Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature

<p>Abstract</p> <p>Background</p> <p>Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction.</p> <p>Results</p> <p>In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including the impact of interior residues, different contributions of adjacent residues, and the imbalanced data which contain much more non-epitope residues than epitope residues. In order to address above issues, we take following strategies. Firstly, a concept of 'thick surface patch' instead of 'surface patch' is introduced to describe the local spatial context of each surface residue, which considers the impact of interior residue. The comparison between the thick surface patch and the surface patch shows that interior residues contribute to the recognition of epitopes. Secondly, statistical significance of the distance distribution difference between non-epitope patches and epitope patches is observed, thus an adjacent residue distance feature is presented, which reflects the unequal contributions of adjacent residues to the location of binding sites. Thirdly, a bootstrapping and voting procedure is adopted to deal with the imbalanced dataset. Based on the above ideas, we propose a new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine. The experiments show that our method can predict conformational B-cell epitopes with high accuracy. Evaluated by leave-one-out cross validation (LOOCV), our method achieves the mean AUC value of 0.633 for the benchmark bound dataset, and the mean AUC value of 0.654 for the benchmark unbound dataset. When compared with the state-of-the-art prediction models in the independent test, our method demonstrates comparable or better performance.</p> <p>Conclusions</p> <p>Our method is demonstrated to be effective for the prediction of conformational epitopes. Based on the study, we develop a tool to predict the conformational epitopes from 3D structures, available at <url>http://code.google.com/p/my-project-bpredictor/downloads/list</url>.</p

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far

Blueprint for a minimal photoautotrophic cell: conserved and variable genes in Synechococcus elongatus PCC 7942

Background: Simpler biological systems should be easier to understand and to engineer towards pre-defined goals. One way to achieve biological simplicity is through genome minimization. Here we looked for genomic islands in the fresh water cyanobacteria Synechococcus elongatus PCC 7942 (genome size 2.7 Mb) that could be used as targets for deletion. We also looked for conserved genes that might be essential for cell survival.Results: By using a combination of methods we identified 170 xenologs, 136 ORFans and 1401 core genes in the genome of S. elongatus PCC 7942. These represent 6.5%, 5.2% and 53.6% of the annotated genes respectively. We considered that genes in genomic islands could be found if they showed a combination of: a) unusual G+C content; b) unusual phylogenetic similarity; and/or c) a small number of the highly iterated palindrome 1 (HIP1) motif plus an unusual codon usage. The origin of the largest genomic island by horizontal gene transfer (HGT) could be corroborated by lack of coverage among metagenomic sequences from a fresh water microbialite. Evidence is also presented that xenologous genes tend to cluster in operons. Interestingly, most genes coding for proteins with a diguanylate cyclase domain are predicted to be xenologs, suggesting a role for horizontal gene transfer in the evolution of Synechococcus sensory systems.Conclusions: Our estimates of genomic islands in PCC 7942 are larger than those predicted by other published methods like SIGI-HMM. Our results set a guide to non-essential genes in S. elongatus PCC 7942 indicating a path towards the engineering of a model photoautotrophic bacterial cell.Financial support was provided by grants BFU2009-12895-C02-01/BMC (Ministerio de Ciencia e Innovación, Spain), the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement number 212894 and Prometeo/2009/092 (Conselleria d’Educació, Generalitat Valenciana, Spain) to A. Moya. Work in the FdlC laboratory was supported by grants BFU2008-00995/BMC (Spanish Ministry of Education), RD06/0008/1012 (RETICS research network, Instituto de Salud Carlos III, Spanish Ministry of Health) and LSHM-CT- 2005_019023 (European VI Framework Program). Dr. González-Domenech was supported by grant from the University of Granada. LD, thanks to financial support from Facultad de Ciencias, Universidad Nacional Autónoma de México

Cross-reactive neutralizing antibody epitopes against Enterovirus 71 identified by an in silico approach

Currently, infections of hand, foot and mouth disease HFMD) due to Human Enterovirus 71 (EV71) cannot be prevented or treated, as there are no suitable vaccines or antiviral drugs. This study aimed to identify potential vaccine candidates for EV71 using in silico analysis of its viral capsid proteins. A combined in silico approach utilizing computational hidden Markov model (HMM), propensity scale algorithm, and artificial learning, identified three 15-mer structurally conserved B-cell epitope candidates lying within the EV71 capsid proteins. Peptide vaccine candidates incorporating a target B-cell epitope and a promiscuous T-cell epitope from the related polio virus were synthesized using solid-phase Fmoc chemistry. Inbred BALB/C mice which were inoculated with two 10μg doses of the synthetic peptide, generated anti-peptide antibodies. Purified IgG isolated from pooled sera of the inoculated mice neutralized EV71 infections in vitro. Furthermore, these neutralizing antibodies were cross-reactive against other members of the Picornaviridae family, demonstrating greater than 50% virus neutralization. This indicates that the current approach is promising for the development of synthetic peptide-based vaccine candidates against Picornaviridae. Development of effective vaccines is of paramount importance in managing the disease in the Asia Pacific regions where this virus is endemic and has significant social, economic and public health ramifications

Does conformational free energy distinguish loop conformations in proteins?

Limitations in protein homology modeling often arise from the inability to adequately model loops. In this paper we focus on the selection of loop conformations. We present a complete computational treatment that allows the screening of loop conformations to identify those that best fit a molecular model. The stability of a loop in a protein is evaluated via computations of conformational free energies in solution, i.e., the free energy difference between the reference structure and the modeled one. A thermodynamic cycle is used for calculation of the conformational free energy, in which the total free energy of the reference state (i.e., gas phase) is the CHARMm potential energy. The electrostatic contribution of the solvation free energy is obtained from solving the finite-difference Poisson-Boltzmann equation. The nonpolar contribution is based on a surface area-based expression. We applied this computational scheme to a simple but well-characterized system, the antibody hypervariable loop (complementarity-determining region, CDR). Instead of creating loop conformations, we generated a database of loops extracted from high-resolution crystal structures of proteins, which display geometrical similarities with antibody CDRs. We inserted loops from our database into a framework of an antibody; then we calculated the conformational free energies of each loop. Results show that we successfully identified loops with a "reference-like" CDR geometry, with the lowest conformational free energy in gas phase only. Surprisingly, the solvation energy term plays a confusing role, sometimes discriminating "reference-like" CDR geometry and many times allowing "non-reference-like" conformations to have the lowest conformational free energies (for short loops). Most "reference-like" loop conformations are separated from others by a gap in the gas phase conformational free energy scale. Naturally, loops from antibody molecules are found to be the best models for long CDRs (> or = 6 residues), mainly because of a better packing of backbone atoms into the framework of the antibody model

Biological sensors: More than one way to sense oxygen

International audienceRecently determined structures of the oxygen-sensing heme domain of the bacterial protein FixL have revealed a new binding environment and signal transduction mechanism for heme; they have also provided new insights into the diverse 'PAS' domain superfamily

INTERALIGN: interactive alignment editor for distantly related protein sequences

International audienceImproving and ascertaining the quality of a multiple sequence alignment is a very challenging step in protein sequence analysis. This is particularly the case when dealing with sequences in the 'twilight zone', i.e. sharing <30% identity. Here we describe INTERALIGN, a dedicated user-friendly alignment editor including a view of secondary structures and a synchronized display of carbon alpha traces of corresponding protein structures. Profile alignment, using CLUSTALW, is implemented to improve the alignment of a sequence of unknown structure with the visually optimized structural alignment as compared with a standard multiple sequence alignment. Tree-based ordering further helps in identifying the structure closest to a given sequence. Availability: Windows and Linux packages, as well as source files, are available under the CeCILL free software licensing agreemen

Structural insights into protein–uranyl interaction: towards an in silico detection method

Documenting the modes of interaction of uranyl (UO 2 2+) with large biomolecules, and particularly with proteins, is instrumental for the interpretation of its behavior in vitro and in vivo. The gathering of three-dimensional information concerning uranyl-first shell atoms from two structural databases, the Cambridge Structural Databank and the Protein Data Bank (PDB) allowed a screening of corresponding topologies in proteins of known structure. In the computer-aided procedure, all potentially bound residues from the template structure were granted full flexibility using a rotamer library. The Amber force-field was used to loosen constraints and score each predicted site. Our algorithm was validated as a first stage through the recognition of existing experimental data in the PDB. The coherent localization of missing atoms in the density map of an ambiguous uranium/uranyl-protein complex exemplified the efficiency of our approach, which is currently suggesting the experimental investigation of uranyl-protein binding site