Synthesis and biological evaluation of two chemically modified peptide epitopes for the class I MHC protein HLA-B*2705

The T-cell receptor of a CD8(+) T-cell recognises peptide epitopes bound by class I major histocompatibility complex (MHC) glycoproteins presented in a groove on their upper surface. Within the groove of the MHC molecule are 6 pockets, two of which mostly display a high degree of specificity for binding amino acids capable of making conserved and energetically favourable contacts with the MHC. One type of MHC molecule, HLA-B*2705, preferentially binds peptides containing an arginine at position 2. In an effort to increase the affinity of peptides for HLA-B*2705, potentially leading to better immune responses to such a peptide, we synthesised two modified epitopes where the amino acid at position 2 involved in anchoring the peptide to the class I molecule was replaced with the alpha-methylated beta,gamma-unsaturated arginine analogue 2-(S)-amino-5-guanidino-2-methyl-pent-3-enoic acid. The latter was prepared via a multi-step synthetic sequence, starting from alpha-methyl serine, and incorporated into dipeptides which were fragment-coupled to resin-bound heptameric peptides yielding the target nonameric sequences. Biological characterisation indicated that the modified peptides were poorer than the native peptides at stabilising empty class I MHC complexes, and cells sensitised with these peptides were not recognised as well by cognate CD8(+) T-cells, where available, compared to those sensitised with the native peptide. We suggest that the modifications made to the peptide have decreased its ability to bind to the peptide binding groove of HLA-B*2705 molecules which may explain the decrease in recognition by cytotoxic T-cells when compared to the native peptide.     

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.     

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.     

Dynamic characteristics of a peptide-binding groove of human HLA-A2 class I MHC molecules: normal mode analysis of the antigen peptide-class I MHC complex

Class I major histocompatibility complex (MHC) binds antigen peptides with various sequences. We performed a normal mode analysis of HLA-A2 MHC that binds three peptides with different affinity. HLA-A2 MHC has a peptide-binding groove composed of two alpha-helices (residue 49-84, residue 140-179). Some residues in the center of the groove showed an increase in fluctuations and some residue pairs between two helix groups showed a negative change in correlations by removing the antigen peptide. The extent of the fluctuation and correlation changes correlated well with the experimental ranking of the three peptides in binding affinity. Some definite anti-correlative motions were found between two helix groups in low frequency modes (<50 cm(-1)) by removing the antigen peptide. We propose that the above anti-correlative motions play an important role to bind the antigen peptide, especially in obtaining a "dynamic fit."     

多肽序列的结构特征与其MHC限制性

从多肽序列的一级结构出发,基于多肽序列中氨基酸侧链间距离和氨基酸侧链的电性特征,构建了多肽序列特征矢量,简称ζ矢量,选取文献中主要组织相容性复合物(MHC)中的14个A^d限制性和14个非A^d限制性多肽序列作为训练集建立辅助性T细胞(helperTlymphocytc,Th)表位预测的定量模型,为了检验该预报系统的精确性,进行了随机抽样检验和交互检验.结果表明,该预报系统具有稳定性好、预测能力强的特点,该方法可用于人的MHCⅠ和Ⅱ类表位的预测、蛋白质抗原免疫识别、亚单位疫苗分子设计及研制.     

Peptide reagent design based on physical and chemical properties of amino acid residues

It has tremendous values for both drug discovery and basic research to develop a solid bioinformatical tool for guiding peptide reagent design. Based on the physical and chemical properties of amino acids, a new strategy for peptide reagent design, the so-called AABPD (amino acid based-peptide design), is proposed. The peptide samples in a training dataset are described by a series of HMLP (heuristic molecular lipophilicity potential) parameters and other physicochemical properties of amino acid residues that form a three-dimensional data matrix where each component is defined by three indexes: the first index refers to the peptide samples, the second to the amino acid positions, and the third to the amino acid parameters. The binding free energy between a peptide ligand and its protein receptor is calculated by a linear free energy equation through the physicochemical parameters, resulting in a set of simultaneous linear equations between the bioactivity of the peptides and the physicochemical properties of amino acids. An iterative double least square technique is developed for the solution of the three-dimensional simultaneous linear equation set to determine the amino acid position coefficients of peptide sequence and the physicochemical parameter coefficients of amino acid residues alternately. The two sets of coefficients thus obtained are used for predicting the bioactivity of other query peptide reagents. Two calculation examples, the peptide substrate specificity of the SARS coronavirus 3C-like proteinase and the affinity prediction for epitope-peptides with Class I MHC molecules are studied by using the peptide reagent design strategy.     

Recovery of known T-cell epitopes by computational scanning of a viral genome

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A^*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list.The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.     

Efficient estimation of binding free energies between peptides and an MHC class II molecule using coarse‐grained molecular dynamics simulations with a weighted histogram analysis method

We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with the weighted histogram analysis method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse‐grained (CG) force field (MARTINI) is less prone to significant error induced by inadequate‐sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3–4 residue peptide segments while leaving the remaining peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and reliably estimate the binding free energy between an arbitrary peptide and an MHC class II molecule. © 2017 Wiley Periodicals, Inc.     

基于环境的编码方法在预测HLA-A*0201结合多肽中的应用

T淋巴细胞对抗原的识别是产生与调节有效免疫应答的关键, T细胞只识别主要组织相容性复合物(MHC)呈递上来的抗原, 因此MHC与抗原多肽的结合就成为一系列免疫应答过程中基础的一环. 为了辅助疫苗设计, 多种机器学习技术已被普遍应用于MHC结合多肽的预报领域中. 本文以支持向量机(SVM)为手段, 以HLA-A*0201的实验数据集为对象, 对多种肽段编码方法形成的模型进行评价, 得到的AUC值的范围在0.932~0.936之间. 提出一种新的利用抗原多肽结合环境的编码方法, 使预报的AUC值提高到0.953. 对独立数据集进行建模预报, 同样证明环境编码模型的预报准确率高于传统编码方法的准确率.     

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.     

A computational docking study on the pH dependence of peptide binding to HLA-B27 sub-types differentially associated with ankylosing spondylitis

A single amino acid difference (Asp116His), having a key role in a pathogenesis pathway, distinguishes HLA-B*27:05 and HLA-B*27:09 sub-types as associated and non-associated with ankylosing spondylitis, respectively. In this study, molecular docking simulations were carried out with the aim of comprehending the differences in the binding behavior of both alleles at varying pH conditions. A library of modeled peptides was formed upon single point mutations aiming to address the effect of 20 naturally occurring amino acids at the binding core peptide positions. For both alleles, computational docking was applied using Autodock 4.2. Obtained free energies of binding (FEB) were compared within the peptide library and between the alleles at varying pH conditions. The amino acid preferences of each position were studied enlightening the role of each on binding. The preferred amino acids for each position of pVIPR were found to be harmonious with experimental studies. Our results indicate that, as the pH is lowered, the capacity of HLA-B*27:05 to bind peptides in the library is largely lost. Hydrogen bonding analysis suggests that the interaction between the main anchor positions of pVIPR and their respective binding pocket residues are affected from the pH the most, causing an overall shift in the FEB profiles.     

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.     

Effect of chain length on the conformation and T cell recognition of synthetic hemagglutinin fragments

Circular dichroism and Fourier-transform infrared spectroscopies were used to compare the conformational mobility of 13-mer peptides covering the 317-329 region of the envelope protein hemagglutinin of human influenza A virus subtypes H1, H2 and H3 with that of their truncated deca- and nonapeptide analogs. These peptides were demonstrated to bind to the murine I-Ed major histocompatibility complex encoded class II and human HLA-B*2705 class I molecules. Despite the amino acid substitutions in the three 13-mer subtype sequences, no significant differences in the conformational properties could be shown. Deletion of the N-terminal three residues resulted in a shift to an increased alpha-helical conformer population in the 317-329 H1 peptide and the breakage of the 3(10) or weakly H-bonded (nascent) alpha-helix in the H2 and H3 peptides. The conformational change observed upon deletion did not influence the efficiency of I-Ed peptide interaction, however, the C-terminal Arg had a beneficial effect both on MHC class II and class I binding without causing any remarkable change in solution conformation.     

Prediction of peptide retention time in reversed-phase high-performance liquid chromatography.

Peptide retention in reversed-phase chromatography depends mainly on the amino acid composition of peptides and can therefore be predicted by summing the relative hydrophobic contributions of each constitutive amino acid residue. The prediction is correct for small peptides but overestimates the retention times of peptides larger than 10-15 residues. A new prediction model is proposed in which the contribution to peptide retention of each amino acid residue is not a constant but a decreasing function of peptide length. From the retention times of 104 peptides, the parameters of decreasing functions were estimated by a non-linear multiple regression analysis. The contribution to peptide retention of charged, polar and non-polar residues appears to be differently affected by peptide length. The secondary structure of most peptides during reversed-phase high-performance liquid chromatography could be responsible for this. The high correlation between the predicted and observed retention times of peptides which were not used to establish the model indicates a good predictive accuracy of the new model.     

Simulated Molecular Evolution for Anticancer Peptide Design

A computational technique based on a simulated molecular evolution protocol was employed for anticancer peptide (ACP) design. Starting from known ACPs, innovative bioactive peptides were automatically generated in computer‐assisted design–synthesize–test cycles. This design algorithm offers a viable strategy for the generation of novel peptide sequences, without requiring a priori structure–activity knowledge. Sequence morphing and activity improvement were achieved through iterative amino acid variation and selection. Results show that not only the interaction of ACPs with the target membrane is important for their anticancer activity, but also the degree of peptide dimerization, which was corroborated by temperature profiling and electrospray mass spectrometry.     

具有双活性序列的新型抗菌肽的设计及性质

设计合成了具有2个活性序列的线性和环状多肽及具有单个活性序列的短链多肽, 研究了它们的杀菌活性、 细胞毒性及溶血性. 结果表明, 线性肽和环状肽的杀菌活性高于短链肽. 利用计算模拟的方法计算了多肽与细菌细胞膜中一种重要的成分磷脂酰甘油(DMPG)的结合能. 结果表明, 多肽-DMPG的结合能与多肽的杀菌活性具有较高的相关性, 线性和环状多肽与DMPG的结合能大于短链肽. 线性和环状多肽均含有2个活性序列, 可提供多个荷正电氨基酸与荷负电的磷脂结合, 结合能较大, 杀菌活性较强. 采用模拟生物膜对其中几条多肽的作用机理进行了初步研究. 结果表明, 该类多肽有可能使正常哺乳动物细胞的细胞膜产生孔洞; 而对于细菌细胞膜, 多肽并未在膜上产生明显孔洞, 而是引起了细菌细胞膜的聚集.     

In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study

Antigenic peptides or cancer peptide vaccines can be directly delivered to cancer patients to produce immunologic responses against cancer cells. Specifically, designed peptides can associate with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells activating anti-tumor effector mechanisms by triggering helper T cell (Th) or cytotoxic T cells (CTL). In general, high binding to MHCs approximately correlates with in vivo immunogenicity. Consequently, a molecular docking technique was run on a library of novel discontinuous peptides predicted by PEPOP from Human epidermal growth factor receptor 2 (HER2 ECD) subdomain III. This technique is expected to improve the prediction accuracy in order to identify the best MHC class I and II binder peptides. Molecular docking analysis through GOLD identified the peptide 1412 as the best MHC binder peptide to both MHC class I and II molecules used in the study. The GOLD results predicted HLA-DR4, HLA-DP2 and TCR as the most often targeted receptors by the peptide 1412. These findings, based on bioinformatics analyses, can be exploited in further experimental analyses in vaccine design and cancer therapy to find possible proper approaches providing beneficial effects.     

Systematic mutational analysis of an ubiquitin ligase (MDM2)-binding peptide: computational studies

To understand the importance of amino acids that comprise the peptide PMI (p53-MDM2/MDMX inhibitor), a p53-mimicking peptide with high affinity for the ubiquitin ligase MDM2, computational alanine scanning has been carried out using various protocols. This approach is very useful for identifying regions of a peptide that can be mutated to yield peptides that bind to their targets with higher affinities. Computational alanine scanning is a very useful technique that involves mutating each amino acid of the peptide in its complex with its target (MDM2 in the current study) to alanine, running short simulations on the mutated complex and computing the difference in interaction energies between the mutant peptides and the target protein (MDM2 in the current study) relative to the interaction energy of the original (wild-type) peptide and the target protein (MDM2 in the current study). We find that running multiple short simulations yield values of computed binding affinities (enthalpies) that are similar to those obtained from a long simulation and are well correlated with the trends in the data available from experiments that used Surface Plasmon Resonance to obtain dissociation constants. The p53-mimicking peptides contain three amino acids (F19, W23 and L26) that are major determinants of the interactions between the peptides and MDM2 and form an essential motif. We find in the current study that the trends amongst the contributions to experimental binding affinities of the hydrophobic residues F19, W23 and L26 are the best reproduced in all the computational protocols examined here. This study suggests that running such short simulations may provide a rapid method to redesign peptides to obtain high-affinity variants against a target protein. We further observe that modelling an extended conformation at the C-terminus of the helical PMI peptides, in accord with the conformation of the p53-peptide complexed to MDM2, reproduces the trends seen amongst the experimental affinities of the peptides that carry the alanine mutations at their C-termini. This suggests that some of the mutant peptides possibly interconvert between helical and extended states and can bind to MDM2 in either conformation. This novel feature, not obvious from the crystallographic data, if factored into modelling protocols, may yield novel high-affinity peptides. Our findings suggest that such protocols may enable rapid investigations of at least certain types of amino acid mutations, notably from large to small amino acids.