Construction and application of the weighted amino acid network based on energy

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Double Mutant of Chymotrypsin Inhibitor 2 Stabilized through Increased Conformational Entropy

The conformational heterogeneity of a folded protein can affect not only its function but also stability and folding. We recently discovered and characterized a stabilized double mutant (L49I/I57V) of the protein CI2 and showed that state-of-the-art prediction methods could not predict the increased stability relative to the wild-type protein. Here, we have examined whether changed native-state dynamics, and resulting entropy changes, can explain the stability changes in the double mutant protein, as well as the two single mutant forms. We have combined NMR relaxation measurements of the ps-ns dynamics of amide groups in the backbone and the methyl groups in the side chains with molecular dynamics simulations to quantify the native-state dynamics. The NMR experiments reveal that the mutations have different effects on the conformational flexibility of CI2: a reduction in conformational dynamics (and entropy estimated from this) of the native state of the L49I variant correlates with its decreased stability, while increased dynamics of the I57V and L49I/I57V variants correlates with their increased stability. These findings suggest that explicitly accounting for changes in native-state entropy might be needed to improve the predictions of the effect of mutations on protein stability.

Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition

SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.

Distinguishing Enzymes and Non-enzymes Based on Structural Information with an Alignment Free Approach

Background:

Knowledge of protein functions is very crucial for the understanding of biological processes. Experimental methods for protein function prediction are powerless to treat the growing amount of protein sequence and structure data.

Objective:

To develop some computational techniques for the protein function prediction.

Method:

Based on the residue interaction network features and the motion mode information, an SVM model was constructed and used as the predictor. The role of these features was analyzed and some interesting results were obtained.

Results:

An alignment-free method for the classification of enzyme and non-enzyme is developed in this work. There is not any single feature that occupies a dominant position in the prediction process. The topological and the information-theoretic residue interaction network features have a better performance. The combination of the fast mode and the slow mode can get a better explanation for the classification result.

Conclusion:

The method proposed in this paper can act as a classifier for the enzymes and nonenzymes.

Identification of novel inhibitor of protein tyrosine phosphatases delta: structure-based pharmacophore modeling, virtual screening, flexible docking, molecular dynamics simulation, and post-molecular dynamics analysis

Owing to their unique functions in regulating the synapse activity of protein tyrosine phosphatases delta (PTPδ) that has drawn special attention for developing drugs to autism spectrum disorders (ASDs). In this study, the PTPδ pharmacophore was first established by the structure-based pharmacophore method. Subsequently, 10 compounds contented Lipinski's rule of five was acquired by the virtual screening of the PTPδ pharmacophore against ZINC and PubChem databases. Then, the 10 identified molecules were discovered that had better binding affinity than a known PTPδ inhibitors compound SCHEMBL16375396. Two compounds SCHEMBL16375408 and ZINC19796658 with high binding score, low toxicity were gained. They were observed by docking analysis and molecular dynamics simulations that the novel potential inhibitors not only possessed the same function as SCHEMBL16375396 did in inhibiting PTPδ, but also had more favorable conformation to bind with the catalytic active regions. This study provides a new method for identify PTPδ inhibitor for the treatment of ASDs disease.Communicated by Ramaswamy H. Sarma.

Prediction of interface residue based on the features of residue interaction network

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.

Communities in the iron superoxide dismutase amino acid network

Amino acid networks (AANs) analysis is a new way to reveal the relationship between protein structure and function. We constructed six different types of AANs based on iron superoxide dismutase (Fe-SOD) three-dimensional structure information. These Fe-SOD AANs have clear community structures when they were modularized by different methods. Especially, detected communities are related to Fe-SOD secondary structures. Regular structures show better correlations with detected communities than irregular structures, and loops weaken these correlations, which suggest that secondary structure is the unit element in Fe-SOD folding process. In addition, a comparative analysis of mesophilic and thermophilic Fe-SOD AANs' communities revealed that thermostable Fe-SOD AANs had more highly associated community structures than mesophilic one. Thermophilic Fe-SOD AANs also had more high similarity between communities and secondary structures than mesophilic Fe-SOD AANs. The communities in Fe-SOD AANs show that dense interactions in modules can help to stabilize thermophilic Fe-SOD.

Amino acid contact energy networks impact protein structure and evolution

One of the most challenging tasks in structural proteomics is to understand the relationship between protein structure, biological function, and evolution. An understanding of amino acid networks based on protein topology has an important role in the study of this relationship; however, the relationship between network parameters underlying protein topology with structural properties or evolutionary rate is still unknown. To investigate this further, we modeled the three dimensional structure of proteins as amino acid contact energy networks (AACENs) with nodes represented as amino acid residues and edges established according to environment-dependent residue-residue contact energies. Five other types of networks were also constructed to investigate their topological parameters and compare their effect on protein structure and evolution: (1) a random contact network (RCN), (2) a rewiring network with the same degree of distribution as AACEN (RNDD), (3) long-range contact energy networks with and without the backbone connectivity (LCEN_BBs and LCENs), and (4) short range contact energy networks (SCENs). The results indicated that the long-range link percentage and the network clustering coefficient showed a significantly positive and negative correlation, respectively, with protein secondary structure density. In addition, the long-range link percentage and network diameter had a significantly positive and negative correlation, respectively, with evolutionary rate. According to our knowledge, this is the first study to identify the potential role of long-range links and network diameter in protein evolution.

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

Allosteric regulation of the Hsp90 dynamics and stability by client recruiter cochaperones: protein structure network modeling

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.

Analysis of Unweighted Amino Acids Network

The analysis of amino acids network is very important to studying the various physicochemical properties of amino acids. In this paper we consider the amino acid network based on mutation of the codons. To analyze the relative importance of the amino acids we have discussed different measures of centrality. The measure of centrality is a powerful tool of graph theory for ranking the vertices and analysis of biological network. We have also investigated the correlation coefficients between various measures of centrality. Also we have discussed clustering coefficient as well as average clustering coefficient of the network. Finally we have discussed the degree of distribution as well as skewness.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

A modified amino acid network model contains similar and dissimilar weight

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.

Local motifs in proteins combine to generate global functional moves

Literature on the topological properties of folded proteins that has emerged as a field in its own right in the past decade is reviewed. Physics-based construction of coarse-grained models of proteins from knowledge of all-atom coordinates of the average structure is discussed. Once network is thus obtained with the node and link information, local motifs provide plethora of information on protein function. The hierarchical structure of the proteins manifested in the interrelations of local motifs is emphasized. Motifs are also related to modularity of the structure, and they quantify shifts in the landscapes upon conformational changes induced by, e.g. ligand binding. Redundancy emerges as a balance between local and global network descriptors and is related to the collectivity of the protein motions. Introducing weight on links followed by sequential removal of least cohesive contacts allows interactions in proteins to be represented as the superposition of essential and redundant sets. Lack of the former makes the network non-functional, while the latter ensures robust functioning under a wide range of perturbation scenarios.

Scoring function based on weighted residue network

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.

Computational modeling of allosteric communication reveals organizing principles of mutation-induced signaling in ABL and EGFR kinases

The emerging structural information about allosteric kinase complexes and the growing number of allosteric inhibitors call for a systematic strategy to delineate and classify mechanisms of allosteric regulation and long-range communication that control kinase activity. In this work, we have investigated mechanistic aspects of long-range communications in ABL and EGFR kinases based on the results of multiscale simulations of regulatory complexes and computational modeling of signal propagation in proteins. These approaches have been systematically employed to elucidate organizing molecular principles of allosteric signaling in the ABL and EGFR multi-domain regulatory complexes and analyze allosteric signatures of the gate-keeper cancer mutations. We have presented evidence that mechanisms of allosteric activation may have universally evolved in the ABL and EGFR regulatory complexes as a product of a functional cross-talk between the organizing αF-helix and conformationally adaptive αI-helix and αC-helix. These structural elements form a dynamic network of efficiently communicated clusters that may control the long-range interdomain coupling and allosteric activation. The results of this study have unveiled a unifying effect of the gate-keeper cancer mutations as catalysts of kinase activation, leading to the enhanced long-range communication among allosterically coupled segments and stabilization of the active kinase form. The results of this study can reconcile recent experimental studies of allosteric inhibition and long-range cooperativity between binding sites in protein kinases. The presented study offers a novel molecular insight into mechanistic aspects of allosteric kinase signaling and provides a quantitative picture of activation mechanisms in protein kinases at the atomic level.

Despite recent progress in computational and experimental studies of dynamic regulation in protein kinases, a mechanistic understanding of long-range communication and mechanisms of mutation-induced signaling controlling kinase activity remains largely qualitative. In this study, we have performed a systematic modeling and analysis of allosteric activation in ABL and EGFR kinases at the increasing level of complexity - from catalytic domain to multi-domain regulatory complexes. The results of this study have revealed organizing structural and mechanistic principles of allosteric signaling in protein kinases. Although activation mechanisms in ABL and EGFR kinases have evolved through acquisition of structurally different regulatory complexes, we have found that long-range interdomain communication between common functional segments (αF-helix and αC-helix) may be important for allosteric activation. The results of study have revealed molecular signatures of activating cancer mutations and have shed the light on general mechanistic aspects of mutation-induced signaling in protein kinases. An advanced understanding and further characterization of molecular signatures of kinase mutations may aid in a better rationalization of mutational effects on clinical outcomes and facilitate molecular-based therapeutic strategies to combat kinase mutation-dependent tumorigenesis.

Computational model for protein unfolding simulation

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 α -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 Φ values of these transition states showed significant correlations with the experimental data (the correlation coefficients are >0.8). 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.

Stability and folding behavior analysis of zinc-finger using simple models

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.

Frameworks for understanding long-range intra-protein communication

The phenomenon of intra-protein communication is fundamental to such processes as allostery and signaling, yet comparatively little is understood about its physical origins despite notable progress in recent years. This review introduces contemporary but distinct frameworks for understanding intra-protein communication by presenting both the ideas behind them and a discussion of their successes and shortcomings. The first framework holds that intra-protein communication is accomplished by the sequential mechanical linkage of residues spanning a gap between distal sites. According to the second framework, proteins are best viewed as ensembles of distinct structural microstates, the dynamical and thermodynamic properties of which contribute to the experimentally observable macroscale properties. Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying intra-protein communication, and the insights into both frameworks it provides are presented through a discussion of numerous examples from the literature. Distinct from mechanical and thermodynamic considerations of intra-protein communication are recently applied graph and network theoretic analyses. These computational methods reduce complex three dimensional protein architectures to simple maps comprised of nodes (residues) connected by edges (inter-residue "interactions"). Analysis of these graphs yields a characterization of the protein's topology and network characteristics. These methods have shown proteins to be "small world" networks with moderately high local residue connectivities existing concurrently with a small but significant number of long range connectivities. However, experimental studies of the tantalizing idea that these putative long range interaction pathways facilitate one or several macroscopic protein characteristics are unfortunately lacking at present. This review concludes by comparing and contrasting the presented frameworks and methodologies for studying intra-protein communication and suggests a manner in which they can be brought to bear simultaneously to further enhance our understanding of this important fundamental phenomenon.

Evolving model of amino acid networks

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.