Wouter Boomsma
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View article: The importance of UBQLN2 ubiquitylation for its turnover and localization
The importance of UBQLN2 ubiquitylation for its turnover and localization Open
UBQLN2 is a member of the UBL-UBA domain protein family that functions as extrinsic substrate receptors for the 26S proteasome. UBQLN2 has been shown to undergo phase separation in vitro. In cells, UBQLN2 forms condensates that may be of i…
View article: Supervised Learning of Protein Melting Temperature: Cross‐Species vs. Species‐Specific Prediction
Supervised Learning of Protein Melting Temperature: Cross‐Species vs. Species‐Specific Prediction Open
Protein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets of melting temperatu…
View article: Gene finding revisited: improved robustness through structured decoding from learned embeddings
Gene finding revisited: improved robustness through structured decoding from learned embeddings Open
Gene finding is the task of identifying the locations of coding sequences within the vast amount of genetic code contained in the genome. With an ever increasing quantity of raw genome sequences, gene finding is an important avenue towards…
View article: Foundation models of protein sequences: A brief overview
Foundation models of protein sequences: A brief overview Open
Protein sequence models have evolved from simple statistics of aligned families to versatile foundation models of evolutionary scale. Enabled by self-supervised learning and an abundance of protein sequence data, such foundation models now…
View article: SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions
SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions Open
The ability to predict how amino acid changes affect proteins has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in either prote…
View article: Supervised learning of protein melting temperatures: cross-species vs species-specific prediction
Supervised learning of protein melting temperatures: cross-species vs species-specific prediction Open
A bstract Protein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets of melting…
View article: Probabilistic Multiple Sequence Alignment using Spatial Transformations
Probabilistic Multiple Sequence Alignment using Spatial Transformations Open
A bstract Multiple Sequence Alignment (MSA) has long been a prominent and critical tool in bioinformatics and computational biology. Its importance lies in its ability to provide valuable insights into the relationships between sequences a…
View article: Kermut: Composite kernel regression for protein variant effects
Kermut: Composite kernel regression for protein variant effects Open
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty est…
View article: A systematic analysis of regression models for protein engineering
A systematic analysis of regression models for protein engineering Open
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimizat…
View article: A Continuous Relaxation for Discrete Bayesian Optimization
A Continuous Relaxation for Discrete Bayesian Optimization Open
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can b…
View article: Kermut: Composite kernel regression for protein variant effects
Kermut: Composite kernel regression for protein variant effects Open
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty est…
View article: Deep learning assisted single particle tracking for automated correlation between diffusion and function
Deep learning assisted single particle tracking for automated correlation between diffusion and function Open
Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. How…
View article: A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences Open
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these …
View article: A joint embedding of protein sequence and structure enables robust variant effect predictions
A joint embedding of protein sequence and structure enables robust variant effect predictions Open
The ability to predict how amino acid changes may affect protein function has a wide range of applications including in disease variant classification and protein engineering. Many existing methods focus on learning from patterns found in …
View article: BEND: Benchmarking DNA Language Models on biologically meaningful tasks
BEND: Benchmarking DNA Language Models on biologically meaningful tasks Open
The genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of the various functional, non-coding and regulatory element…
View article: Deep learning assisted single particle tracking for automated correlation between diffusion and function
Deep learning assisted single particle tracking for automated correlation between diffusion and function Open
Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. How…
View article: Implicit Variational Inference for High-Dimensional Posteriors
Implicit Variational Inference for High-Dimensional Posteriors Open
In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex m…
View article: Phosphorylation of <i>Schizosaccharomyces pombe</i> Dss1 mediates direct binding to the ubiquitin‐ligase Dma1 in vitro
Phosphorylation of <i>Schizosaccharomyces pombe</i> Dss1 mediates direct binding to the ubiquitin‐ligase Dma1 in vitro Open
Intrinsically disordered proteins (IDPs) are often multifunctional and frequently posttranslationally modified. Deleted in split hand/split foot 1 (Dss1—Sem1 in budding yeast) is a highly multifunctional IDP associated with a range of prot…
View article: FLOP: Tasks for Fitness Landscapes Of Protein wildtypes
FLOP: Tasks for Fitness Landscapes Of Protein wildtypes Open
Protein engineering has the potential to create optimized protein variants with improved properties and function. An initial step in the protein optimization process typically consists of a search among natural (wildtype) sequences to find…
View article: Assessing the performance of protein regression models
Assessing the performance of protein regression models Open
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimizat…
View article: Rapid protein stability prediction using deep learning representations
Rapid protein stability prediction using deep learning representations Open
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and ac…
View article: Author response: Rapid protein stability prediction using deep learning representations
Author response: Rapid protein stability prediction using deep learning representations Open
Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract Predicting the thermo…
View article: Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters Open
After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of structural states. Parametric models of fluctuations are difficu…
View article: MembraneFold: Visualising transmembrane protein structure and topology
MembraneFold: Visualising transmembrane protein structure and topology Open
Background AlphaFold’s accuracy, which is often comparable to that of experimentally determined structures, has revolutionized protein structure research. Being a statistical method, AlphaFold implicitly infers the cellular environment, e.…
View article: Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components
Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components Open
In terms of its relative frequency, lysine is a common amino acid in the human proteome. However, by bioinformatics we find hundreds of proteins that contain long and evolutionarily conserved stretches completely devoid of lysine residues.…
View article: Rapid protein stability prediction using deep learning representations
Rapid protein stability prediction using deep learning representations Open
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and ac…
View article: Learning meaningful representations of protein sequences
Learning meaningful representations of protein sequences Open
How we choose to represent our data has a fundamental impact on our ability to subsequently extract information from them. Machine learning promises to automatically determine efficient representations from large unstructured datasets, suc…