DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations

MOTIVATION: The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results. RESULTS: We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPred(POS)) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 A from the corresponding experimental structures.     

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Background

The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

Methods

We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

Results

Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

Conclusions

The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

     

系统比较氨基酸描述子在多肽定量构效关系中的应用

氨基酸描述子是采用多元统计方法从大量氨基酸性质参数中提取得到的少数关键信息成分,目前已被广泛应用于多肽生物活性预测及蛋白质功能判别等领域。鉴于近年来氨基酸描述子种类的极速扩增,收集了目前国内外发表的27种氨基酸描述子,并将其用于8组经典多肽集的结构表征及定量构效关系研究。通过系统比较这些描述子对同一肽集与不同肽集的统计建模结果,我们认为物理化学描述子的建模效果优于拓扑描述子,拓扑描述子的建模效果优于三维结构描述子,且已有诸多氨基酸描述子已经达到性能限度,如未考虑肽链内部各氨基酸残基的交互影响,肽配基与相应靶标蛋白的相互结合,因此不再建议按照传统思路进一步提出新型描述子种类。     

Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.     

POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties

MOTIVATION: Both modeling of antigen-processing pathway including major histocompatibility complex (MHC) binding and immunogenicity prediction of those MHC-binding peptides are essential to develop a computer-aided system of peptide-based vaccine design that is one goal of immunoinformatics. Numerous studies have dealt with modeling the immunogenic pathway but not the intractable problem of immunogenicity prediction due to complex effects of many intrinsic and extrinsic factors. Moderate affinity of the MHC-peptide complex is essential to induce immune responses, but the relationship between the affinity and peptide immunogenicity is too weak to use for predicting immunogenicity. This study focuses on mining informative physicochemical properties from known experimental immunogenicity data to understand immune responses and predict immunogenicity of MHC-binding peptides accurately. RESULTS: This study proposes a computational method to mine a feature set of informative physicochemical properties from MHC class I binding peptides to design a support vector machine (SVM) based system (named POPI) for the prediction of peptide immunogenicity. High performance of POPI arises mainly from an inheritable bi-objective genetic algorithm, which aims to automatically determine the best number m out of 531 physicochemical properties, identify these m properties and tune SVM parameters simultaneously. The dataset consisting of 428 human MHC class I binding peptides belonging to four classes of immunogenicity was established from MHCPEP, a database of MHC-binding peptides (Brusic et al., 1998). POPI, utilizing the m = 23 selected properties, performs well with the accuracy of 64.72% using leave-one-out cross-validation, compared with two sequence alignment-based prediction methods ALIGN (54.91%) and PSI-BLAST (53.23%). POPI is the first computational system for prediction of peptide immunogenicity based on physicochemical properties. AVAILABILITY: A web server for prediction of peptide immunogenicity (POPI) and the used dataset of MHC class I binding peptides (PEPMHCI) are available at http://iclab.life.nctu.edu.tw/POPI     

Homology modeling of an immunoglobulin-like domain in the Saccharomyces cerevisiae adhesion protein alpha-agglutinin.

The Saccharomyces cerevisiae adhesion protein alpha-agglutinin is expressed by cells of alpha mating type. On the basis of sequence similarities, alpha-agglutinin has been proposed to contain variable-type immunoglobulin-like (IgV) domains. The low level of sequence similarity to IgV domains of known structure made homology modeling using standard sequence-based alignment algorithms impossible. We have therefore developed a secondary structure-based method that allowed homology modeling of alpha-aggulutinin domain III, the domain most similar to IgV domains. The model was assessed and where necessary refined to accommodate information obtained by biochemical and molecular genetic approaches, including the positions of a disulfide bond, glycosylation sites, and proteolytic sites. The model successfully predicted surface exposure of glycosylation and proteolytic sites, as well as identifying residues essential for binding activity. One side of the domain was predicted to be covered by carbohydrate residues. Surface accessibility and volume packing analyses showed that the regions of the model that have greatest sequence dissimilarity from the IgV consensus sequence are poorly structured in the biophysical sense. Nonetheless, the utility of the model suggests that these alignment and testing techniques should be of general use for building and testing of models of proteins that share limited sequence similarity with known structures.     

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.     

Control of AMP deaminase 1binding to myosin heavy chain

AMP deaminase (AMPD) plays a central role in preserving theadenylate energy charge in myocytes following exercise and in producingintermediates for the citric acid cycle in muscle. Prior studies havedemonstrated that AMPD1 binds to myosin heavy chain (MHC)in vitro; binding to the myofibril varies with the state of musclecontraction in vivo, and binding of AMPD1 to MHC is required foractivation of this enzyme in myocytes. The present study has identifiedthree domains in AMPD1 that influence binding of this enzyme to MHCusing a cotransfection model that permits assessment of mutationsintroduced into the AMPD1 peptide. One domain that encompasses residues178-333 of this 727-amino acid peptide is essential for binding ofAMPD1 to MHC. This region of AMPD1 shares sequence similarity withseveral regions of titin, another MHC binding protein. Two additionaldomains regulate binding of this peptide to MHC in response tointracellular and extracellular signals. A nucleotide binding site,which is located at residues 660-674, controls binding of AMPD1 toMHC in response to changes in intracellular ATP concentration. Deletionanalyses demonstrate that the amino-terminal 65 residues of AMPD1 playa critical role in modulating the sensitivity to ATP-induced inhibitionof MHC binding. Alternative splicing of the AMPD1 gene product, which alters the sequence of residues 8-12, produces two AMPD1 isoforms that exhibit different MHC binding properties in the presence of ATP.These findings are discussed in the context of the various rolesproposed for AMPD in energy production in the myocyte.

     

Structural and immunological reactivity of the principal neutralizing determinant V3 of glycoprotein gp 120 of HIV-1

The variable domain V3 in the outer glycoprotein gp 120 of HIV-1 is a highly important region with respect to immune response during the course of viral infection. Neutralizing antibodies are produced against this domain; in addition, it has been shown to be a functionally active epitope for T helper and cytotoxic T cells. The high degree of amino acid variability in individual HIV-isolates, however, limits the use of the V3-domain in approaches to vaccine development. In order to characterize the residues important for antibody interaction and binding to MHC class I proteins, we constructed a consensus sequence of the V3-domain with broad reactivity [1] and used synthetic peptides derived from this consensus with individual residues altered to alanine. These peptides were used as antigens in ELISA tests to define the amino acids which are important for binding to human and rabbit/anti-peptide immunoglobulins. In addition, we used these alanine-derived peptides in interaction studies with human HLA-A2.1 and mouse H-2D^d by testing their capacity to stabilize the respective MHC class I protein complexes on the surface of mutant cell lines T2 and RMA-S transfected with D^d gene. The experimental tests allowed us to define individual residues involved in antibody and MHC-protein interaction, respectively. In a further approach, we used those results to design interaction models with HLA-A2.1 and H-2D^d. Therefore, a structural model for H-2D^d was built that exhibits an overall similar conformation to the parental crystal structure of HLA-A2.1. The resulting interaction models show V3-peptide bound in an extended β-conformation with a bulge in its centre for both H-2D^d and HLA-A2.1 complexes. The N- and C-termini of V3 peptide reside in conserved pockets within both MHC-proteins. Anchoring residues could be determined that are crucial for the binding of the respective MHC class I haplotype. The cross-reactivity of V3-peptide in enhancing the expression of two different MHC class I molecules (H-2D^d and HLA-A2.1) is shown to be based on similar peptide binding that induces an almost identical peptide conformation.     

Different Strategies Adopted by Kb and Ld to Generate T Cell Specificity Directed against Their Respective Bound Peptides

Mouse T cell clone 2C recognizes two different major histocompatibility (MHC) ligands, the self MHC K^b and the allogeneic MHC L^d. Two distinct peptides, SIY (SIYRYYGL) and QL9 (QLSPFPFDL), act as strong and specific agonists when bound to K^b and L^d, respectively. To explore further the mechanisms involved in peptide potency and specificity, here we examined a collection of single amino acid peptide variants of SIY and QL9 for 1) T cell activity, 2) binding to their respective MHC, and 3) binding to the 2C T cell receptor (TCR) and high affinity TCR mutants. Characterization of SIY binding to MHC K^b revealed significant effects of three SIY residues that were clearly embedded within the K^b molecule. In contrast, QL9 binding to MHC L^d was influenced by the majority of peptide side chains, distributed across the entire length of the peptide. Binding of the SIY-K^b complex to the TCR involved three SIY residues that were pointed toward the TCR, whereas again the majority of QL9 residues influenced binding of TCRs, and thus the QL9 residues had impacts on both L^d and TCR binding. In general, the magnitude of T cell activity mediated by a peptide variant was influenced more by peptide binding to MHC than by binding the TCR, especially for higher affinity TCRs. Findings with both systems, but QL9-L^d in particular, suggest that many single-residue substitutions, introduced into peptides to improve their binding to MHC and thus their vaccine potential, could impair T cell reactivity due to their dual impact on TCR binding.     

On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles

Structural alignments often reveal relationships between proteins that cannot be detected using sequence alignment alone. However, profile search methods based entirely on structural alignments alone have not been found to be effective in finding remote homologs. Here, we explore the role of structural information in remote homolog detection and sequence alignment. To this end, we develop a series of hybrid multidimensional alignment profiles that combine sequence, secondary and tertiary structure information into hybrid profiles. Sequence-based profiles are profiles whose position-specific scoring matrix is derived from sequence alignment alone; structure-based profiles are those derived from multiple structure alignments. We compare pure sequence-based profiles to pure structure-based profiles, as well as to hybrid profiles that use combined sequence-and-structure-based profiles, where sequence-based profiles are used in loop/motif regions and structural information is used in core structural regions. All of the hybrid methods offer significant improvement over simple profile-to-profile alignment. We demonstrate that both sequence-based and structure-based profiles contribute to remote homology detection and alignment accuracy, and that each contains some unique information. We discuss the implications of these results for further improvements in amino acid sequence and structural analysis.