17 research outputs found
Highly significant improvement of protein sequence alignments with AlphaFold2
No full textData, figures and tables from the manuscript "Highly significant improvement of protein sequence alignments with AlphaFold2" (https://doi.org/10.1093/bioinformatics/btac625).
The repository containing all the steps to replicate the analysis is available at GitHub (https://github.com/cbcrg/msa-af2-nf).
*The authors Athanasios Baltzis and Leila Mansouri contributed equally
nf-core bytesize_proteinfold.mp4
No full textAthanasios Baltzis talks about the newest developments in the nf-core/proteinfold pipeline.</p
Impact of recent protein structure prediction methods on homology, evolutionary and functional inference
Get PDFRecent advances in deep learning techniques have revolutionised protein structure modelling. Since AlphaFold2’s release, a set of tools have now become available to predict native-like structures at near-experimental accuracy for a large fraction of the proteome. This massive amount of structural data is now powering every kind of biological inference requiring structural information. The work presented here features an exploration of the impact of experimental and predicted protein structural information onto homology, evolutionary and functional inference. The first part addresses the issue of accurate multiple sequence alignment (MSA) computation through a novel large-scale algorithmic approach and the systematic use of predicted structural information. In the second part, I explored the contribution of MSAs and structural information to refine phylogenetic and functional inference. On top of developing generic structure-based phylogeny reconstruction methods, I used RBM10, a well characterised splicing factor, as a showcase for the use of predicted structural information to support the inference of functional and phenotypic predictions, especially in the case of pathogenic mutations. The last part of this thesis presents a best-practice bioinformatics pipeline, nf-core/proteinfold, implemented using the Nextflow workflow management system and following nf-core guidelines. This pipeline was developed as a support for the rest of the projects in order to provide a solution to the need of high throughput structure predictions.Els avenços recents en tècniques de deep learning han revolucionat la modelització d'estructures de proteïnes. Desde el llançament d'AlphaFold2, hi ha disponibles un conjunt d'eines per preveure les estructures de forma nativa amb una precisió gairebé experimental per una gran part del proteoma. A dia d'avui, aquesta gran quantitat de data estructural està alimentant tot tipus de inferència biològica que requereix informació estructural. El treball que es presenta aquí conté una exploració de l'impacte de la informació estructural experimental i predictiva de la proteïna en la inferència de la homologia, l'evolució i la funció. La primera part resolt el problema de la computació precisa d'alineacions de seqüències múltiples (MSA) a través d'un nou enfocament algorítmic de gran escala i l'ús sistemàtic de informació estructural predictiva. En la segona part, exploro la contribució de MSAs i la informació estructural per refinar la inferència filogenètica i funcional. A més a més de desenvolupar mètodes genèrics de reconstrucció filogenètica basada en estructures, he utilitzat RBM10, un factor d'empalmament ben caracteritzat, com un exemple per l'ús d'informació estructural predictiva per recolzar la inferència de prediccions funcional i fenotípica, especialment en el cas de mutacions patogèniques. La última part d'aquesta tesis presenta un pipeline bioinformatic best-practise, nf-core/proteinfold, implementat utilitzant el sistema de gestió de fluxos de treball Nextflow i seguint les directrius de nf-core. Aquest pipeline ha sigut desenvolupat com un suport a la resta de projectes i per proveir una solució a la necessitat de prediccions estructurals de gran escala.Programa de doctorat en Biomedicin
Retraction IRAK1-independent pathways required for the interleukin-1-stimulated activation of the Tpl2 catalytic subunit and its dissociation from ABIN2
No full textVolume 424 (2009), pp. 109–118This paper is being retracted at the request of the authors. Three members of the laboratory of the last-named author have tried subsequently to reproduce the result reported in Figure 2B of the paper, but have been unable to do so. Consequently, the authors no longer consider the conclusion that interleukin-1 is able to activate the full-length catalytic subunit of the protein kinase Tpl2 when it is co-transfected into IRAK1-null HEK293 cells that stably express the interleukin-1 receptor to be correct. All of the authors, apart from the first author, have agreed to this retraction.Margaret J. Stafford, Eamon McManus, Dionissios Baltzis, Mark Peggie, Philip Cohen (MRC Protein Phosphorylation Unit, College of Life Sciences, University of Dundee, UK
Impact of recent protein structure prediction methods on homology, evolutionary and functional inference
No full textRecent advances in deep learning techniques have revolutionised protein structure modelling. Since AlphaFold2’s release, a set of tools have now become available to predict native-like structures at near-experimental accuracy for a large fraction of the proteome. This massive amount of structural data is now powering every kind of biological inference requiring structural information. The work presented here features an exploration of the impact of experimental and predicted protein structural information onto homology, evolutionary and functional inference. The first part addresses the issue of accurate multiple sequence alignment (MSA) computation through a novel large-scale algorithmic approach and the systematic use of predicted structural information. In the second part, I explored the contribution of MSAs and structural information to refine phylogenetic and functional inference. On top of developing generic structure-based phylogeny reconstruction methods, I used RBM10, a well characterised splicing factor, as a showcase for the use of predicted structural information to support the inference of functional and phenotypic predictions, especially in the case of pathogenic mutations. The last part of this thesis presents a best-practice bioinformatics pipeline, nf-core/proteinfold, implemented using the Nextflow workflow management system and following nf-core guidelines. This pipeline was developed as a support for the rest of the projects in order to provide a solution to the need of high throughput structure predictions.Els avenços recents en tècniques de deep learning han revolucionat la modelització d'estructures de proteïnes. Desde el llançament d'AlphaFold2, hi ha disponibles un conjunt d'eines per preveure les estructures de forma nativa amb una precisió gairebé experimental per una gran part del proteoma. A dia d'avui, aquesta gran quantitat de data estructural està alimentant tot tipus de inferència biològica que requereix informació estructural. El treball que es presenta aquí conté una exploració de l'impacte de la informació estructural experimental i predictiva de la proteïna en la inferència de la homologia, l'evolució i la funció. La primera part resolt el problema de la computació precisa d'alineacions de seqüències múltiples (MSA) a través d'un nou enfocament algorítmic de gran escala i l'ús sistemàtic de informació estructural predictiva. En la segona part, exploro la contribució de MSAs i la informació estructural per refinar la inferència filogenètica i funcional. A més a més de desenvolupar mètodes genèrics de reconstrucció filogenètica basada en estructures, he utilitzat RBM10, un factor d'empalmament ben caracteritzat, com un exemple per l'ús d'informació estructural predictiva per recolzar la inferència de prediccions funcional i fenotípica, especialment en el cas de mutacions patogèniques. La última part d'aquesta tesis presenta un pipeline bioinformatic best-practise, nf-core/proteinfold, implementat utilitzant el sistema de gestió de fluxos de treball Nextflow i seguint les directrius de nf-core. Aquest pipeline ha sigut desenvolupat com un suport a la resta de projectes i per proveir una solució a la necessitat de prediccions estructurals de gran escala.Programa de doctorat en Biomedicin
Επιρροή των σύγχρονων μεθόδων πρόβλεψης πρωτεϊνικών δομών στον προσδιορισμό ομολογίας, εξελικτικών σχέσεων και βιολογικής λειτουργίας
No full textRecent advances in deep learning techniques have revolutionised protein structure modelling. Since AlphaFold2’s release, a set of tools have now become available to predict native-like structures at near-experimental accuracy for a large fraction of the proteome. This massive amount of structural data is now powering every kind of biological inference requiring structural information. The work presented here features an exploration of the impact of experimental and predicted protein structural information onto homology, evolutionary and functional inference. The first part addresses the issue of accurate multiple sequence alignment (MSA) computation through a novel large-scale algorithmic approach and the systematic use of predicted structural information. In the second part, I explored the contribution of MSAs and structural information to refine phylogenetic and functional inference. On top of developing generic structure-based phylogeny reconstruction methods, I used RBM10, a well characterised splicing factor, as a showcase for the use of predicted structural information to support the inference of functional and phenotypic predictions, especially in the case of pathogenic mutations. The last part of this thesis presents a best-practice bioinformatics pipeline, nf-core/proteinfold, implemented using the Nextflow workflow management system and following nf-core guidelines. This pipeline was developed as a support for the rest of the projects in order to provide a solution to the need of high throughput structure predictions.Οι πρόσφατες εξελίξεις στις τεχνικές deep learning έχουν φέρει επανάσταση στη μοντελοποίηση της δομής των πρωτεϊνών. Αρχής γενομένης από την κυκλοφορία του AlphaFold2, έχει πλέον γίνει διαθέσιμο ένα σύνολο εργαλείων για την πρόβλεψη δομών που μοιάζουν με εγγενείς με σχεδόν πειραματική ακρίβεια για ένα μεγάλο μέρος του πρωτεώματος. Αυτή η τεράστια ποσότητα δομικών δεδομένων τροφοδοτεί τώρα κάθε είδους βιολογικό προσδιορισμό που απαιτεί δομικές πληροφορίες. Η εργασία που παρουσιάζεται εδώ περιλαμβάνει μια εξερεύνηση του αντίκτυπου των πειραματικών και προβλεπόμενων δομικών πληροφοριών πρωτεΐνης στην ομολογία, την εξελικτική και λειτουργική εξαγωγή συμπερασμάτων. Το πρώτο μέρος πραγματεύεται το ζήτημα του ακριβούς υπολογισμού της ευθυγράμμισης πολλαπλών αλληλουχιών (MSA) μέσω μιας νέας αλγοριθμικής προσέγγισης μεγάλης κλίμακας και της συστηματικής χρήσης προβλεπόμενων δομικών πληροφοριών. Στο δεύτερο μέρος, διερεύνησα τη συμβολή των MSA και των δομικών πληροφοριών για τη βελτίωση του φυλογενετικού και λειτουργικού προσδιορισμού. Εκτός από την ανάπτυξη γενικών μεθόδων αναδόμησης φυλογένεσης με βάση την πρωτεϊνική δομή, χρησιμοποίησα την πρωτεΐνη RBM10, έναν καλά χαρακτηρισμένο παράγοντα ματίσματος, ως ένα ενδεικτικό παράδειγμα για τη χρησιμότητα των δομικών πληροφοριών προερχόμενων από μοντελοποίηση για την υποστήριξη της εξαγωγής λειτουργικών και φαινοτυπικών προβλέψεων, ειδικά στην περίπτωση παθογόνων μεταλλάξεων. Το τελευταίο μέρος αυτής της διατριβής παρουσιάζει ένα πρόγραμμα βιοπληροφορικής βέλτιστων πρακτικών, nf-core/proteinfold, που υλοποιείται χρησιμοποιώντας το σύστημα διαχείρισης ροής εργασιών Nextflow και ακολουθώντας τις οδηγίες nf-core. Αυτό το πρόγραμμα αναπτύχθηκε ως υποστήριξη για τα υπόλοιπα έργα αυτής της διατριβής προκειμένου να δώσει λύση στην ανάγκη πρόβλεψης πρωτεϊνικών δομών υψηλής απόδοσης
Characterizing a partially ordered miniprotein through folding molecular dynamics simulations: Comparison with the experimental data
No full textReplication Data for "multistrap: boosting phylogenetic analyses with structural information"
No full textDataset for the replication of the analysis presented in "multistrap: boosting phylogenetic analyses with structural information".
Overview of the Content of the uploaded dataset and its Structure:
ids: Contains the full list of the identifiers of the datasets.
ids_titration: Contains the 56 identifiers used for the titration analysis.
pdb/: Contains all the PDB structures for each of the sequences of each dataset.
alignments/: Contains the untrimmed alignments produced with mTMalign, used for downstream analyses.
matrices/
{IMD,ME}/: Contains the distance matrices computed using the untrimmed mTMalign alignments.
/replicates: Contains 100 replicate distance matrices for each dataset for IMD.
trees/
{IMD,ME,ML}/: Trees computed with the tree method specified in the folder name.
/replicates: Contains the 100 replicate trees for each dataset.
auc_analysis/: Contains the files necessary to replicate the AUC analysis
Phylogenetic and functional characterization of water bears (Tardigrada) tubulins
No full textPreprin
Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
No full textMany fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package
