Biology subjects

Wallner, B.

Publications and source records attributed to Wallner, B..

At least 19 recordsLinked to original sources

bioRxiv2026

A generalisable framework to inject distance information into Alphafold-like structure predictors

Structure prediction methods are now highly successful at predicting three-dimensional structures from sequence. However, it is still often desirable to supplement these methods with additional external priors on pairwise distances in the structures. We present a general method for injecting prior information into AlphaFold-like structure predictors by biasing the pair representation to produce desirable features in the distogram, which are then reflected in the structures. We demonstrate this approach to: sample alternate states by selectively pushing or pulling mobile amino acid pairs; integrate NMR NOESY data with structure prediction; and improve the success of protein-protein and protein-ligand complex prediction. We demonstrate that this approach is applicable both to AlphaFold 2 and a reproduction of AlphaFold 3 (OpenFold3). resTrain is open source, available to all users on GitHub and as a Colab notebook: github.com/clami66/resTrain

bioinformatics↗

bioRxiv2026

AFsample3: Generating and selecting multipleconformational states with Alphafold3

Accurately capturing the conformational diversity of proteins is essential for understanding their mechanisms and regulation. However, current structure prediction approaches, including AlphaFold derivatives, are largely limited to modeling one dominant conformation. Improved sampling methods have expanded this to predicting two states. Here, we present AFsample3, an enhanced sampling framework built upon AlphaFold3 that substantially improves the generation and selection of diverse protein conformations. Across a benchmark of 238 non-redundant proteins with multiple experimentally determined states, AFsample3 significantly outperforms AlphaFold3 and its predecessor AFsample2 by improved predictions for 28% (67/239) of targets ({Delta}TM > 0.1) while degrading only 3% (8/239), and increases the number of high-quality alternate-state models (TM > 0.8) by 54% (from 54 to 83; p < 0.0001). AF-sample3 improves alternate-state accuracy for 28% of targets ({Delta}TM > 0.1) while degrading only 3%, and increases the number of targets with high-quality predictions (TM > 0.8) by over 50% compared to standard AlphaFold3. Ensemble diversity is also markedly enhanced (p < 0.0001), enabling accurate modeling of potential intermediate and or additional states. These results demonstrate that the improved sampling in AFsample3 can capture multiple native-like conformations, representing a significant advance in modeling of protein conformational landscapes.

bioinformatics↗

bioRxiv2024

AFsample2: Predicting multiple conformations and ensembles with AlphaFold2

Understanding protein dynamics and conformational states carries profound scientific and practical implications for several areas of research, ranging from a general understanding of biological processes at the molecular level to a detailed understanding of disease mechanisms, which in turn can open up new avenues in drug development. Multiple solutions have been recently developed to widen the conformational landscape of predictions made by Alphafold2 (AF2). Here, we introduce AFsample2, a method employing random MSA column masking to reduce the influence of co-evolutionary signals to enhance the structural diversity of models generated by the AF2 neural network. AFsample2 improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the data set of open-closed conformations (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. Furthermore, AFsample2 increased the diversity of intermediate conformations by 70% compared to the standard AF2 system, producing highly confident models, that could potentially be on-path between the two states. In addition, we also propose a way of selecting the end-states in generated model ensembles. These solutions could potentially enhance the generation and identification of alternative protein conformations, thereby providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.

bioinformatics↗

bioRxiv2024

DockQ v2: Improved automatic quality measure for protein multimers, nucleic acids, and small molecules

It is important to assess the quality of modeled biomolecules to benchmark and assess the performance of different prediction methods. DockQ has emerged as the standard tool for assessing the quality of protein interfaces in model structures against given references. However, as predictions of large multimers with multiple chains become more common, there is a need to update DockQ with more functionality for robustness and speed. Moreover, as the field progresses and more methods are released to predict interactions between proteins and other types of molecules, such as nucleic acids and small molecules, it becomes necessary to have a tool that can assess all types of interactions. Here, we present a complete reimplementation of DockQ in pure Python. The updated version of DockQ is more portable, faster and introduces novel functionalities, such as automatic DockQ calculations for multiple interfaces and automatic chain mapping with multi-threading. These enhancements are designed to facilitate comparative analyses of protein complexes, particularly large multi-chain complexes. Furthermore, DockQ is now also able to score interfaces between proteins, nucleic acids, and small molecules. Codehttps://wallnerlab.org/DockQ

bioinformatics↗

bioRxiv2024

Structural characterization of the Pseudomonas Aeruginosa MexR-mexR repressor-operator complex: a small-angle X-ray and neutron scattering perspective

The rapid spread of acquired multidrug resistance (MDR) in bacteria is a world-wide health threat. The MexR protein regulates the expression of the MexAB-OprM efflux pump, which actively extrudes chemical compounds with high toxicity to the host organism Pseudomonas Aeruginosa. In repression mode, two MexR dimers bind to an operator with two homologous pseudo-palindromic boxes located in proximity (named PI and PII). Here we report a first structural characterization of the complex in solution using small angle X-ray scattering (SAXS), small-angle neutron scattering (SANS) and rigid body modelling. The spacing between the PI and PII boxes is rich in AT base pairs indicate possible flexibility between the two MexR dimer binding sites. In agreement, our best modelling fits show a requirement for DNA bending between the two MexR binding sites to optimally fit SAS data as well as known biological properties of the MexR operons. Taken together, this study contributes to better understanding of the structural properties of bacterial operators and their repressor proteins.

biophysics↗

bioRxiv2024

Towards understanding of allostery in MALT1: a possible role of interdomain motions as revealed by NMR and AlphaFold

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) has emerged as an attractive target for the development of modulatory compounds, particularly in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1(PCASP-Ig3)339-719 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained largely unexplored. We present here inaugural dynamic analyses of the apo MALT1(PCASP-Ig3)339-719 form along with its mutated variant, E549A. This investigation harnessed an array of NMR relaxation techniques, including longitudinal and transverse 15N auto-relaxation, heteronuclear NOE, transverse cross-correlated relaxation and NOE measurements between side-chain methyl groups. Our findings unequivocally confirm that MALT1(PCASP-Ig3)339-719 exists solely as a monomer in solution, and demonstrate that the two domains display semi-independent movements in relation to each other. Our extensive dynamic study, covering a range of time scales, along with the assessment of diverse conformational populations for MALT1(PCASP-Ig3)339-719, by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, shed light at potential mechanisms underlying the allosteric regulation of this enzyme, and the specific importance of interdomain motions.

molecular biology↗

bioRxiv2023

Unmasking AlphaFold: integration of experiments and predictions with a smarter template mechanism

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and results at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFolds capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This new approach generates near-perfect structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked fills incomplete structures by a procedure called"structural inpainting", which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently. Codegithub.com/clami66/AF_unmasked

bioinformatics↗

bioRxiv2023

Behavioural, physiological, and genetic drivers of coping

Animals regularly experience stressful situations, ranging from predation to social stress, yet successfully deal with them on most occasions. This adaptive mechanism, coping, reduces the adverse effects of stressors through behavioural and physiological efforts, failing to which may result in reduced fitness. However, considerable variation in coping is observed. Unlike in humans, coping is often considered a personality trait in non-human animals due to construct similarity, resulting in conceptual ambiguity. Besides, limited multidisciplinary research has rendered comprehending the drivers of coping in animals challenging. We assessed repetitive behavioural coping or coping styles (n=30), emotional arousal (n=12), and consistent inter-individual differences, i.e., personalities (n=32) of long-tailed macaques (Macaca fascicularis) using observations, ecologically relevant experiments, and infrared thermography. We finally investigated the association of coping with a Valine/Methionine polymorphism encoded by the Catechol-O-methyltransferase (COMT) gene (n=26), which is widely known for its involvement in human stress regulation. Our findings suggest that personality and the presence of the human equivalent COMT Val158Met polymorphism in long-tailed macaques are associated with emotion-focused and problem-focused coping styles. These coping styles were consistent with emotional arousal as measured with infrared thermography. We discuss these proximate drivers of coping for a better understanding of its evolution in primates.

animal behavior and cognition↗

bioRxiv2022

AFsample: Improving Multimer Prediction with AlphaFold using Aggressive Sampling

The AlphaFold neural network model has revolutionized structural molecular biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated around 6,000 models per target compared to 25 default for AF2-multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared to AF2-multimer it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared to all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modelling multimeric structures, alternate conformations or flexible structures. AvailabilityAFsample is available online at http://wallnerlab.org/AFsample.

bioinformatics↗

bioRxiv2022

dgram2dmap: Extraction, visualisation and formatting of distance constraints from AlphaFold distograms

Distograms are data structures output by AlphaFold, along with the predicted 3D coordinates of the target protein, that encode predictions about Euclidean distances between pairs of amino acids. Although distograms are often overlooked, they do provide information that is to some extent complementary to that of the final 3D model. Here, we introduce dgram2dmap, a simple tool to convert distograms into distance maps that can be visualised and used in external tools for further downstream analyses. dgram2dmap runs within seconds to minutes, is open source and available on GitHub: https://github.com/clami66/dgram2dmap.

bioinformatics↗

bioRxiv2021

InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol

MotivationInteractions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine the structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-[A]ngstrom precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural network seeing use in protein model quality assessment. ResultsHere, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%. AvailabilityInterPepScore is available online at http://wallnerlab.org/InterPepScore.

bioinformatics↗

bioRxiv2021

The PNUTS-PAD domain recruits MYC to the PNUTS:PP1 phosphatase complex via the oncogenic MYC-MB0 region

Despite MYC dysregulation in most human cancers, strategies to target this potent oncogenic driver remains an urgent unmet need. Recent evidence shows the PP1 phosphatase and its regulatory subunit PNUTS control MYC phosphorylation and stability, however the molecular basis remains unclear. Here we demonstrate that MYC interacts directly with PNUTS through the MYC homology Box 0 (MB0), a highly conserved region recently shown to be important for MYC oncogenic activity. MB0 interacts with PNUTS residues 1-148, a functional unit here termed, PNUTS amino-terminal domain (PAD). Using NMR spectroscopy we determined the solution structure of PAD, and characterised its interaction with MYC. Point mutations of residues at the MYC-PNUTS interface significantly weaken their interaction both in vitro and in vivo. These data demonstrate the MB0 binding pocket of the PAD represents an attractive site for pharmacological disruption of the MYC-PNUTS interaction. In BriefSolving the structure of MYC-PNUTS direct interaction reveals how the intrinsically disordered MYC-Box0 (MB0) region anchors into a binding pocket in the N-terminal PAD domain of PNUTS. These data provide insight into the molecular mechanism of how the PNUTS:PP1 phosphatase complex regulates MYC phosphorylation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/470928v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@179b89corg.highwire.dtl.DTLVardef@47338eorg.highwire.dtl.DTLVardef@89b6f4org.highwire.dtl.DTLVardef@14a80bd_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA region critical for MYC oncogenesis, MYC-Box0 (MB0), directly interacts with PNUTS C_LIO_LIPNUTS amino-terminal domain (PAD) is a structural domain that interacts with MYC MB0 C_LIO_LIMutation of single residues at the interaction interface disrupts MYC-PNUTS binding in cells C_LIO_LIMYC-PNUTS binding releases MYC intramolecular interactions to enable PP1substrate access C_LI

cancer biology↗

bioRxiv2021

Benchmarking Peptide-Protein Docking and Interaction Prediction with AlphaFold-Multimer

Protein interactions are key in vital biological process. In many cases, particularly often in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions of other proteins. The flexible nature of peptides enable rapid, yet specific, regulation of important functions in the cell, such as the cell life-cycle. Because of this, understanding the molecular details of these interactions are crucial to understand and alter their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has caused a leap in accuracy for computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact as well as its accuracy in modeling the resulting interaction complexes are benchmarked against established methods in the fields of peptide-protein interaction prediction and modeling. We find that AlphaFold-Multimer consistently produces predicted interaction complexes with a median DockQ of 0.47 for all 112 complexes investigated. Additionally, it can be used to separate interacting from non-interacting pairs of peptides and proteins with ROC-AUC and PR-AUC of 0.78 and 0.61, respectively, best among the method benchmarked. However, the most interestingly result is the possibility to improve AlphaFold by enabling dropout at inference to sample a wider part of the conformational space. This improves the median DockQ from 0.47 to 0.56 for rank 1 and the median best DockQ improves from 0.58 to 0.72. This scheme of generating more structures with AlphaFold should be generally useful for many application involving multiple states, flexible regions and disorder.

bioinformatics↗

bioRxiv2020

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

MotivationPeptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials. ResultsWe present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine-learning based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD<4[A]. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC of circa 0.69. When included as selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of Medium and High quality models produced by 80% and 40%, respectively. AvailabilityThe program is available from: http://wallnerlab.org/InterPepRank ContactBjorn Wallner bjorn.wallner@liu.se Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2019

InterPep2: Global Peptide-Protein Docking with Structural Templates

MotivationInteractions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming, and because of the disordered nature of the ligand, the interactions are especially difficult to predict through software, requiring specialized solutions. Although several prediction-methods exist, most are limited in performance or availability.\n\nResultsInterPep2 is a freely available method for predicting the structure of peptide-protein interactions. We have previously shown that structural templates can be used to accurately predict peptide-protein binding sites, and that using templates from regular protein-protein interactions will increase the number of sites found. Here, we show that the same principle can be extended to dock the peptide to the binding surface using InterPep2. A key component of InterPep2 is the ability to score plausible interaction templates using a RandomForest trained to predict the DockQ-score using sequence and structural features. InterPep2 is tested on a difficult dataset of 251 peptide-protein complexes, where it correctly positions 136 (54%) at the correct site compared to 114 (45%) for the second best method. Analyzing the confidence score InterPep2 recalls more true positives across all specificity levels compared to the second best method, for example at 10% False Positive Rate it correctly identifies 59% of the complexes compared to 44% for the second best method.\n\nAvailabilityThe program is available from: http://wallnerlab.org/InterPep\n\nContactBjorn Wallner bjorn.wallner@liu.se

bioinformatics↗

bioRxiv2019

InterLig: a fast and accurate software for ligand-based virtual screening

In the past few years, drug discovery processes have been relying more and more on computational methods to sift out the most promising molecules before time and resources are spent to test them in experimental settings. Whenever the protein target of a given disease is not known, it becomes fundamental to have accurate methods for ligand-based Virtual Screening, which compare known active molecules against vast libraries of candidate compounds. Recently, 3D-based similarity methods have been developed that are capable of scaffold-hopping and to superimpose matching molecules. Here, we present InterLig, a new method for the comparison and superposition of small molecules based on 3D, topologically-independent alignments of atoms. We test InterLig on a standard benchmark and show that it compares favorably to the best currently available 3D methods. InterLig is open source and is available to everyone at: http://wallnerlab.org/interlig.

bioinformatics↗

bioRxiv2018

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/

bioinformatics↗

bioRxiv2018

rawMSA: Proper Deep Learning makes protein sequence profiles and feature extraction obsolete

In the last few decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and about their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed in the hope of taking advantage of these new architectures. On the other hand, most methods are still based on heavy pre-processing of the input data, as well as the extraction and integration of multiple hand-picked, manually designed features. Since Multiple Sequence Alignments (MSA) are almost always the main source of information in de novo prediction methods, it should be possible to develop Deep Networks to automatically refine the data and extract useful features from it. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering sequence profiles and other pre-calculated features obsolete. We developed rawMSA in three different flavors to predict secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on a par with the top ranked CASP12 methods in the inter-residue contact map prediction category. We believe that rawMSA represents a promising, more powerful approach to protein structure prediction that could replace older methods based on protein profiles in the coming years.\n\nAvailabilitydatasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa

bioinformatics↗