How sticky are our proteins? Quantifying hydrophobicity of the human proteome

Gils, Juami H. M. van; Gogishvili, Dea; Eck, Jan van; Bouwmeester, Robbin; Dijk, Erik van; Abeln, Sanne

doi:10.1093/bioadv/vbac002

Cited by 15 publications

(18 citation statements)

References 60 publications

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“…These results suggest that the brain is especially prone to such diseases due to the high expression of proteins with a large hydrophobic surface. Due to their major functional role of large “sticky” patches in protein–protein interaction and antibody binding, insights and tools provided by this study are especially valuable for investigating novel biomarkers and consequent assay development ( 54 ).…”

Section: Miriade Esr Processes In Biomarker Discovery and Developmentmentioning

confidence: 99%

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

et al. 2022

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Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.

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Section: Miriade Esr Processes In Biomarker Discovery and Developmentmentioning

confidence: 99%

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

et al. 2022

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“…Area under the receiver operating characteristic curve (AUC ROC) is used for measuring model performance on the EPI prediction task. Hydrophobic patch prediction (HPR) The hydrophobic patch dataset contains the structure-based assignment of hydrophobic patches, as created by Van Gils et al 22 . PISCES was used to collect PDB structure of a resolution 3.0 Å, R-factor 0.3, and of sequence length between 40–10 000 residues.…”

Section: Proteinglue Benchmark Tasksmentioning

confidence: 99%

“…The resulting set contains 4 917 proteins. Because the model is unable to predict the total hydrophobic surface area, MolPatch 22 was used to generate, per protein, the rank of each hydrophobic patch. For amino acids belonging to multiple patches the rank of the largest patch was assigned.…”

Section: Proteinglue Benchmark Tasksmentioning

confidence: 99%

“…The PPI and EPI prediction tasks are expected to be ‘ medium ’ difficult 18 , 20 . The hydrophobic patch prediction is expected to be a hard prediction task 22 . The SS3 and SS8 tasks are naturally related, as are BUR and ASA; also between these pairs we expect some correlation, as for example loops tend to be exposed 1 .…”

Section: Proteinglue Benchmark Tasksmentioning

confidence: 99%

“…Hydrophobic residues on the surface of the protein may be important for the interaction between proteins, or for membrane interactions, and have been implicated as being a driving factor in protein aggregation 21 . Protein aggregation in turn is thought to be a major causative factor in the development of diseases like Alzheimer and Parkinson 22 . Hydrophobic patch prediction may be modeled as a regression task over every amino acid by identifying the rank of the size of the hydrophobic patch to which the amino acid belongs.…”

Section: Introductionmentioning

confidence: 99%

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ProteinGLUE multi-task benchmark suite for self-supervised protein modeling

Capel

Weiler

Dijkstra

et al. 2022

Sci Rep

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Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger model does not outperform the smaller model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue.

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Electric Field Cycling of Physisorbed Antibodies Reduces Biolayer Polarization Dispersion

Di Franco,

Macchia,

Catacchio

et al. 2024

Advanced Science

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The electric dipoles of proteins in a biolayer determine their dielectric properties through the polarization density P. Hence, its reproducibility is crucial for applications, particularly in bioelectronics. Biolayers encompassing capturing antibodies covalently bound at a biosensing interface are generally preferred for their assumed higher stability. However, surface physisorption is shown to offer advantages like easily scalable fabrication processes and high stability. The present study investigates the effects of electric‐field (EF)‐cycling of anti‐Immunoglobulin M (anti‐IgM) biolayers physisorbed on Au. The impact of EF‐cycling on the dielectric, optical, and mechanical properties of anti‐IgM biolayer is investigated. A reduction of the dispersion (standard deviation over a set of 31 samples) of the measured P values is observed, while the set median stays almost constant. Hence, physisorption combined with EF cycling, results in a biolayer with highly reproducible bioelectronic properties. Additionally, the study provides important insights into the mechanisms of dielectric rearrangement of dipole moments in capturing biolayers after EF‐cycling. Notably, EF‐cycling acts as an annealing process, driving the proteins in the biolayer into a statistically more probable and stable conformational state. Understanding these phenomena enhances the knowledge of the properties of physisorbed biolayers and can inform design strategies for bioelectronic devices.

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How sticky are our proteins? Quantifying hydrophobicity of the human proteome

Cited by 15 publications

References 60 publications

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

ProteinGLUE multi-task benchmark suite for self-supervised protein modeling

Electric Field Cycling of Physisorbed Antibodies Reduces Biolayer Polarization Dispersion

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