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This article deals with protein three-dimensional structure prediction, and focuses on how to make structural sense out of a single input sequence with unknown structure, the ‘query’ or ‘target’ sequence. Both template based and template free modelling techniques are treated, and how resulting structural models may be selected and refined. We also discuss the way in which structural predictions are validated within the global community, and elaborate on the extent to which predicted structures may be trusted and used in practice. We give a concrete flowchart for how to decide which modelling strategy is best suited in particular circumstances, and which steps need to be taken in each strategy. Notably, the ability to locate a suitable structural template by homology or fold recognition is crucial; without this models will be of low quality at best. With a template available, the quality of the query-template alignment crucially determines the model quality. We also discuss how other, courser, experimental data may be incorporated in the modelling process to alleviate the problem of missing template structures. Finally we discuss whether the concept of a single fold pertaining to a protein structure is sustainable given recent insights. In short, we conclude that the general protein three-dimensional structure prediction problem remains unsolved, especially if we desire quantitative predictions. However, if a homologous structural template is available in the PDB model or reasonable to high accuracy may be generated.

Additional file 1. Sequence motifs in mTLR9 active ODNs having an absolute difference in the occurrences above 10% in high and low activity groups of ODNs, arranged in a clockwise manner. The width of the ribbon shows the average percentage composition of the motifs each group.

Additional file 3. The effect of ODN motif occurrences on the median mTLR9 activity in the high activity group. The median RAW-Blue activity for all the ODNs in the high activity group was 0.53. Increase or decrease in the median activity values due to the presence of a motif are coloured in green and red, respectively, with statistically significant values in bold. The significance threshold was set at p value

This file contains Notes 1 and 2, Figures S1-S8. and Tables S1-S9. and relevant references. (PDF 3690 kb)

Phylogenetic trees of influenza A/H7N9. Time-scale phylogenetic trees of PB2, PB1, PA, HA, NP, NA, M1 and NS1 genes of influenza H7N9 strains. (PDF 117 kb)

Average energy contribution of residues that involved in receptor-ligand interactions in the optimally docked complexes. (PDF 24 kb)

Average energy contribution of residues that involved in receptor-ligand interactions in the optimally docked complexes. (PDF 24 kb)

List of InCoB2017 Reviewers. (PDF 52Â kb)

Energy decomposition of HA-ligand complexes. VdW energy, electrostatic energy and total interaction energy of SH13-LSTa, SH13-LSTc, TW17-LSTa and TW17-LSTc. (PDF 702 kb)

Energy decomposition of HA-ligand complexes. VdW energy, electrostatic energy and total interaction energy of SH13-LSTa, SH13-LSTc, TW17-LSTa and TW17-LSTc. (PDF 702 kb)

Two rounds of 50Â ns molecular dynamics simulation for SH13-LSTa, SH13-LSTc, TW17-LSTa, TW17-LSTc. Average total binding energy (kJ/mol) of the HA-LSTa/LSTc complexes. (PDF 424 kb)

Two rounds of 50Â ns molecular dynamics simulation for SH13-LSTa, SH13-LSTc, TW17-LSTa, TW17-LSTc. Average total binding energy (kJ/mol) of the HA-LSTa/LSTc complexes. (PDF 424 kb)

Superimpose the best and worst docked HA-ligand complexes. Visualize the structure of docked SH13-LSTa, SH13-LSTc, TW17-LSTa and TW17-LSTc complexes with the highest and the lowest binding affinities. (PDF 466 kb)

Residues contribution during the whole simulation process. The convergence analysis of energy contribution for residues involved in HA-ligand interaction. (PDF 153 kb)

InCoB2017 Best Paper Awards. (PDF 73Â kb)

Residues contribution during the whole simulation process. The convergence analysis of energy contribution for residues involved in HA-ligand interaction. (PDF 153 kb)

Superimpose the best and worst docked HA-ligand complexes. Visualize the structure of docked SH13-LSTa, SH13-LSTc, TW17-LSTa and TW17-LSTc complexes with the highest and the lowest binding affinities. (PDF 466 kb)

A. Trajectory of ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination I to PKCε and B. the respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 258 kb)

A. Trajectory of ∆ G MMGB SA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination II to PKA and B. The respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 255 kb)

A. Trajectory of ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination I to PKCε and B. the respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 258 kb)

A. Trajectory of ∆ G MMGB SA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination II to PKA and B. The respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 255 kb)

A. Trajectory of ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ of balanol analogues in charge state combination I to PKA and B. The respective correlation coefficients to experimental binding energy over 100 ns of MD simulations. Each data point in A was obtained from a 10-ns sliding window every 10 ns. Error bars in A were obtained from ∆ G MMGBSA ° $$ \Delta {G}_{MMGBSA}^{{}^{\circ}} $$ calculations of 100 snapshots within 10 ns trajectory. Error bars in B were derived from the errors of experimental K d values. (PDF 298 kb)

Throughout history, infectious diseases have posed a serious burden to mankind. More recently, there has been an alarming increase in drug-resistant microbes. Furthermore, new pathogens are emerging due to microbial evolution and adaptation. The spread of these diseases is a result of pathogen mutations and changes in human behavior patterns. Then, there are diseases that are lurking in the background, waiting for the right conditions before they strike again. In the war against these diseases, we have come to understand the behaviors of microbes in a heterogeneous world and the mechanisms governing disease transmission. These works have profoundly shaped modern knowledge of emerging and re-emerging infections. More recently, computational techniques have led the way into this new era by allowing rapid high-throughput analysis of pathogens which was previously not possible using traditional laboratory techniques. This chapter introduces methods in mathematical modeling, computational biology, and bioinformatics that have been used to study infectious diseases.

InCoB2016 Best Paper Awards (PDF 86Â kb)

List of InCoB2016 Reviewers (PDF 109Â kb)

Background: The blood-feeding hookworm Necator americanus infects hundreds of millions of people worldwide. In order to elucidate fundamental molecular biological aspects of this hookworm, the transcriptome of the adult stage of Necator americanus was explored using next-generation sequencing and bioinformatic analyses. Methodology/PrincipalFindings:A total of 19,997 contigs were assembled from the sequence data; 6,771 of these contigs had known orthologues in the free-living nematode Caenorhabditis elegans, and most of them encoded proteins with WD40 repeats (10.6%), proteinase inhibitors (7.8%) or calcium-binding EF-hand proteins (6.7%). Bioinformatic analyses inferred that the C. elegans homologues are involved mainly in biological pathways linked to ribosome biogenesis (70%), oxidative phosphorylation (63%) and/or proteases (60%); most of these molecules were predicted to be involved in more than one biological pathway. Comparative analyses of the transcriptomes of N. americanus and the canine hookworm, Ancylostoma caninum, revealed qualitative and quantitative differences. For instance, proteinase inhibitors were inferred to be highly represented in the former species, whereas SCP/Tpx-1/Ag5/PR-1/Sc7 proteins ( = SCP/TAPS or Ancylostoma-secreted proteins) were predominant in the latter. In N. americanus, essential molecules were predicted using a combination of orthology mapping and functional data available for C. elegans. Further analyses allowed the prioritization of 18 predicted drug targets which did not have homologues in the human host. These candidate targets were inferred to be linked to mitochondrial (e.g., processing proteins) or amino acid metabolism (e.g., asparagine t-RNA synthetase). Conclusions: This study has provided detailed insights into the transcriptome of the adult stage of N. americanus and examines similarities and differences between this species and A. caninum. Future efforts should focus on comparative transcriptomic and proteomic investigations of the other predominant human hookworm, A. duodenale, for both fundamental and applied purposes, including the prevalidation of anti-hookworm drug targets.

The order Strongylida (clade V) represents parasites most closely related to C. elegans, and the group most likely to benefit from the comparative value of its genome sequence. C. elegans has already served as an essential guide for exploring the genomes of other nematode species. However, transcriptomic data from parasitic nematode species, generated by EST approaches, have already served as a boon for C. elegans biology, as they confirm gene predictions and add depth to analyses of structure-function. Here, we propose sequencing the genomes of 20 species, members of the order Strongylida (‘strongylids’). Strongylids, including human hookworms and many intestinal parasites of livestock, are relatively closely related to one another and may have resulted from a recent radiation. To advance and facilitate molecular studies of strongylid parasites, an expressed sequence tag (EST)-based gene discovery program has been carried out, as a part of a broad study of the transcriptomes of members of the phylum Nematoda. More than 60,000 ESTs have been generated for key parasites of human and domestic animals, and for model parasitic species belonging to order Strongylida (comprising 83% of all clade V non-Caenorhabditis ESTs), and deposited in the public databases. Although the transcriptomic data hold promise for the development of novel control strategies for these important pathogens, emerging whole genome data will be pivotal in a more comprehensive approach to identify alternative control strategies, which is increasingly pressing as levels of parasite resistance to available drugs is rising, making some treatment strategies that worked just 5 years ago, ineffective. Also, some drug treatments and environmental nematocideecological contamination.

Background A wealth of quality genomic and mRNA/EST sequences in recent years has provided the data required for large-scale genome-wide analysis of alternative splicing. We have capitalized on this by constructing a database that contains alternative splicing information organized as splicing graphs, where all transcripts arising from a single gene are collected, organized and classified. The splicing graph then serves as the basis for the classification of the various types of alternative splicing events. Description DEDB http://proline.bic.nus.edu.sg/dedb/index.html is a database of Drosophila melanogaster exons obtained from FlyBase arranged in a splicing graph form that permits the creation of simple rules allowing for the classification of alternative splicing events. Pfam domains were also mapped onto the protein sequences allowing users to access the impact of alternative splicing events on domain organization. Conclusions DEDB's catalogue of splicing graphs facilitates genome-wide classification of alternative splicing events for genome analysis. The splicing graph viewer brings together genome, transcript, protein and domain information to facilitate biologists in understanding the implications of alternative splicing.