrophilic residue networks. By analyzing the network parameters, it is found that the correct binding complex conformations are of both higher sum of the interface degree values and lower characteristic path length than those incorrect ones. These features reflect that the correct binding complex conformations have better geometric and/or residue type complementarity, and the correct binding modes are very important for preserving the characteristic path lengths of native protein complexes. In addition, two scoring terms are proposed based on the network parameters, in which the characteristics of the entire complex shape and residue type complementarity are taken into account. These network-based scoring terms have also been used in conjunction with other scoring terms, and the new multi-term scoring HPNCscore is devised in this work. It can improve the discrimination of the combined scoring function of RosettaDock more than 12%. This work might enhance our knowledge of the mechanisms of protein-protein interactions and recognition.

Protein–protein interactions (PPIs) are vital to a number of biological processes. With computational methods, plenty of domain information can help us to predict and assess PPIs. In this study, we proposed a domain-based approach for the prediction of human PPIs based on the interactions between the proteins and the domains. In this method, an optimizing model was built with the information from InterDom, 3did, DOMINE and Pfam databases. With this model, for 147 proteins in the integrin adhesome PPI network, 736 probable PPIs have been predicted, and the corresponding confidence probabilities of these PPIs were also calculated. It provides an opportunity to visualize the PPIs by using network graphs, which were constructed with Cytoscape, so that we can indicate underlying pathways possible.

A DNA tetrahedral nanostructure-based electrochemical biosensor was developed to detect avian influenza A (H7N9) virus through recognizing a fragment of the hemagglutinin gene sequence. The DNA tetrahedral probe was immobilized onto a gold electrode surface based on self-assembly between three thiolated nucleotide sequences and a longer nucleotide sequence containing complementary DNA to hybridize with the target single-stranded (ss)DNA. The captured target sequence was hybridized with a biotinylated-ssDNA oligonucleotide as a detection probe, and then avidin-horseradish peroxidase was introduced to produce an amperometric signal through the interaction with 3,3',5,5'-tetramethylbenzidine substrate. The target ssDNA was obtained by asymmetric polymerase chain reaction (PCR) of the cDNA template, reversely transcribed from the viral lysate of influenza A (H7N9) virus in throat swabs. The results showed that this electrochemical biosensor could specifically recognize the target DNA fragment of influenza A (H7N9) virus from other types of influenza viruses, such as influenza A (H1N1) and (H3N2) viruses, and even from single-base mismatches of oligonucleotides. Its detection limit could reach a magnitude of 100 fM for target nucleotide sequences. Moreover, the cycle number of the asymmetric PCR could be reduced below three with the electrochemical biosensor still distinguishing the target sequence from the negative control. To the best of our knowledge, this is the first report of the detection of target DNA from clinical samples using a tetrahedral DNA probe functionalized electrochemical biosensor. It displays that the DNA tetrahedra has a great potential application as a probe of the electrochemical biosensor to detect avian influenza A (H7N9) virus and other pathogens at the gene level, which will potentially aid the prevention and control of the disease caused by such pathogens.

For a more detailed description of the interaction between residues, this paper proposes an amino acid network model, which contains two types of weight—similar weight and dissimilar weight. The weight of the link is based on a self-consistent statistical contact potential between different types of amino acids. In this model, we can get a more reasonable representation of the distance between residues. Furthermore, with the network parameter, average shortest path length, we can get a more accurate reflection of the molecular size. This amino acid network is a “small-world” network, and the network parameter is sensitive to the conformation change of protein. For some disease-related proteins, the highly central residues of the amino acid network are highly correlated with the hot spots. In the compound with the related drug, these residues either interacted directly with the drug or with the residue which is in contact with the drug.

This paper develops a decision-making system based on the BP Neural Network to navigate a robot in an unknown environment. Based on the neural network model, the robot can move out of specific mazes successfully through adjusting its direction and speed continuously. A BP neural network, which includes three input nodes and nine output nodes, are designed for the navigation system. The information of the surrounding environment is returned by six ultrasonic sensors on the front and bilateral sides of the robot. After thousands of training, the robot learns the navigation knowledge successfully from the samples, and move out of the mazes autonomously. The performance of the robot is validated with the simulation results and two physical experiments. The results show that the robot could navigate autonomously in unknown environments.

The signal recognition particle (SRP) and its receptors (SR) mediate the cotranslational targeting of the membrane and secretory proteins in all cells. In Escherichia coli, SRP is composed of the Ffh protein and the 4.5S SRP RNA. Ffh is a multidomain protein comprising a methionine-rich (M) domain, a helical N domain, and a Ras-like guanine triphosphatase (GTPase) (G) domain. The N and G domains are commonly referred to as one structural unit, the NG domain. In this article, the complex structure of SRP and SR is investigated with the Gaussian network model (GNM) and anisotropic network model (ANM). GNM provides the information of structure stability. It is found that the intermolecular interactions between SRP and SR can obviously decrease the fluctuation of NG domains. Nevertheless, the large structural rearrangement will take place during the cotranslational protein targeting cycle. Hence, the moving directions of fluctuation regions are further ascertained by using cross-correlation analysis and the ANM. The NG domain of Ffh undergoes a clockwise rotation around the GM linker and the M domain of Ffh shows an opposite direction to the NG domain. These functional movements will facilitate the SRP structure to transform into the free form and the sequence-bound form. These simple coarse-grained analyses can be used as a general and quick method for the mechanism studies of protein assembly and supramolecular systems.

Molecular docking is an important method for the research of protein-protein interaction and recognition. A protein can be considered as a network when the residues are treated as its nodes. With the contact energy between residues as link weight, a weighted residue network is constructed in this paper. Two weighted parameters (strength and weighted average nearest neighbors' degree) are introduced into this model at the same time. The stability of a protein is characterized by its strength. The global topological properties of the protein-protein complex are reflected by the weighted average nearest neighbors' degree. Based on this weighted network model and these two parameters, a new docking scoring function is proposed in this paper. The scoring and ranking for 42 systems' bound and unbounded docking results are performed with this new scoring function. Comparing the results obtained from this new scoring function with that from the pair potentials scoring function, we found that this new scoring function has a similar performance to the pair potentials on some items, and this new scoring function can get a better success rate. The calculation of this new scoring function is easy, and the result of its scoring and ranking is acceptable. This work can help us better understand the mechanisms of protein-protein interactions and recognition.

Based on a modified amino acid network model, the dynamic simulation trajectory of a small protein (3CI2) is analyzed. The ration between different type of links is calculated, and the change of the long-rang link is calculated at the same time. With the conformation cluster analysis, the transition state can be got, and the network parameter betweenness of this state is analyzed.

The protein folding problem is one of the fundamental and important questions in molecular biology. However, the all-atom molecular dynamics studies of protein folding and unfolding are still computationally expensive and severely limited by the time scale of simulation. In this paper, a simple and fast protein unfolding method is proposed based on the conformational stability analyses and structure modeling. In this method, two structure-based conditions are considered to identify the unstable regions of proteins during the unfolding processes. The protein unfolding trajectories are mimicked through iterative structure modeling according to conformational stability analyses. Two proteins, chymotrypsin inhibitor 2 (CI2) and $\ensuremath{\alpha}$ -spectrin SH3 domain (SH3) were simulated by this method. Their unfolding pathways are consistent with the previous molecular dynamics simulations. Furthermore, the transition states of the two proteins were identified in unfolding processes and the theoretical $\ensuremath{\Phi}$ values of these transition states showed significant correlations with the experimental data (the correlation coefficients are g0.8). The results indicate that this method is effective in studying protein unfolding. Moreover, we analyzed and discussed the influence of parameters on the unfolding simulation. This simple coarse-grained model may provide a general and fast approach for the mechanism studies of protein folding.

Based on a self-consistent contact energy, an amino acid network model is proposed to improve the shortcoming of the model constructed early. The model used in this paper contains two types of weight - similar weight and dissimilar weight. The definition methods of the distance between nodes are compared and the small world character of the amino acid network is investigated. Finally, the network parameter - average shortest path length is used to reflect the conformation change on an unfolding pathway.

In this work, the motion modes of the talin rod domain is investigated with the Gaussian network model (GNM). The slowest mode of the structure shows that the two free terminal move in a negatively correlated way, on an opposite direction. It is thought that this conformational motion mode is the nature of the topology structure of talin rod domain. The peaks of the fast modes correspond to some residues, which are important to the stability of the structure, and these amino acids may have interactions with integrin as key residues.

The three-dimensional structure of a protein can be treated as a complex network composed of amino acids, and the network properties can help us to understand the relationship between structure and function. Since the amino acid network of a protein is formed in the process of protein folding, it is difficult for general network models to explain its evolving mechanism. Based on the perspective of protein folding, we propose an evolving model for amino acid networks. In our model, the evolution starts from the amino acid sequence of a native protein and it is guided by two generic assumptions: i.e., the neighbor preferential rule and the energy preferential rule. We find that the neighbor preferential rule predominates the general network properties and the energy preferential rule predominates the specific biological structure characteristics. Applied to native proteins, our model mimics the features of amino acid networks well.

Protein-protein complex, composed of hydrophobic and hydrophilic residues, can be divided into hydrophobic and hydrophilic amino acid network structures respectively. In this paper, we are interested in analyzing these two different types of networks and find that these networks are of small-world properties. Due to the characteristic complementarity of the complex interfaces, protein-protein docking can be viewed as a particular network rewiring. These networks of correct docked complex conformations have much more increase of the degree values and decay of the clustering coefficients than those of the incorrect ones. Therefore, two scoring terms based on the network parameters are proposed, in which the geometric complementarity, hydrophobic-hydrophobic and polar-polar interactions are taken into account. Compared with a two-term energy function, a simple scoring function HPNet which includes the two network-based scoring terms shows advantages in two aspects, not relying on energy considerations and better discrimination. Furthermore, combing the network-based scoring terms with some other energy terms, a new multi-term scoring function HPNet-combine can also make some improvements to the scoring function of RosettaDock.

Identifying protein interface is crucial for the prediction of protein-protein interactions and for protein functional classification. In this work, the protein structure is modeled as an undirected graph with the conservation amino- acid residues the vertices and all atom contacts between them the edges. We find that the conservation residue networks are characterized by intermediate values of clustering coefficient and characteristic path length, which are the typical property of small-world networks. The residues on the protein interfaces typically have higher degree and lower clustering coefficient values than that of the surface residues. Additionally, it is detected that the spatial clustering of the conservation residues is a general phenomenon, consistent with the cooperative nature of residues in determining the structure and function. These results indicate that the conservation residue network propensities can give us some new parameters in protein-protein interface prediction.

In this work, the mechanism of domain movements of glutamine-binding protein (GlnBP), especially the influence of the ligand on GlnBP dynamic behavior is investigated with the aid of a Gaussian network model (GNM) and an anisotropy elastic network model. The results show that the "open-closed" transition mainly appears as the large movement of the small domain, especially the top region including two alpha-helices and two beta-strands. The slowest mode of each three forms of GlnBP--ligand-free open, ligand-bound closed, and ligand-free closed GlnBP--shows that the open-closed motion of the two domains has a common hinge axis centered on Lys-87 and Gln-183. Accompanying the conformational transition, the residues within both large and small domains move in a highly coupled way. The peaks of the fast modes correspond to residues that were thought, in the GNM, to be important for the stability of the protein, and these residues may be involved in the interactions with the membrane-bound components. With the contacts between the large domain and the small domain increasing, the ability of the "open-closed" motion is decreased. All the results agree well with those of molecular dynamics simulations, and it is thought that the open-closed conformation transition is the nature of the topology structure of GlnBP. Also, the influence of the ligand on GlnBP is studied with a modified GNM method. The results obtained show that the ligand does not influence the closed-to-open transition tendency.

An approach to protein design is proposed based on the relative entropy and a reduced amino acid alphabet. In this approach, the relative entropy is used as a minimization object function. The method has been tested on a real protein's off-lattice model successfully, and the results are similar to those obtained from other design studies. It can be applied as a uniform frame for both folding and inverse folding of protein. An iterative calculation method of the ensemble average of the contact strength is proposed at the same time.