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Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK
[chapter]
2017
Msphere
Preserved Fulltext
Geng acknowledges financial support from the China Scholarship Council, grant NO. 201406220132. This protocol is adapted from a computer practical offered to our chemistry bachelor students [33] . ...
Geng et al. ...
runX directory and restart HADDOCK: file.cns
-> file.cns_clustX_best#
file.list
-> file.list_clustX_best#
file.nam
-> file.nam_clustX_best#
analysis
-> analysis_clustX_best#
> haddock2.2
Cunliang ...
doi:10.1007/978-1-4939-6798-8_8
pmid:28236236
fatcat:zeqm6y3pc5hbtlqencdlmwavv4
iScore: A novel graph kernel-based function for scoring protein-protein docking models
[article]
2018
bioRxiv
pre-print
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ABSTRACTProtein complexes play a central role in many aspects of biological function. Knowledge of the three-dimensional (3D) structures of protein complexes is critical for gaining insights into the structural basis of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determination of 3D structures of protein complexes, computational docking has evolved as a valuable tool to predict
doi:10.1101/498584
fatcat:wwt674vqjnfpdhygedlv3vwz5y
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... he 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to that of the state-of-the-art scoring functions on independent data sets consisting docking software-specific data sets and the CAPRI score set built from a wide variety of docking approaches. iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary and topological, and physicochemical information for scoring docked conformations. This work represents the first successful demonstration of graph kernel to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. It paves the way for the further development of computational methods for predicting the structure of protein complexes.
DeepRank: A deep learning framework for data mining 3D protein-protein interfaces
[article]
2021
bioRxiv
pre-print
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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).
doi:10.1101/2021.01.29.425727
fatcat:nubuph4jlveftohrx3v2c2q3ci
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... k 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 or outperforms state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.
The pdb2sql Python Package: Parsing, Manipulation and Analysis of PDB Files Using SQL Queries
2020
Journal of Open Source Software
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This is, for example, the case of iScore (Geng et al., 2019) , which uses graph kernels and support vector machines to rank protein-protein interfaces. ...
doi:10.21105/joss.02077
fatcat:wpe4l6uvafbojcmvy4cygalxau
iSEE: Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations
[article]
2018
bioRxiv
pre-print
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S1
Supporting material
for
iSEE: Interface Structure, Evolution and Energy-based machine
learning predictor of binding affinity changes upon mutations
Cunliang Geng a , Anna Vangone a , Gert E ...
Geng C, Vangone A, Bonvin AMJJ. Exploring the interplay between experimental
methods and the performance of predictors of binding affinity change upon mutations in
protein complexes. ...
doi:10.1101/331280
fatcat:iffadwdasfaprok56vmguq4dyu
matchms - processing and similarity evaluation of mass spectrometry data
[article]
2020
biorxiv/medrxiv
pre-print
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Mass spectrometry data is at the heart of numerable applications in the biomedical and life sciences. With growing use of high throughput techniques researchers need to analyse larger and more complex datasets. In particular through joint effort in the research community, fragmentation mass spectrometry datasets are growing in size and number. Matchms is an open-access Python package to import, process, clean, and compare mass spectrometry data (MS/MS). It allows to implement and run an
doi:10.1101/2020.08.06.239244
fatcat:folnxbg2t5aqhf32pcf3mln5b4
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... follow, easy-to-reproduce workflow from raw mass spectra to pre- and post-processed spectral data.
Deep-learning enhancement of large scale numerical simulations
[article]
2020
arXiv
pre-print
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Traditional simulations on High-Performance Computing (HPC) systems typically involve modeling very large domains and/or very complex equations. HPC systems allow running large models, but limits in performance increase that have become more prominent in the last 5-10 years will likely be experienced. Therefore new approaches are needed to increase application performance. Deep learning appears to be a promising way to achieve this. Recently deep learning has been employed to enhance solving
arXiv:2004.03454v1
fatcat:l4vs2ham6ngdjdvio66uieb5my
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... blems that traditionally are solved with large-scale numerical simulations using HPC. This type of application, deep learning for high-performance computing, is the theme of this whitepaper. Our goal is to provide concrete guidelines to scientists and others that would like to explore opportunities for applying deep learning approaches in their own large-scale numerical simulations. These guidelines have been extracted from a number of experiments that have been undertaken in various scientific domains over the last two years, and which are described in more detail in the Appendix. Additionally, we share the most important lessons that we have learned.
iScore: A novel graph kernel-based function for scoring protein-protein docking models
2019
Bioinformatics
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Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular
doi:10.1093/bioinformatics/btz496
pmid:31199455
pmcid:PMC6956772
fatcat:jejx6kx7xjanhpoakh5sunmsi4
more »
... mplexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). Supplementary data are available at Bioinformatics online.
An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45
[article]
2019
biorxiv/medrxiv
pre-print
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ORCID Cunliang Geng https://orcid.org/0000-0002-1409-8358 Alexandre M. J. J. Bonvin https://orcid.org/0000-0001-7369-1322 ( 10 000/400/400 instead of the default 1000/200/200 models). ...
doi:10.1101/718122
fatcat:qvpwweci7rfcxa6dra5kaewk44
iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines
[article]
2019
bioRxiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph
doi:10.1101/788166
fatcat:c5l7fiivp5debfp4xnznznkygy
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... imilarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automatize the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.
matchms - processing and similarity evaluation of mass spectrometry data
2020
Journal of Open Source Software
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N-Glycoform Diversity of Cellobiohydrolase I fromPenicillium decumbensand Synergism of Nonhydrolytic Glycoform in Cellulose Degradation
2012
Journal of Biological Chemistry
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The information on the role of N-glycosylation is limited. Results: The site and structure of N-glycosylation have evident effects on the activity and stability of CBH. Conclusion: N-glycosylation affects the characteristics of CBHI, and also brings a new function to CBHI as a nonenzymatic synergism factor. Significance: Understanding the effects of N-glycosylation is important for improvement of enzyme technology. SUMMARY Four cellobiohydrolase I (CBHI) glycoforms, namely, CBHI-A, CBHI-B,
doi:10.1074/jbc.m111.332890
pmid:22427663
pmcid:PMC3346090
fatcat:pyuolxmxdna2zowfqsaq6fyiwy
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... C, and CBHI-D, were purified from the cultured broth of Penicillium decumbens JU-A10. All glycoforms had the same amino acid sequence, but displayed different characteristics and biological functions. The effects of the N-glycans of the glycoforms on CBH activity were analyzed using mass spectrum data. Longer N-glycan chains at the Asn-137 of CBHI increased CBH activity. After the N-glycans were removed using site-directed mutagenesis and homologous expression in P. decumbens, the specific CBH activity of the recombinant CBHI without N-glycosylation increased by 65% compared with the wild-type CBHI with the highest specific activity. However, the activity was not stable. Only the N-glycosylation at Asn-137 can improve CBH activity by 40%. rCBHI with N-glycosylation only at Asn-470 exhibited no enzymatic activity. CBH activity was affected whether or not the protein was glycosylated, together with N-glycosylation site and N-glycan structure. N-glycosylation not only affects CBH activity but may also bring a new feature to a nonhydrolytic CBHI glycoform (CBHI-A). By supplementing CBHI-A to different commercial cellulase preparations, the glucose yield of lignocellulose hydrolysis increased by > 20%. After treatment with a low dose (5 mg/g substrate) of CBHI-A at 50 °C for seven days, the hydrogen-bond intensity and crystalline degree of cotton fibers decreased by 17% and 34%, respectively. These results may provide new guidelines for cellulase engineering.
iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations
2018
Proteins: Structure, Function, and Bioinformatics
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Cunliang Geng https://orcid.org/0000-0002-1409-8358 Anna Vangone https://orcid.org/0000-0003-2485-7378 Li C. Xue https://orcid.org/0000-0002-2613-538X Alexandre M. J. J. ...
doi:10.1002/prot.25630
pmid:30417935
fatcat:6v4lvtepzzcgpa3m72vh6m7oue
Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes
2016
Protein Engineering Design & Selection
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Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Engineering, Design, and Selection, 29, 291-299 (2016). Abstract Reliable prediction of binding affinity changes (∆∆G) upon mutations in protein complexes relies not only on the performance of computational methods but also on the availability and quality of experimental data. Binding affinity changes can be measured by various
doi:10.1093/protein/gzw020
pmid:27284087
fatcat:q2qsuzsl35bordhybx3rwjdbhi
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... l methods with different accuracies and limitations. To understand the impact of these on the prediction of binding affinity change, we present the Database of binding Affinity Change Upon Mutation (DACUM), a database of 1872 binding affinity changes upon single point mutations, a subset of the SKEMPI database (Moal and Fernández-Recio, 2012) extended with information on the experimental methods used for ∆∆G measurements. The ∆∆G data were classified into different datasets based on the experimental method used and the position of the mutation (interface and non-interface). We tested the prediction performance of the original HADDOCK score, a newly trained version of it and mCSM (Pires et al., 2014), one of the best reported ∆∆G predictor so far, on these various datasets. Our results demonstrate a strong impact of the experimental methods on the performance of binding affinity change predictors for protein complexes. This underscores the importance of properly considering and carefully choosing experimental methods in the development of novel binding affinity change predictors. The DACUM database is available online at https://github.com/haddocking/DACUM. Keywords: binding affinity/computational prediction/experimental methods/protein-protein interactions/singe point mutation
iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines
2020
SoftwareX
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Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph
doi:10.1016/j.softx.2020.100462
pmid:35419466
pmcid:PMC9005067
fatcat:qbxbq627wjdilf4vzhdza3auuy
more »
... imilarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.
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