Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).     

New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity

Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).     

A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif

The 3D-QSAR CoMSIA technique was applied to a set of 458 peptides binding to the five most widespread HLA-A2-like alleles: A^*0201, A^*0202, A^*0203, A^*0206 and A^*6802. Models comprising the main physicochemical properties (steric bulk, electron density, hydrophobicity and hydrogen-bond formation abilities) were obtained with acceptable predictivity (q ² ranged from 0.385 to 0.683). The use of coefficient contour maps allowed an A2-supermotif to be identified based on common favoured and disfavoured areas. The CoMSIA definition for the best HLA-A2 binder is as follows: hydrophobic aromatic amino acid at position 1; hydrophobic bulky side chains at positions 2, 6 and 9; non-hydrogen-bond-forming amino acids at position 3; small aliphatic hydrogen-bond donors at position 4; aliphatic amino acids at position 5; small aliphatic side chains at position 7; and small aliphatic hydrophilic and hydrogen-bond forming amino acids at position 8.     

HLA-A*0201限制性CTL表位肽的三维定量构效关系的研究

运用比较分子力场(CoMFA)和比较分子相似性指数分析(CoMFA)方法研究了50个HLA-A^*0201限制性CTL表位九肽结构与亲和性间的关系,另外15个表位九肽作为预测集用于检验模型的预测能力.结果表明采用CoMSIA得到的构效关系模型(q^2=0.628,r^2=0.997,F=840.419)要明显优于采用CoMFA得到的构效关系模型.在CoMSIA计算中,当引入疏水场时,三维构效关系模型得到明显改善,通过该三维构效关系模型,可较精确地估算预测集中15个CTL表位肽与HLA-A^*0201间的亲和力(r^2pred=0.743).通过分析分子场等值面图在空间的分布,可以观察到表位肽分子周围的立体及疏水特征对表位肽与HLA-A^*0201间结合亲和力的影响,从而为进一步对CTL表位肽进行结构改造并基于此进行治疗性疫苗分子设计提供理论基础.     

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.     

Analysis of Ah receptor binding affinities of polybrominated diphenyl ethers via in silico molecular docking and 3D-QSAR

Polybrominated diphenyl ethers (PBDEs) have become ubiquitous contaminations due to their use as flame retardants. The structural similarity of PBDE to some dioxin-like compounds suggested that they may share similar toxicological effects: they might activate the aryl hydrocarbon receptor (AhR) signal transduction pathway and thus might have adverse effects on wildlife and humans. In this study, in silico computational workflow combining molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) was performed to investigate the binding interactions between PBDEs and AhR and the structural features affecting the AhR binding affinity of PBDE. The molecular docking showed that hydrogen-bond and hydrophobic interactions were the major driving forces for the binding of ligands to AhR, and several key amino acid residues were also identified. The CoMSIA model was developed from the conformations obtained from molecular docking and exhibited satisfactory results as q ² of 0.605 and r ² of 0.996. Furthermore, the derived model had good robustness and statistical significance in both internal and external validations. The 3D contour maps generated from CoMSIA provided important structural features influence the binding affinity. The obtained results were beneficial to better understand the toxicological mechanism of PBDEs.     

The HLA-A2-supermotif: a QSAR definition

Identification of epitopes capable of binding multiple HLA types will significantly rationalise the development of epitope-based vaccines. A quantitative method assessing the contribution of each amino acid at each position was applied to over 500 nonamer peptides binding to 5 MHC alleles--A*0201, A*0202, A*0203, A*0206 and A*6802--which together define the HLA-A2-like supertype. FXIGXI (L)IFV was identified as a supermotif for the A2-supertype based on the contributions of the common preferred amino acids at each of the nine positions. The results indicate that HLA-A*6802 is an intermediate allele standing between A2 and A3 supertypes: at anchor position 2 it is closer to A3 and at anchor position 9 it is nearer to A2. Models are available free on-line at http://www.jenner.ac.uk/MHCPred and can be used for binding affinity prediction.     

Comparative Molecular Similarity Index Analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries

Comparative molecular field analysis has been applied to a data set of thermolysin inhibitors. Fields expressed in terms of molecular similarity indices (CoMSIA) have been used instead of the usually applied Lennard-Jones- and Coulomb-type potentials (CoMFA). Five different properties, assumed to cover the major contributions responsible for ligand binding, have been considered: steric, electrostatic, hydrophobic, and hydrogen-bond donor or acceptor properties. The statistical evaluation of the field properties by PLS analysis reveals a similar predictive potential to CoMFA. However, significantly improved and easily interpretable contour maps are obtained. The features in these maps intuitively suggest where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity. They can also be interpreted with respect to the known structural protein environment of thermolysin. Most of the highlighted regions in the maps are mirrored by features in the surrounding environment required for binding. Using the derived correlation model, different members of a combinatorial library designed for thermolysin inhibition have been scored for affinity. The results obtained demonstrate the prediction power of the CoMSIA method.     

Highly predictive CoMFA and CoMSIA models for two series of stromelysin-1 (MMP-3) inhibitors elucidate S1' and S1-S2' binding modes

Three-dimensional quantitative structure-activity relationship models have been derived using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for two training sets of arylsulfonyl isoquinoline-based and thazine/thiazepine-based matrix metalloproteinase inhibitors (MMPIs). The crystal structure of stromelysin-1 (MMP-3) was used to pinpoint areas on the ligands and receptors where steric and electrostatic effects (for CoMFA) and steric, electrostatic, hydrogen-bond donor, hydrogen-bond acceptor, and hydrophobic effects (for CoMSIA) correlate with an increase or decrease in experimental biological activity. The most predictive CoMFA and CoMSIA models were obtained using training-series subsets that sampled a wide range of activities, together with docking and scoring, inertial alignment, investigation of various partial charge formalisms, and manual adjustment of each compound within the active site. The models developed in this study are in agreement with experimentally observed MMP-3 structure-activity relationship data and offer new insights into binding modes involving the partly solvent-exposed S1-S2' subpocket and certain zinc-chelating groups.     

A 3D QSAR study on a set of dopamine D4 receptor antagonists

The molecular alignments obtained from a previously reported pharmacophore model have been employed in a three-dimensional quantitative structure-activity relationship (3D QSAR) study, to obtain a more detailed insight into the structure-activity relationships for D(2) and D(4) receptor antagonists. The frequently applied CoMFA method and the related CoMSIA method were used. Statistically significant models have been derived with these two methods, based on a set of 32 structurally diverse D(2) and D(4) receptor antagonists. The CoMSIA and the CoMFA methods produced equally good models expressed in terms of q(2) values. The predictive power of the derived models were demonstrated to be high. Graphical interpretation of the results, provided by the CoMSIA method, brings to light important structural features of the compounds related to either low- or high-affinity D(2) or D(4) antagonism. The results of the 3D QSAR studies indicate that bulky N-substituents decrease D(2) binding, whereas D(4) binding is enhanced. Electrostatically favorable and unfavorable regions exclusive to D(2) receptor binding were identified. Likewise, certain hydrogen-bond acceptors can be used to lower D(2) affinity. These observations may be exploited for the design of novel dopamine D(4) selective antagonists.     

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.     

Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach

Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].     

苯并咪唑类缓蚀剂的3D-QSAR研究及分子设计

采用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 对苯并咪唑衍生物抗盐酸腐蚀的缓蚀性能进行了三维定量构效关系研究, 并使用留一法交叉验证手段对3D-QSAR模型的稳定性及预测能力进行了分析. 结果表明, 立体场、静电场和氢键供体场(电子给体)是影响苯并咪唑缓蚀剂缓蚀性能的主要因素; 所构建的CoMFA模型(q2=0.541, R2=0.996)和CoMSIA模型(q2=0.581, R2=0.987)均具有较好的统计学稳定性和预测能力. 基于3D-QSAR等势图设计出了几种具有较好缓蚀性能的苯并咪唑化合物, 为油气田新型缓蚀剂的研发提供了一种新思路.     

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics: comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.     

3D-QSAR Study of Melittin and Amoebapore Analogues by CoMFA and CoMSIA Methods

Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q~2) was 0.583 and non-cross-validation correlation coefficient(r~2) was 0.972 for the melittin CoMFA model. The q~2 and r~2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.     

A structure-based 3D-QSAR study of anthrapyrazole analogues of the anticancer agents losoxantrone and piroxantrone

A series of 13 anthrapyrazole compounds that are analogues of piroxantrone and losoxantrone were synthesized, and their cell growth inhibitory effects, DNA binding, topoisomerase IIalpha mediated (EC 5.99.1.3) cleavage of DNA, and inhibition of DNA topoisomerase IIalpha decatenation catalytic activities were determined. Cell growth inhibitory activity was well-correlated with DNA binding, suggesting that these compounds may act by targeting DNA. However, cell growth inhibition was not well-correlated with the inhibition of topoisomerase IIalpha catalytic activity, suggesting that these anthrapyrazoles did not act solely by inhibiting the catalytic activity of topoisomerase II. Most of the analogues were able to induce DNA cleavage, and thus, it was concluded that they acted, at least in part, as topoisomerase II poisons. Structure-based three-dimensional quantitative structure-activity analyses (3D-QSAR) were carried out on the aligned structures of the anthrapyrazoles docked into DNA using comparative molecular field analysis (CoMFA) and comparative molecular similarity index (CoMSIA) analyses in order to determine the structural features responsible for their activity. Both CoMFA and CoMSIA yielded statistically significant models upon partial least-squares analyses. The 3D-QSAR analyses showed that hydrogen-bond donor interactions and electrostatic interactions with the protonated amino side chains of the anthrapyrazoles led to high cell growth inhibitory activity.     

雌激素β受体喹啉类配体的分子对接及3D-QSAR研究

对33个喹啉衍生物的雌激素β受体活性进行了分子对接以及比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA). 对接结果显示氢键和疏水作用是配体与受体结合的主要因素,同时结果亦显示对接结合能与观测值pIC50具有极显著的线性相关性. 根据对接后各优势构象将33个样本进行叠合并进行CoMFA与CoMSIA研究,均得到了较优的结果,其中以选用立体场、静电场和疏水场建立的CoMSIA模型结果最优,其主成分数,r2,q2（LOO）和r2pred分别为2, 0.894, 0.708和0.802. 构效关系模型分析显示基团的空间位阻、电性及疏水作用是影响活性的主要因素     

Cooperative effects in two-dimensional ring-like networks of three-center hydrogen bonding interactions

Cooperative effects in two-dimensional cyclic networks containing intermolecular three-centered hydrogen bonding interactions of the type H1...A...H2 are investigated by means of ab intio molecular orbital and density functional theory calculations. Ring-like clusters consisting of three and up to nine monomers of the cis-cis isomer of carbonic acid H2CO3 are used as basic models, where each unit acts simultaneously as a double hydrogen-bond donor and double hydrogen-bond acceptor. Cooperative effects based on binding energies are evident for (H2CO3)n, where n goes from 2 to 9. Thus, the ZPVE-corrected dissociation energy per bifurcated hydrogen bond increases from 11.52 kcal/mol in the dimer to 20.42 kcal/mol in the nonamer, i.e., a 77% cooperative enhancement. Cooperative effects are also manifested in such indicators as geometries, and vibrational frequencies and intensities. The natural bond orbital analysis method is used to rationalize the results in terms of the substantial charge delocalization taking place in the cyclic clusters. Cooperativity seems close to reaching an asymptotic limit in the largest ring considered, n = 9.     

作用于秋水仙碱位点的微管蛋白抑制剂的结合模式 研究与结构模型的构建

采用对接的方法建立了秋水仙碱位点抑制剂与微管蛋白的结合模式, 并构建了其结构模型. 结果表明: 抑制剂主要借助于与口袋I和II的疏水作用, 以及同α-Thr178, α-Val181和β-Cys241之间的氢键来实现与微管蛋白的结合. 根据抑制剂的结合构象, 将抑制剂的结构分为A, B以及AB间的桥连三个部分, 从而建立了由A部分中的疏水中心H1、氢键受体A1, B部分中的疏水中心H2、疏水基团H3和极性原子P以及桥连结构中的氢键受体A2组成的结构模型. 并指出H1与H2对活性的影响因素分别为疏水基团的体积和平面特征, 而桥连部分则应以刚性的形式保证AB处于桥连的同侧(即顺式构象). 还提出在A2与loop区之间存在一个的潜在氢键受体A3. 研究结果为设计新型小分子微管蛋白抑制剂提供指导.