Biology subjects

Boomsma, W.

Publications and source records attributed to Boomsma, W..

14 recordsLinked to original sources

bioRxiv2026

APOSM: Pairwise preference learning improves generative small-molecule design

Small-molecule lead refinement is constrained by the cost of synthesizing and assaying candidates, making the surrogate models that prioritize compounds for experimental testing central to the design process. The reliability of such surrogates is limited by the noise and sparsity of screening measurements. We show that training the surrogate on pairwise comparisons between candidate molecules, rather than on absolute predicted scores, yields a substantially more reliable signal for active candidate selection in this regime. We develop APOSM, an active-learning algorithm that combines a fragment-based generator, a pairwise message-passing graph neural network surrogate, and probabilistic ranking inside a batched acquisition loop. On the Practical Molecular Optimization benchmark and a GPCR ligand rediscovery task, APOSM improves target attainment and sampling efficiency over unguided fragment-based optimization, the Graph-GA genetic algorithm, and a pointwise-regression ablation, with the largest gains on tasks where absolute scores are hardest to calibrate.

bioinformatics↗

bioRxiv2025

The importance of UBQLN2 ubiquitylation for its turnover and localization

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 importance for tuning protein degradation via the ubiquitin-proteasome system and potentially of relevance for UBQLN2-linked amyotrophic lateral sclerosis (ALS). Here we show that UBQLN2 is ubiquitylated on lysine residues in the N-terminal UBL domain. The C-terminal region of UBQLN2 is lysine-depleted, and we show that introducing lysine residues in this region leads to its E6AP-dependent degradation. The UBL domain critically stabilizes UBQLN2 and protects it from proteasomal degradation. Fusion of ubiquitin to the UBQLN2 N-terminus stabilizes UBQLN2 and increases its propensity for locating in puncta, indicating that ubiquitylation of the UBQLN2 UBL domain regulates abundance and localization.

biochemistry↗

bioRxiv2024

Probabilistic Multiple Sequence Alignment using Spatial Transformations

AO_SCPLOWBSTRACTC_SCPLOWMultiple 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 and the evolutionary pressure leading to amino acid preferences at particular sites in a protein. Despite the recent advances in protein language models, MSAs remain critical in many applications, e.g. for state-of-the-art prediction of 3D structure and protein variant effects. Sequence alignment is typically considered a deterministic preprocessing step, leading to a single static MSA. Especially for low-similarity sequences, parts of an alignment will be subject to substantial uncertainty, which is disregarded when processing a static MSA. Earlier, HMM-based approaches handled this uncertainty by considering the full posterior ensemble over alignments. In this paper, we explore whether a similar approach is feasible within a modern deep learning approach, where we move beyond the Markovian restrictions of earlier models. In particular, we consider whether we can learn the alignment process as distribution over spatial transformations, in combination with a deep latent variable model of protein sequences. A proof-of-concept implementation of this work is available at https://github.com/deltadedirac/Explicit_Disentanglement_Molecules.

bioinformatics↗

bioRxiv2024

Cross-species vs species-specific models for protein melting temperature prediction

AO_SCPLOWBSTRACTC_SCPLOWProtein 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 temperatures for diverse natural proteins, it has become possible to train models to predict this quantity, and the literature has reported impressive performance values in terms of Spearman rho. The high correlation scores suggest that it should be possible to accurately predict melting temperature changes in engineered variants, and to reliably identify naturally thermostable proteins. However, in practice, results in these settings are often disappointing. In this paper, we explore this apparent discrepancy. We show that Spearman rho over cross-species data gives an overly optimistic impression of prediction performance, and that this metric reflects the ability to distinguish global differences in amino acid composition between species, rather than the specific effects of genetic variation. We proceed by investigating whether cross-species training on melting temperature is beneficial at all, compared to training specific models for each species. We address this question using four different transfer-learning approaches and a fine-tuning procedure. Surprisingly, we consistently find no benefit of cross-species training. We conclude that 1) current models for supervised prediction of melting temperature perform substantially worse than the literature suggests, and 2) that reliable transfer across species is still a challenging problem. An implementation of this work is available at https://github.com/deltadedirac/thermocontrast_tm

bioinformatics↗

bioRxiv2024

Kermut: Composite kernel regression for protein variant effects

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 estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modeling mutation similarity, which obtains state-of-the-art performance for supervised protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging.

synthetic biology↗

bioRxiv2023

A joint embedding of protein sequence and structure enables robust variant effect predictions

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 either protein sequences or protein structures. Here, we present a method for integrating information from protein sequences and structures in a single model that we term SSEmb (Sequence Structure Embedding). SSEmb combines a graph representation for the protein structure with a transformer model for processing multiple sequence alignments, and we show that by integrating both types of information we obtain a variant effect prediction model that is more robust to cases where sequence information is scarce. Furthermore, we find that SSEmb learns embeddings of the sequence and structural properties that are useful for other downstream tasks. We exemplify this by training a downstream model to predict protein-protein binding sites at high accuracy using only the SSEmb embeddings as input. We envisage that SSEmb may be useful both for zero-shot predictions of variant effects and as a representation for predicting protein properties that depend on protein sequence and structure.

bioinformatics↗

bioRxiv2023

Deep learning assisted single particle tracking for automated correlation between diffusion and function

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. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

biophysics↗

bioRxiv2023

FLOP: Tasks for Fitness Landscapes Of Protein wildtypes

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 the naturally occurring proteins with the most desirable properties. Promising candidates from this initial discovery phase then form the basis of the second step: a more local optimization procedure, exploring the space of variants separated from this candidate by a number of mutations. While considerable progress has been made on evaluating machine learning methods on single protein datasets, benchmarks of data-driven approaches for global fitness landscape exploration are still lacking. In this paper, we have carefully curated a representative benchmark dataset, which reflects industrially relevant scenarios for the initial wildtype discovery phase of protein engineering. We focus on exploration within a protein family, and investigate the downstream predictive power of various protein representation paradigms, i.e., protein language model-based representations, structure-based representations, and evolution-based representations. Our benchmark highlights the importance of coherent split strategies, and how we can be misled into overly optimistic estimates of the state of the field. The codebase and data can be accessed via https://github.com/petergroth/FLOP.

bioinformatics↗

bioRxiv2023

Assessing the performance of protein regression models

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 optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

bioinformatics↗

bioRxiv2022

MembraneFold: Visualising transmembrane protein structure and topology

BackgroundAlphaFolds 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.g. the cell membrane, from the protein sequence. Membrane protein topology prediction methods predict the cellular environment for each protein residue but not the structure. Current structure and topology tools thus provide complementary information. ResultsWe introduce the web server MembraneFold. MembraneFold combines protein structure (from an uploaded PDB file/AlphaFold DB/OmegaFold) and topology (DeepTMHMM) prediction in one server. The output is shown both as a structure with topology superimposed and as a sequence annotation. MembraneFold uses structures predicted by OmegaFold if neither a PDB file is uploaded nor the structure is available in AlphaFold DB. ConclusionMembraneFold is a user-friendly web server that provides practitioners with fast and accurate information about membrane proteins. It is available at https://ku.biolib.com/MembraneFold/.

bioinformatics↗

bioRxiv2022

Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components

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. These so-called lysine deserts show a high prevalence in intrinsically disordered proteins with known or predicted functions within the ubiquitin-proteasome system (UPS), including many E3 ubiquitin-protein ligases and UBL domain proteasome substrate shuttles, such as BAG6, RAD23A, UBQLN1 and UBQLN2. We show that introduction of lysine residues into the deserts leads to a striking increase in ubiquitylation of some of these proteins. In case of BAG6, we show that ubiquitylation is catalyzed by the E3 RNF126, while RAD23A is ubiquitylated by E6AP. Despite the elevated ubiquitylation, mutant RAD23A appears stable, but displays a partial loss of function phenotype in fission yeast. In case of UBQLN1 and BAG6, introducing lysine leads to a reduced abundance due to proteasomal degradation of the proteins. For UBQLN1 we show that arginine residues within the lysine depleted region are critical for its ability to form cytosolic inclusions. We propose that selective pressure to avoid lysine residues may be a common evolutionary mechanism to prevent unwarranted ubiquitylation and/or perhaps other lysine post-translational modifications. This may be particularly relevant for UPS components as they closely and frequently encounter the ubiquitylation machinery and are thus more susceptible to non-specific ubiquitylation.

biochemistry↗

bioRxiv2022

Rapid protein stability prediction using deep learning representations

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 accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate [~] 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available--including via a Web interface--and enables large-scale analyses of stability in experimental and predicted protein structures.

biophysics↗

bioRxiv2018

Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

Energy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (G[o]) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models which include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based G[o] potential, and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi, and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22* force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials, while retaining the advantages of G[o] models for sampling and guiding to the native state. We expect that the model will be widely applicable to study the folding of more complex proteins, or to study conformational dynamics and integration with experimental data.

biophysics↗

bioRxiv2018

Analyze Nucleic Acids Structures and Trajectories with Barnaba.

RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be non-trivial because it requires processing very high-dimensional data that are not easy to visualize and interpret.\n\nHere we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acids structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to i) calculate distances between three-dimensional structures using different metrics, ii) back-calculate experimental data from three-dimensional structures, iii) perform cluster analysis and dimensionality reductions, iv) search three-dimensional motifs in PDB structures and trajectories and v) construct elastic network models (ENM) for nucleic acids and nucleic acids-protein complexes.\n\nIn addition, Barnaba makes it possible to calculate torsion angles, pucker conformations and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of 1le formats such as PDB, dcd and xtc 1les. Source code, documentation and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.

bioinformatics↗