Prediction of the multimeric assembly of staphylococcal enterotoxin A with cell-surface protein receptors

Staphylococcal enterotoxin A (SEA) cross-links two class II major histocompatibility complex (MHC) molecules and forms a multimeric assembly with T-cell receptors (TcRs). The X-ray crystal structure of SEA has been solved, yet details describing molecular recognition and association remain unclear. We present a structural model for the interactions of SEA with cell-surface proteins. Molecular docking calculations predicting SEA association with the class II MHC molecule HLA-DR1 were performed by using a rigid-body docking method. Docked orientations were evaluated by a Poisson-Boltzmann model for the electrostatic free energy of binding and the hydrophobic effect calculated from molecular surface areas. We found that the best-scoring SEA conformers for the DR1alpha interface display a binding mode similar to that determined crystallographically for staphylococcal enterotoxin B bound to HLA-DR1. For the zinc-binding site of SEA, docking DR1beta yielded several orientations exhibiting tetrahedral-like coordination geometries. Combining the two interfaces, tetramers were modeled by docking an alphabeta TcR with trimolecular complexes DR1beta-SEA-DR1alpha and SEA-betaDR1alpha-SEA. Our results indicate that the complex DR1beta-SEA-DR1alpha provides a more favorable assembly for the engagement of TcRs, forming SEA molecular contacts that are in accord with reported mutagenesis studies. In contrast, the cooperative association of two SEA molecules on a single DR1 molecule sterically inhibits interactions with TcRs. We suggest that signal transduction stimulated by SEA through large-scale assembly is limited to four or five TcR-(DR1beta-SEA-DR1alpha) tetramers and requires the dimerization of class II MHC molecules, while TcR dimerization is unlikely.     

用广义选择性神经网络集成预测MHC-II分子结合肽

MHC II类分子结合肽的预测对于免疫研究和疫苗设计非常重要,然而其结合肽长度的可变性等原因使其预测变得极为困难,提出了一种基于广义选择性神经网络集成的MHC II分子结合肽预测算法,该算法是一种双层集成模型。第一层是用微分进化算法去生成初始神经网络集成池,第二层是从初始神经网络集成池中选择部分组成最终的神经网络集成。实验结果表明广义选择性神经网络集成比传统的选择性神经网络有更好的泛化性能。     

A Bayesian regression approach to the prediction of MHC-II binding affinity

Peptide-major histocompatibility complex (MHC) binding is an important prerequisite event and has immediate consequences to immune response. Those peptides binding to MHC molecules can activate the T-cell immunity, and they are useful for understanding the immune mechanism and developing vaccines for diseases. Recently, researchers are interested in making prediction about binding affinity instead of differentiating the peptides as binder or non-binder. In this paper, we use sparse Bayesian regression algorithm proposed by Tipping [M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. (2001)] to derive position-specific scoring matrices from allele-related peptides, and develop the models allowing for the prediction of MHC-II binding affinity. We explore the peptide length and peptide flanking residue length's impact on binding affinity, and incorporate these factors into our models to enhance prediction performance. When applied to the datasets from AntiJen database and IEDB database, our method produces better performances than several popular quantitative methods.     

Structure-based prediction of MHC-peptide association: algorithm comparison and application to cancer vaccine design

Peptide vaccination for cancer immunotherapy requires identification of peptide epitopes derived from antigenic proteins associated with the tumor. Such peptides can bind to MHC proteins (MHC molecules) on the tumor-cell surface, with the potential to initiate a host immune response against the tumor. Computer prediction of peptide epitopes can be based on known motifs for peptide sequences that bind to a certain MHC molecule, on algorithms using experimental data as a training set, or on structure-based approaches. We have developed an algorithm, which we refer to as PePSSI, for flexible structural prediction of peptide binding to MHC molecules. Here, we have applied this algorithm to identify peptide epitopes (of nine amino acids, the common length) from the sequence of the cancer-testis antigen KU-CT-1, based on the potential of these peptides to bind to the human MHC molecule HLA-A2. We compared the PePSSI predictions with those of other algorithms and found that several peptides predicted to be strong HLA-A2 binders by PePSSI were similarly predicted by another structure-based algorithm, PREDEP. The results show how structure-based prediction can identify potential peptide epitopes without known binding motifs and suggest that side chain orientation in binding peptides may be obtained using PePSSI.     

Analysis of the HLA class I associated peptide repertoire in a hepatocellular carcinoma cell line reveals tumor-specific peptides as putative targets for immunotherapy

HLA class I molecules present peptides on the cell surface to CD8(+) T cells. The repertoire of peptides that associate to class I molecules represents the cellular proteome. Therefore, cells expressing different proteomes could generate different class I-associated peptide repertoires. A large number of peptides have been sequenced from HLA class I alleles, mostly from lymphoid cells. On the other hand, T cell immunotherapy is a goal in the fight against cancer, but the identification of T cell epitopes is a laborious task. Proteomic techniques allow the definition of putative T cell epitopes by the identification of HLA natural ligands in tumor cells. In this study, we have compared the HLA class I-associated peptide repertoire from the hepatocellular carcinoma (HCC) cell line SK-Hep-1 with that previously described from lymphoid cells. The analysis of the peptide pool confirmed that, as expected, the peptides from SK-Hep-1 derive from proteins localized in the same compartments as in lymphoid cells. Within this pool, we have identified 12 HLA class I peptides derived from HCC-related proteins. This confirms that tumor cell lines could be a good source of tumor associated antigens to be used, together with MS, to define putative epitopes for cytotoxic T cells from cancer patients.     

Quantitative prediction of MHC-II peptide binding affinity using relevance vector machine

Peptide-MHC binding is an important prerequisite event and has immediate consequences to immune response. Those peptides binding to MHC molecules can activate the T-cell immunity, and they are useful for understanding the immune mechanism and developing vaccines for diseases. Accurate prediction of the binding between peptides and MHC-II molecules has long been a challenge in bioinformatics. Recently, instead of differentiating peptides as binder or non-binder, researchers are more interested in making predictions directly on peptide binding affinities. In this paper, we investigate the use of relevance vector machine to quantitatively predict the binding affinities between MHC-II molecules and peptides. In our scheme, a new encoding scheme is used to generate the input vectors, and then by using relevance vector machine we develop the prediction models on the basis of binding cores, which are recognized in an iterative self-consistent way. When applied to three MHC-II molecules DRB1*0101, DRB1*0401 and DRB1*1501, our method produces consistently better performance than several popular quantitative methods, in terms of cross-validated squared error, cross-validated correlation coefficient, and area under ROC curve. All evidences indicate that our method is an effective tool for MHC-II binding affinity prediction.     

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-A11 binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes.     

基于粗集的T细胞表位预测方法

T细胞表位预测技术对于减少实验合成重叠肽、研究病原体与机体作用的免疫机制以及深入理解T细胞介导的免疫特异性均有重要意义。为增强T细胞表位预测模型的可理解性,本文在通过肽的预处理构建出存储等长肽段的决策表之后,设计出了一种基于粗集的T细胞表位预测方法。该方法由基于信息熵的属性约简完备算法和基于锚点知识的属性值顺序约简改进算法共同组成。基于HLA-DR4（B10401）编码的MHCII类分子结合肽的实验数据表明,在预测精度与传统神经网络方法大致相当的基础上,本文方法可以提取出用于帮助专家理解MHC分子与抗原肽结合机理的产生式规则。     

Conformational and energy evaluations of novel peptides binding to dengue virus envelope protein

Effective novel peptide inhibitors which targeted the domain III of the dengue envelope (E) protein by blocking dengue virus (DENV) entry into target cells, were identified. The binding affinities of these peptides towards E-protein were evaluated by using a combination of docking and explicit solvent molecular dynamics (MD) simulation methods. The interactions of these complexes were further investigated by using the Molecular Mechanics-Poisson Boltzmann Surface Area (MMPBSA) and Molecular Mechanics Generalized Born Surface Area (MMGBSA) methods. Free energy calculations of the peptides interacting with the E-protein demonstrated that van der Waals (vdW) and electrostatic interactions were the main driving forces stabilizing the complexes. Interestingly, calculated binding free energies showed good agreement with the experimental dissociation constant (K_d) values. Our results also demonstrated that specific residues might play a crucial role in the effective binding interactions. Thus, this study has demonstrated that a combination of docking and molecular dynamics simulations can accelerate the identification process of peptides as potential inhibitors of dengue virus entry into host cells.     

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.     

Designing of multi-targeted molecules using combination of molecular screening and in silico drug cardiotoxicity prediction approaches

We have previously investigated and reported a set of phenol- and indole-based derivatives at the binding pockets of carbonic anhydrase isoenzymes using in silico and in vitro analyses. In this study, we extended our analysis to explore multi-targeted molecules from this set of compounds. Thus, 26 ligands are screened at the binding sites of 229 proteins from 5 main enzyme family classes using molecular docking algorithms. Derived docking scores are compared with reported results of ligands at carbonic anhydrase I and II isoenzymes. Results showed potency of multi-targeted drugs of a few compounds from investigated ligand set. These promising ligands are then tested in silico for their cardiotoxicity risks. Results of this work can be used to improve the desired effects of these compounds by molecular engineering studies. In addition these results may lead to further investigation of studied molecules by medicinal chemists to explore different therapeutic aims.     

VRDD: applying virtual reality visualization to protein docking and design

We have developed an interactive docking program called VRDD. It offers various modes of displaying molecules in an immersive, three-dimensional virtual reality (VR) environment. It allows a user to interactively perform molecular docking aided by automatic docking and side chain conformational search. Binding free energies are computed in real time, and the program enables the user to explore only clash-free orientations of a ligand. VRDD also supplies visual and auditory feedback during docking and side chain search, indicating the levels of atomic overlap and interaction energy. The stunning VR graphics immerse users in the scene and can maximally stimulate their design intuition. We have tested VRDD on three cases with increasing complexity: a nine-residue-long peptide bound to a major histocompatibility complex (MHC) molecule, barstar bound to barnase, and an antibody bound to a hemagglutinin. Without prior knowledge, combinations of hand-docking and automatic refinement led to accurate complex structures for the first two complexes. The third case, for which all automatic docking algorithms failed to identify the correct complex in a previous blind test, also failed for VRDD. Our results show that the combination of VR docking and automatic docking can make unique contributions to molecular modeling.     

Prediction of MHC II-binding peptides using rough set-based rule sets ensemble

Peptide binding to Major Histocompatibility Complex (MHC) is a prerequisite for any T cell-mediated immune response. Predicting which peptides can bind to a specific MHC molecule is indispensable to minimizing the number of peptides required to synthesize, to the development of vaccines and immunotherapy of cancer, and to aiding to understand the specificity of T-cell mediated immunity. At present, although predictions based on machine learning methods have good prediction performance, they cannot acquire understandable knowledge and prediction performance can be further improved. Thereupon, the Rule Sets ENsemble (RSEN) algorithm, which takes advantage of diverse attribute and attribute value reduction algorithms based on rough set (RS) theory, is proposed as the initial trial to acquire understandable rules along with enhancement of prediction performance. Finally, the RSEN is applied to predict the peptides that bind to HLA-DR4(B1^* 0401). Experimentation results show: (1) prepositional rules for predicting the peptides that bind to HLA-DR4 (B1^* 0401) are obtained; (2) compared with individual RS-based algorithms, the RSEN has a significant decrease (13%–38%) in prediction error rate; (3) compared with the Back-Propagation Neural Networks (BPNN), prediction error rate of the RSEN decreases by 4%–16%. The acquired rules have been applied to help experts make molecules modeling. An Zeng received the Ph.D. degree in computer applications technology from South China University of Technology in 2005. Nowadays she is a lecturer at the Faculty of Computer of Guangdong University of Technology. Her research interests are data mining, bioinformatics, neural networks, artificial intelligence, and computational immunology. In these areas she has published over 20 technical papers in various prestigious journals or conference proceedings. She is a member of the IEEE. Contact her at the Faculty of Computer, Guangdong Univ. of Technology, University Town, PanYu District, Guangzhou, 510006, P.R. China. Dan Pan received the Ph.D. degree in circuits and systems from South China University of Technology in 2001. He is a senior engineer in Guangdong Mobile Communication Co. Ltd at present. His research interests are data mining, machine learning, bioinformatics, and data warehousing, and applications of business modeling and software engineering to computer-aided business operations systems, especially in the telecom industry. In these areas he has published over 30 technical papers in refereed journals or conference proceedings. As a member of the International Association of Science and Technology for Development (IASTED) technical committee on artificial intelligence and expert systems, he served a number of conferences and publications. He is a member of the IEEE. Contact him at Guangdong Mobile Communication Co. Ltd., 208 Yuexiu South Rd., Guangzhou, 510100, P.R. China. Jian-bin He received the M.E. in computer science from South China University of Technology in 2002. He now is a data mining consultant at Teradata division of NCR (China), supporting telecom carriers to do data mining in data warehouses for market research. His research interests include statistical learning, semi-supervised learning, spectral clustering, multi-relational data mining and their application to social science. Contact him at NCR(China) Co. Ltd., Unit 2306, Tower B, Center Plaza, 161 Linhexi Road, Guangzhou, 510620, P.R. China.     

Designing new surfactant peptides for binding to carbon nanotubes via computational approaches

The non-covalent interaction between single-walled carbon nanotube and surfactant peptides makes them soluble in biological media to be used in nano-medicine, drug delivery and gene therapy. Pervious study has shown that two important parameters in binding peptides into nanotubes are hydrophobic effect and the number of aromatic amino acids. Ten surfactant peptides with the length of eight residue, including Lys, Trp, Tyr, Phe and Val, were designed to investigate the important parameters in binding peptides to a (6, 6) carbon nanotube. 500 ns MD simulation was performed for free surfactant peptides in water or near to a nanotube. Our results have indicated that the binding affinity of peptides to nanotube increases with the increase of aromatic residue content. Also, among aromatic residues, the peptides containing Trp residues have higher binding affinity to nanotube compared to the peptides with Phe or Tyr residue. Steric hindrance between bulky aromatic residues in peptide sequence has negative influence in binding peptide to nanotube, and in designing a surfactant peptide, the number and distance of aromatic residue and polarity of them should be taken into account. Our results also show that in docking peptides to nanotube, full-flexible docking leads to incorrect results.     

Supporting the deployment of ISO-based project management processes with agile metrics

Agile approaches are well perceived in software development companies. These approaches are not faced with traditional process models. This research analyses how by defining and monitoring a set of Agile metrics, Agile mature companies can be also conformant with the best practices proposed by the targeted ISO process reference models. Conformance has been identified with five Technical Management processes of the ISO/IEC/IEEE 12207 standard and with two activities of the Project Management process of the ISO/IEC TR 29110-5-1-2 standard. The findings may be of the interest of those Agile settings that need to work according to the process model established in the company.     

Identification of potential bivalent inhibitors from natural compounds for acetylcholinesterase through in silico screening using multiple pharmacophores

The symptomatic cure observed in the treatment of Alzheimer's disease (AD) by FDA approved drugs could possibly be due to their specificity against the active site of acetylcholinesterase (AChE) and not by targeting its pathogenicity. The AD pathogenicity involved in AChE protein is mainly due to amyloid beta peptide aggregation, which is triggered specifically by peripheral anionic site (PAS) of AChE. In the present study, a workflow has been developed for the identification and prioritization of potential compounds that could interact not only with the catalytic site but also with the PAS of AChE. To elucidate the essential structural elements of such inhibitors, pharmacophore models were constructed using PHASE, based on a set of fifteen best known AChE inhibitors. All these models on validation were further restricted to the best seven. These were transferred to PHASE database screening platform for screening 89,425 molecules deposited at the “ZINC natural product database”. Novel lead molecules retrieved were subsequently subjected to molecular docking and ADME profiling. A set of 12 compounds were identified with high pharmacophore fit values and good predicted biological activity scores. These compounds not only showed higher affinity for catalytic residues, but also for Trp86 and Trp286, which are important, at PAS of AChE. The knowledge gained from this study, could lead to the discovery of potential AChE inhibitors that are highly specific for AD treatment as they are bivalent lead molecules endowed with dual binding ability for both catalytic site and PAS of AChE.     

排序学习前向掩蔽模型在T细胞表位预测中的应用

在综述了T细胞表位预测的定义，意义和研究现状的基础上，分析了当前流行的基于误差反向传播前馈神经网络(BPNN)的T细胞表位预测模型的不足，即网络结构较难确定、训练速度慢和难以增量学习等，提出了利用排序学习前向掩蔽（SLAM）模型及其增量学习算法作为T细胞表位预测方法，并给出了构建T细胞表位预测模型的基本步骤。基因HLA-DR4 (B1*0401)编码的MHC II类分子结合肽的应用实例表明，与基于BPNN的T细胞表位预测模型相比，基于SLAM的T细胞表位预测模型不但能在极短时间内完成样本的学习,而且能有效地实现增量学习。