DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology

DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology

PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes

Deeper understanding of T-cell-mediated adaptive immune responses is important for the design of cancer immunotherapies and antiviral vaccines against pandemic outbreaks. T-cells are activated when they recognize foreign peptides that are presented on the cell surface by Major Histocompatibility Complexes (MHC), forming peptide:MHC (pMHC) complexes. 3D structures of pMHC complexes provide fundamental insight into T-cell recognition mechanism and aids immunotherapy design. High MHC and peptide diversities necessitate efficient computational modelling to enable whole proteome structural analysis. We developed PANDORA, a generic modelling pipeline for pMHC class I and II (pMHC-I and pMHC-II), and present its performance on pMHC-I here. Given a query, PANDORA searches for structural templates in its extensive database and then applies anchor restraints to the modelling process. This restrained energy minimization ensures one of the fastest pMHC modelling pipelines so far. On a set of 835 pMHC-I complexes over 78 MHC types, PANDORA generated models with a median RMSD of 0.70 Å and achieved a 93% success rate in top 10 models. PANDORA performs competitively with three pMHC-I modelling state-of-the-art approaches and outperforms AlphaFold2 in terms of accuracy while being superior to it in speed. PANDORA is a modularized and user-configurable python package with easy installation. We envision PANDORA to fuel deep learning algorithms with large-scale high-quality 3D models to tackle long-standing immunology challenges

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)