Yanzhi Guo

Probing Immobilization Mechanism of alpha-chymotrypsin onto Carbon Nanotube in Organic Media by Molecular Dynamics Simulation

The enzyme immobilization has been adopted to enhance the activity and stability of enzymes in non-aqueous enzymatic catalysis. However, the activation and stabilization mechanism has been poorly understood on experiments. Thus, we used molecular dynamics simulation to study the adsorption of α-chymotrypsin (α-ChT) on carbon nanotube (CNT) in aqueous solution and heptane media. The results indicate that α-ChT has stronger affinity with CNT in aqueous solution than in heptane media, as confirmed by more adsorption atoms, larger contact area and higher binding free energies. Although the immobilization causes significant structure deviations from the crystal one, no significant changes in secondary structure of the enzyme upon adsorption are observed in the two media. Different from aqueous solution, the stabilization effects on some local regions far from the surface of CNT were observed in heptane media, in particular for S1 pocket, which should contribute to the preservation of specificity reported by experiments. Also, CNT displays to some extent stabilization role in retaining the catalytic H-bond network of the active site in heptane media, which should be associated with the enhanced activity of enzymes. The observations from the work can provide valuable information for improving the catalytic properties of enzymes in non-aqueous media.

A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity

Protein–protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs.

Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles.

Backgroud Type III secretion systems (T3SSs) are central to the pathogenesis and specifically deliver their secreted substrates (type III secreted proteins, T3SPs) into host cells. Since T3SPs play a crucial role in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T3SSs. This study reports a novel and effective method for identifying the distinctive residues which are conserved different from other SPs for T3SPs prediction. Moreover, the importance of several sequence features was evaluated and further, a promising prediction model was constructed. Results Based on the conservation profiles constructed by a position-specific scoring matrix (PSSM), 52 distinctive residues were identified. To our knowledge, this is the first attempt to identify the distinct residues of T3SPs. Of the 52 distinct residues, the first 30 amino acid residues are all included, which is consistent with previous studies reporting that the secretion signal generally occurs within the first 30 residue positions. However, the remaining 22 positions span residues 30–100 were also proven by our method to contain important signal information for T3SP secretion because the translocation of many effectors also depends on the chaperone-binding residues that follow the secretion signal. For further feature optimisation and compression, permutation importance analysis was conducted to select 62 optimal sequence features. A prediction model across 16 species was developed using random forest to classify T3SPs and non-T3 SPs, with high receiver operating curve of 0.93 in the 10-fold cross validation and an accuracy of 94.29% for the test set. Moreover, when performing on a common independent dataset, the results demonstrate that our method outperforms all the others published to date. Finally, the novel, experimentally confirmed T3 effectors were used to further demonstrate the model’s correct application. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/T3SPs.zip.

A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach

The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein–ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients (Rp and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.

In silico identification of Gram-negative bacterial secreted proteins from primary sequence

In this study, we focus on different types of Gram-negative bacterial secreted proteins, and try to analyze the relationships and differences among them. Through an extensive literature search, 1612 secreted proteins have been collected as a standard data set from three data sources, including Swiss-Prot, TrEMBL and RefSeq. To explore the relationships among different types of secreted proteins, we model this data set as a sequence similarity network. Finally, a multi-classifier named SecretP is proposed to distinguish different types of secreted proteins, and yields a high total sensitivity of 90.12% for the test set. When performed on another public independent dataset for further evaluation, a promising prediction result is obtained. Predictions can be implemented freely online at http://cic.scu.edu.cn/bioinformatics/secretPv2_1/index.htm.

Effective discrimination between biologically relevant contacts and crystal packing contacts using new determinants

In the structural models determined by X‐ray crystallography, contacts between molecules can be divided into two categories: biologically relevant contacts and crystal packing contacts. With the growth in the number and quality of available large crystal packing contacts structures, distinguishing crystal packing contacts from biologically relevant contacts remains a difficult task, which can lead to wrong interpretation of structural models. In this study, we performed a systematic analysis on the biologically relevant contacts and crystal packing contacts. The analysis results reveal that biologically contacts are more tightly packed than crystal packing contacts. This property of biologically contacts may contribute to the formation of their interfacial core region. Meanwhile, the differences between the core and surface region of biologically contacts in amino acid composition and evolutionary measure are more dramatic than crystal packing contacts and these differences appear to be useful in distinguishing these two categories of contacts. On the basis of the features derived from our analysis, we developed a random forest model to classify biological relevant contacts and crystal packing contacts. Our method can achieve a high receiver operating curve of 0.923 in the 5‐fold cross‐validation and accuracies of 91.4% and 91.7% for two different test sets. Moreover, in a comparison study, our model outperforms other existing methods, such as DiMoVo, Pita, Pisa, and Eppic. We believe that this study will provide useful help in the validation of oligomeric proteins and protein complexes. The model and all data used in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/bio‐cry.zip. Proteins 2014; 82:3090–3100.

Unfolding mechanism of thrombin-binding aptamer revealed by molecular dynamics simulation and Markov State Model.

Thrombin-binding aptamer (TBA) with the sequence 5′GGTTGGTGTGGTTGG3′ could fold into G-quadruplex, which correlates with functionally important genomic regionsis. However, unfolding mechanism involved in the structural stability of G-quadruplex has not been satisfactorily elucidated on experiments so far. Herein, we studied the unfolding pathway of TBA by a combination of molecular dynamics simulation (MD) and Markov State Model (MSM). Our results revealed that the unfolding of TBA is not a simple two-state process but proceeds along multiple pathways with multistate intermediates. One high flux confirms some observations from NMR experiment. Another high flux exhibits a different and simpler unfolding pathway with less intermediates. Two important intermediate states were identified. One is similar to the G-triplex reported in the folding of G-quadruplex, but lack of H-bonding between guanines in the upper plane. More importantly, another intermediate state acting as a connector to link the folding region and the unfolding one, was the first time identified, which exhibits higher population and stability than the G-triplex-like intermediate. These results will provide valuable information for extending our understanding the folding landscape of G-quadruplex formation.

Position-specific prediction of methylation sites from sequence conservation based on information theory

Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.

A structural dissection of large protein-protein crystal packing contacts

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.