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In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens., Preservation of cancer biopsies by FFPE introduces DNA fragmentation, hindering analysis of rearrangements. Here the authors introduce FFPE Targeted Locus Capture for identification of translocations in preserved samples.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modelling process, and to the robustness of its scoring function. We participated in CAPRI both as server and as manual predictors.In CAPRI rounds 38-45, we have used various strategies depending on the information at hand. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement / CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall in the current rounds, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable models) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces.An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

Molecular modelling and simulations are nowadays an integral part of research in areas ranging from physics to chemistry to structural biology, as well as pharmaceutical drug design. This popularity is due to the development of high-performance hardware and of accurate and efficient molecular mechanics algorithms by the scientific community. These improvements are also benefitting scientific education. Molecular simulations, their underlying theory, and their applications are particularly difficult to grasp for undergraduate students. Having hands-on experience with the methods contributes to a better understanding and solidification of the concepts taught during the lectures. To this end, we have created a computer practical class, which has been running for the past five years, composed of several sessions where students characterize the conformational landscape of small peptides using molecular dynamics simulations in order to gain insights on their binding to protein receptors. In this report, we detail the ingredients and recipe necessary to establish and carry out this practical, as well as some of the questions posed to the students and their expected results. Further, we cite some examples of the students' written reports, provide statistics, and share their feedbacks on the structure and execution of the sessions. These sessions were implemented alongside a theoretical molecular modelling course but have also been used successfully as a standalone tutorial during specialized workshops. The availability of the material on our web page also facilitates this integration and dissemination and lends strength to the thesis of open-source science and education.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 A, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. We are most grateful to the PDBe at the European Bioinformatics Institute in Hinxton, UK, for hosting the CAPRI website. Our deepest thanks go to all the structural biologists and to the following structural genomics initiatives: Northeast Structural Genomics Consortium, Joint Center for Structural Genomics, NatPro PSI:Biology, New York Structural Genomics Research Center, Midwest Center for Structural Genomics, Structural Genomics Consortium, for contributing the targets for this joint CASP-CAPRI experiment. MFL acknowledges support from the FRABio FR3688 Research Federation “Structural & Functional Biochemistry of Biomolecular Assemblies.”

Background Enhancers, not promoters, are the most dynamic in their DNA methylation status throughout development and differentiation. Generally speaking, enhancers that are primed to or actually drive gene expression are characterized by relatively low levels of DNA methylation (hypo-methylation), while inactive enhancers display hyper-methylation of the underlying DNA. The direct functional significance of the DNA methylation state of enhancers is, however, unclear for most loci. Results In contrast to conventional epigenetic interactions at enhancers, we find that DNA methylation status and enhancer activity during early zebrafish development display very unusual correlation characteristics: hypo-methylation is a unique feature of primed enhancers whereas active enhancers are generally hyper-methylated. The hypo-methylated enhancers that we identify (hypo-enhancers) are enriched close to important transcription factors that act later in development. Interestingly, hypo-enhancers are de-methylated shortly before the midblastula transition and reside in a unique epigenetic environment. Finally, we demonstrate that hypo-enhancers do become active at later developmental stages and that they are physically associated with the transcriptional start site of target genes, irrespective of target gene activity. Conclusions We demonstrate that early development in zebrafish embodies a time window characterized by non-canonical DNA methylation–enhancer relationships, including global DNA hypo-methylation of inactive enhancers and DNA hyper-methylation of active enhancers. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1013-1) contains supplementary material, which is available to authorized users.

Advances in biophysics and biochemistry have pushed back the limits for the structural characterization of biomolecular assemblies. Large efforts have been devoted to increase both resolution and accuracy of the methods, probe into the smallest biomolecules as well as the largest macromolecular machineries, unveil transient complexes along with dynamic interaction processes, and, lately, dissect whole organism interactomes using high-throughput strategies. However, the atomic description of such interactions, rarely reached by large-scale projects in structural biology, remains indispensable to fully understand the subtleties of therecognitionprocess,measuretheimpactofamutationorpredicttheeffectofa drug binding to a complex. Mixing even a limited amount of experimental and/or bioinformatic data with modeling methods, such as macromolecular docking, presents a valuable strategy to predict the three-dimensional structures of complexes. Recent developments indicate that the docking community is seething to tackle the greatest challenge of adding the structural dimension to interactomes. C � 2011 John Wiley & Sons, Ltd.

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e. g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results. Molecular & Cellular Proteomics 9:1784-1794, 2010.

Self-assembly of the 40/42 amino acid A! peptide is a key player in Alzheimer's disease. A! 40 is the most prevalent species, while A! 42 is the most toxic. It has been suggested that the amino acids 21-30 could nucleate the fold- ing of A! monomer and a bent in this region could be the rate-limiting step in A! fibril formation. In this study, we review our current understanding of the computer-predicted conformations of amino acids 23-28 in the monomer of A! (21-30) and the monomers A! 40 and A! 42. On the basis of new simulations on dimers of full-length A! , we propose that the rate- limiting step involves the formation of a multimeric ! -sheet spanning the central hydrophobic core (residues 17-21).

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are difficult to study experimentally at the structural level because of the inherent flexibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplified by the recent introduction of protein-peptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high flexibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to define the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The flexibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fit molecular recognition theories.

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are difficult to study experimentally at the structural level because of the inherent flexibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplified by the recent introduction of protein-peptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high flexibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to define the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The flexibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fit molecular recognition theories.

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.

Alzheimer's, Parkinson's, and Cre- utzfeldt-Jakob's neurodegenerative diseases are all linked with the assembly of normally soluble pro- teins into amyloid fibrils. Because of experimental limitations, structural characterization of the solu- ble oligomers, which form early in the process of fibrillogenesis and are cytotoxic, remains to be determined. In this article, we study the aggregation paths of seven chains of the shortest amyloid-form- ing peptide, using an activitated method and a reduced atomic representation. Our simulations show that disordered KFFE monomers ultimately form three distinct topologies of similar energy: amorphous oligomers, incomplete rings with b-bar- rel character, and cross-b-sheet structures with the meridional but not the equatorial X-ray fiber reflec- tions. The simulations also shed light on the path- ways from misfolded aggregates to fibrillar-like structures. They also underline the multiplicity of building blocks that can lead to the formation of the critical nucleus from which rapid growth of the fibril occurs. Proteins 2006;65:180-191. V C 2006 Wiley

There is experimental evidence suggesting that the toxicity of neurodegenerative diseases such as Alzheimer’s disease may result from the soluble intermediate oligomers. It is therefore important to characterize extensively the early steps of oligomer formation at atomic level. As these structures are metastable and short lived, experimental data are difficult to obtain and they must be complemented with numerical simulations. In this work, we use the activation-relaxation technique coupled with a coarse-grained energy model to study in detail the mechanisms of aggregation of four lys‐phe‐phe‐glu ! KFFE" peptides. This is the shortest peptide known to form amyloid fibrils in vitro. Our simulations indicate that four KFFE peptides adopt a variety of oligomeric states ! tetramers, trimers, and dimers" with various orientations of the chains in rapid equilibrium. This conformational distribution is consistent with all-atom molecular-dynamics simulations in explicit solvent and is sequence dependent; as seen experimentally, the lys‐pro‐gly‐glu ! KPGE" peptides adopt disordered structures in solution. Our unbiased simulations also indicate that the assembly process is much more complex than previously thought and point to intermediate structures which likely are kinetic traps for longer chains. © 2005 American Institute of Physics. # DOI: 10.1063/1.1886725$

Amyloid fibril formation, as observed in Alzheimer's disease and type II diabetes, is currently described by a nucleation-condensation mechanism, but the details of the process preceding the formation of the nucleus are still lacking. In this study, using an activation-relaxation technique coupled to a generic energy model, we explore the aggregation pathways of 12 chains of the hexapeptide NFGAIL. The simulations show, starting from a preformed parallel dimer and ten disordered chains, that the peptides form essentially amorphous oligomers or more rarely ordered beta-sheet structures where the peptides adopt a parallel orientation within the sheets. Comparison between the simulations indicates that a dimer is not a sufficient seed for avoiding amorphous aggregates and that there is a critical threshold in the number of connections between the chains above which exploration of amorphous aggregates is preferred.