Dynamics of Ku and bacterial non-homologous end-joining characterized using single DNA molecule analysis

We use single-molecule techniques to characterize the dynamics of prokaryotic DNA repair by non-homologous end-joining (NHEJ), a system comprised only of the dimeric Ku and Ligase D (LigD). The Ku homodimer alone forms a ∼2 s synapsis between blunt DNA ends that is increased to ∼18 s upon addition of LigD, in a manner dependent on the C-terminal arms of Ku. The synapsis lifetime increases drastically for 4 nt complementary DNA overhangs, independently of the C-terminal arms of Ku. These observations are in contrast to human Ku, which is unable to bridge either of the two DNA substrates. We also demonstrate that bacterial Ku binds the DNA ends in a cooperative manner for synapsis initiation and remains stably bound at DNA junctions for several hours after ligation is completed, indicating that a system for removal of the proteins is active in vivo. Together these experiments shed light on the dynamics of bacterial NHEJ in DNA end recognition and processing. We speculate on the evolutionary similarities between bacterial and eukaryotic NHEJ and discuss how an increased understanding of bacterial NHEJ can open the door for future antibiotic therapies targeting this mechanism

Optimal anchoring of a foldamer inhibitor of ASF1 histone chaperone through backbone plasticity

Sequence-specific oligomers with predictable folding patterns, i.e., foldamers, provide new opportunities to mimic.-helical peptides and design inhibitors of protein-protein interactions. One major hurdle of this strategy is to retain the correct orientation of key side chains involved in protein surface recognition. Here, we show that the structural plasticity of a foldamer backbone may notably contribute to the required spatial adjustment for optimal interaction with the protein surface. By using oligoureas as. helix mimics, we designed a foldamer/peptide hybrid inhibitor of histone chaperone ASF1, a key regulator of chromatin dynamics. The crystal structure of its complex with ASF1 reveals a notable plasticity of the urea backbone, which adapts to the ASF1 surface to maintain the same binding interface. One additional benefit of generating ASF1 ligands with nonpeptide oligourea segments is the resistance to proteolysis in human plasma, which was highly improved compared to the cognate alpha-helical peptide

EDS1 complexes are not required for PRR responses and execute TNL‐ETI from the nucleus in Nicotiana benthamiana

Heterodimeric complexes incorporating the lipase-like proteins EDS1 with PAD4 or SAG101 are central hubs in plant innate immunity. EDS1 functions encompass signal relay from TIR domain-containing intracellular NLR-type immune receptors (TNLs) towards RPW8-type helper NLRs (RNLs) and, in Arabidopsis thaliana, bolstering of signaling and resistance mediated by cell-surface pattern recognition receptors (PRRs). Increasing evidence points to the activation of EDS1 complexes by small molecule binding. We used CRISPR/Cas-generated mutant lines and agroinfiltration-based complementation assays to interrogate functions of EDS1 complexes in Nicotiana benthamiana. We did not detect impaired PRR signaling in N. benthamiana lines deficient in EDS1 complexes or RNLs. Intriguingly, in assays monitoring functions of SlEDS1-NbEDS1 complexes in N. benthamiana, mutations within the SlEDS1 catalytic triad could abolish or enhance TNL immunity. Furthermore, nuclear EDS1 accumulation was sufficient for N. benthamiana TNL (Roq1) immunity. Reinforcing PRR signaling in Arabidopsis might be a derived function of the TNL/EDS1 immune sector. Although Solanaceae EDS1 functionally depends on catalytic triad residues in some contexts, our data do not support binding of a TNL-derived small molecule in the triad environment. Whether and how nuclear EDS1 activity connects to membrane pore-forming RNLs remains unknown

Structural prediction of protein interactions and docking using conservation and coevolution

Place: Hoboken Publisher: Wiley WOS:000529748800001Knowledge of the detailed structure of macromolecular interactions is key to a better understanding and modulation of essential cellular functions and pathological situations. Great efforts are invested in the development of improved computational prediction methods, including binding site prediction and protein-protein docking. These tools should benefit from the inclusion of evolutionary information, since the pressure to maintain functional interactions leads to conservation signals on protein surfaces at interacting sites and coevolution between contacting positions. However, unveiling such constraints and finding the best way to integrate them into computational pipelines remains a challenging area of research. Here, we first introduce evolutionary properties of interface structures, focusing on recent work exploring evolutionary mechanisms at play in protein interfaces and characterizing the complexity of evolutionary signals, such as interface deep mutational scans. Then, we review binding site predictors and interface structure modeling methods that integrate conservation and coevolution as important ingredients to improve predictive capacity, ending with a section dedicated to the prediction of binding modes between a globular protein domain and a short motif located within an intrinsically disordered or flexible region. Throughout the review, we discuss practical applications and recent promising developments, in particular regarding the use of coevolutionary information for interface structural prediction and the integration of these coevolution signals with machine learning and deep learning algorithms. This article is categorized under: Structure and Mechanism \textgreater Molecular Structures Structure and Mechanism \textgreater Computational Biochemistry and Biophysics Molecular and Statistical Mechanics \textgreater Molecular Interaction

HHalign-Kbest: exploring sub-optimal alignments for remote homology comparative modeling.

International audienceThe HHsearch algorithm, implementing a hidden Markov model (HMM)-HMM alignment method, has shown excellent alignment performance in the so-called twilight zone (target-template sequence identity with ∼20%). However, an optimal alignment by HHsearch may contain small to large errors, leading to poor structure prediction if these errors are located in important structural elements.HHalign-Kbest server runs a full pipeline, from the generation of suboptimal HMM-HMM alignments to the evaluation of the best structural models. In the HHsearch framework, it implements a novel algorithm capable of generating k-best HMM-HMM suboptimal alignments rather than only the optimal one. For large proteins, a directed acyclic graph-based implementation reduces drastically the memory usage. Improved alignments were systematically generated among the top k suboptimal alignments. To recognize them, corresponding structural models were systematically generated and evaluated with Qmean score. The method was benchmarked over 420 targets from the SCOP30 database. In the range of HHsearch probability of 20-99%, average quality of the models (TM-score) raised by 4.1-16.3% and 8.0-21.0% considering the top 1 and top 10 best models, respectively.http://bioserv.rpbs.univ-paris-diderot.fr/services/HHalign-Kbest/ (source code and server)[email protected] data are available at Bioinformatics online

Atomic-level evolutionary information improves protein-protein interface scoring

International audienceMotivation: The crucial role of protein interactions and the difficulty in characterising them experimentally strongly motivates the development of computational approaches for structural prediction. Even when protein-protein docking samples correct models, current scoring functions struggle to discriminate them from incorrect decoys. The previous incorporation of conservation and coevolution information has shown promise for improving protein-protein scoring. Here, we present a novel strategy to integrate atomic-level evolutionary information into different types of scoring functions to improve their docking discrimination.Results: We applied this general strategy to our residue-level statistical potential from InterEvScore and to two atomic-level scores, SOAP-PP and Rosetta interface score (ISC). Including evolutionary information from as few as ten homologous sequences improves the top 10 success rates of individual atomic-level scores SOAP-PP and Rosetta ISC by respectively 6 and 13.5 percentage points, on a large benchmark of 752 docking cases. The best individual homology-enriched score reaches a top 10 success rate of 34.4%. A consensus approach based on the complementarity between different homology-enriched scores further increases the top 10 success rate to 40%.Availability: All data used for benchmarking and scoring results, as well as a Singularity container of the pipeline, are available at http://biodev.cea.fr/interevol/interevdata/Contact: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online

Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP

In S. cerevisiae, the Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homologue with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling, biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, validating the structural model. Last, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN

Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.

<p>This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.</p><p>It contains two archives: </p><ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol><p>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a></p><h4><strong>Dataset of 42 non redundant complexes</strong></h4><p>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> contains 3 compressed directories and a README file detailing their contents :</p><ul><li>the initial raw sequence and alignment data for every chain        -> DIRECTORY <strong>fasta_msa/</strong></li><li>the input and output data of every Alphafold run for every complex   -> DIRECTORY <strong>af2_runs/</strong></li><li>the native reference structures    -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <i><index>_<pdbcode></i>.</p><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.2</i></p><h4><strong>Dataset of 923 complexes selected from the ELM database</strong></h4><p>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.</p><p>Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein.  </p><p>The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:</p><ul><li>the table describing ELM entries -> FILE <strong>Table_923ELM_uid_delimitations_info_for_archive.txt</strong></li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain        -> DIRECTORY <strong>fasta_msa/</strong></li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY <strong>af2_elm_coali_inputs/</strong></li><li>the best model of every AF2 protocol for every complex according to the AF2   -> DIRECTORY <strong>af2_elm_models/</strong></li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY <strong>elm_cut_models/</strong></li><li>the reference structures used for the evaluation of the models   -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.3</i></p><p>In the version 1.1.0, in the scanidr_data_repository_corr6J08.tar archive, a correction has been made to case 36_6J08. The correct isoform Q3KP22-3 has been used for this case, instead of the previously incorrectly used Q3KP22-1.</p&gt

Automated classification of tailed bacteriophages according to their neck organization

The genetic diversity observed among bacteriophages remains a major obstacle for the identification of homologs and the comparison of their functional modules. In the structural module, although several classes of homologous proteins contributing to the head and tail structure can be detected, proteins of the head-to-tail connection (or neck) are generally more divergent. Yet, molecular analyses of a few tailed phages belonging to different morphological classes suggested that only a limited number of structural solutions are used in order to produce a functional virion. To challenge this hypothesis and analyze proteins diversity at the virion neck, we developed a specific computational strategy to cope with sequence divergence in phage proteins. We searched for homologs of a set of proteins encoded in the structural module using a phage learning database. Results: We show that using a combination of iterative profile-profile comparison and gene context analyses, we can identify a set of head, neck and tail proteins in most tailed bacteriophages of our database. Classification of phages based on neck protein sequences delineates 4 Types corresponding to known morphological subfamilies. Further analysis of the most abundant Type 1 yields 10 Clusters characterized by consistent sets of head, neck and tail proteins. We developed Virfam, a webserver that automatically identifies proteins of the phage head-neck-tail module and assign phages to the most closely related cluster of phages. This server was tested against 624 new phages from the NCBI database. 93% of the tailed and unclassified phages could be assigned to our head-neck-tail based categories, thus highlighting the large representativeness of the identified virion architectures. Types and Clusters delineate consistent subgroups of Caudovirales, which correlate with several virion properties. Conclusions: Our method and webserver have the capacity to automatically classify most tailed phages, detect their structural module, assign a function to a set of their head, neck and tail genes, provide their morphologic subtype and localize these phages within a "head-neck-tail" based classification. It should enable analysis of large sets of phage genomes. In particular, it should contribute to the classification of the abundant unknown viruses found on assembled contigs of metagenomic samples