| 1. |
- Bergfeldt, Nora, et al.
(author)
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Identification of microbial pathogens in Neolithic Scandinavian humans
- 2024
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In: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 14
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Journal article (peer-reviewed)abstract
- With the Neolithic transition, human lifestyle shifted from hunting and gathering to farming. This change altered subsistence patterns, cultural expression, and population structures as shown by the archaeological/zooarchaeological record, as well as by stable isotope and ancient DNA data. Here, we used metagenomic data to analyse if the transitions also impacted the microbiome composition in 25 Mesolithic and Neolithic hunter-gatherers and 13 Neolithic farmers from several Scandinavian Stone Age cultural contexts. Salmonella enterica, a bacterium that may have been the cause of death for the infected individuals, was found in two Neolithic samples from Battle Axe culture contexts. Several species of the bacterial genus Yersinia were found in Neolithic individuals from Funnel Beaker culture contexts as well as from later Neolithic context. Transmission of e.g. Y. enterocolitica may have been facilitated by the denser populations in agricultural contexts.
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| 2. |
- Elofsson, Arne, et al.
(author)
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Methods for estimation of model accuracy in CASP12
- 2018
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 86:S1, s. 361-373
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Journal article (peer-reviewed)abstract
- Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.
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| 3. |
- Glaros, Vassilis, et al.
(author)
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Limited access to antigen drives generation of early B cell memory while restraining the plasmablast response
- 2021
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In: Immunity. - : Elsevier. - 1074-7613 .- 1097-4180. ; 54:9, s. 2005-
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Journal article (peer-reviewed)abstract
- Cell fate decisions during early B cell activation determine the outcome of responses to pathogens and vaccines. We examined the early B cell response to T-dependent antigen in mice by single-cell RNA sequencing. Early after immunization, a homogeneous population of activated precursors (APs) gave rise to a transient wave of plasmablasts (PBs), followed a day later by the emergence of germinal center B cells (GCBCs). Most APs rapidly exited the cell cycle, giving rise to non-GC-derived early memory B cells (eMBCs) that retained an AP-like transcriptional profile. Rapid decline of antigen availability controlled these events; provision of excess antigen precluded cell cycle exit and induced a new wave of PBs. Fate mapping revealed a prominent contribution of eMBCs to the MBC pool. Quiescent cells with an MBC phenotype dominated the early response to immunization in primates. A reservoir of APs/eMBCs may enable rapid readjustment of the immune response when failure to contain a threat is manifested by increased antigen availability.
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| 4. |
- Johansson-Åkhe, Isak, 1993-
(author)
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Development and Application of Computational Models for Peptide-Protein Complexes
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Protein-protein interactions between a protein and a smaller protein fragment or a disordered segment of a protein are called peptide-protein interactions. Such interactions are commonplace in nature and vital for normal cell function in humans. For example, the onco-protein Myc con- tains a large disordered region with several segments involved in peptide-protein interactions as part of transcription regulation, and it is mis-regulated in the vast majority of all human can- cers. As such, understanding the structural details of peptide-protein interactions on an atomic level is a necessary endeavor for understanding disease pathways as well as facilitating targeted drug-design. While experimental methods for structure determination such as X-ray crystallography and NMR can determine the structure of many peptide-protein complexes, these methods are time- consuming and costly. Additionally, the disordered nature of peptides and a sometimes lower binding affinity than for protein-protein binding can lead to transient or weak (but still highly specific) interactions impossible to fully capture with experimental methods. This leads to the need for computational methods as support and complement. Such methods have classically used statistical potentials or simple template search approaches, but as the number of deposited structures in the protein databank (PDB) grows so does the potential for supervised machine learning. The papers in this thesis present the contributions of the author to the field of peptide-protein in- teraction complex prediction, mainly through use of machine learning models. The first papers apply a Random Forest classifier to detect similarities between binding interfaces deposited in the PDB and a peptide-protein pair being investigated to find the optimal templates for struc- ture prediction. In excess of producing predictions with good self-evaluation of performance, the development of the method also confirmed theories on the similarity of protein-protein, domain-domain, and peptide-protein interfaces. Two more method for peptide-protein docking are presented in later papers. One utilizes graph convolution neural networks to improve model selection from rigid-body-docking methods by including MSA profile information as a feature, which also lead to the discovery that while profile information such as position conservation does improve predictive performance, something also seen in the first papers, the most impor- tant features are the ones describing the structural details of the complex and the bonds between residues. The other uses a graph neural network as an additional scoring term to improve upon the already state-of-the-art performing local refinement method FlexPepDock, and is capable of refining even models generated by AlphaFold-multimer. Finally, two manuscripts focus on the application of computational approaches for research into the interactions of human cMyc with TBP and PPP1R10, respectively. In the first of these pa- pers, the template-based peptide-protein complex prediction methods developed in the earlier papers of the thesis are employed together with prior knowledge of the interaction to model the complex to a high degree of certainty not achievable by NMR alone. In the second of these papers, experimental data is used as a basis for computational modeling of the complex, and the modeled complex could act as a basis for further experiments characterizing the interaction.
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| 5. |
- Johansson-Åkhe, Isak, et al.
(author)
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InterPep2: global peptide-protein docking using interaction surface templates
- 2020
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In: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811. ; 36:8, s. 2458-2465
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Journal article (peer-reviewed)abstract
- Motivation: Interactions 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 difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results: InterPep2 is a freely available method for predicting the structure of peptide-protein interactions. Improved performance is obtained by using templates from both peptide-protein and regular protein-protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide-protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 angstrom LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide-protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 angstrom LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18).
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| 6. |
- Johansson-Åkhe, Isak, 1993-, et al.
(author)
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InterPepRank : Assessment of Docked Peptide Conformations by a Deep Graph Network
- 2021
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In: Frontiers in Bioinformatics. - Lausanne, Switzerland : Frontiers Media S.A.. - 2673-7647. ; 1
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Journal article (peer-reviewed)abstract
- Peptide-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 modeling 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 fast enough for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical potentials. We 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 < 4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC between 0.65 and 0.79. When included as a 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. The InterPepRank program as well as all scripts for reproducing and retraining it are available from: http://wallnerlab.org/InterPepRank.
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| 7. |
- Johansson-Åkhe, Isak, et al.
(author)
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Predicting protein-peptide interaction sites using distant protein complexes as structural templates
- 2019
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In: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 9
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Journal article (peer-reviewed)abstract
- 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/.
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| 8. |
- Lankheet, Imke, 1995-, et al.
(author)
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Ancient proteins and the deepest population divergence among modern human populations
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Other publication (other academic/artistic)abstract
- Although ancient DNA significantly improved our understanding of human evolution and human history, it also has its limitations, such as the survival and preservation of DNA. Proteins also contain valuable molecular biological information and generally preserve longer than DNA. Especially for very old samples in hot and humid environments, protein data may provide unique information, which in turn could be extremely valuable for deciphering early human evolution in Africa. We investigate the potential differences in bone- and teeth-related protein sequences between the two groups representing the deepest population divergence in the Homo sapiens tree, namely southern African Khoe-San groups on the one hand, and the rest of humanity on the other hand. We generate protein sequences by both in silico translating modern genomic sequence data, and we obtain directly sequenced protein data from 18 ancient human samples from Africa. Our analysis led to the development of a machine learning model based on 88 amino acids that exhibit frequency differences between Khoe-San and non-Khoe-San individuals. We show that 14 of these amino acids can be investigated in ancient human remains through proteomic analysis. These findings pave the way towards the careful consideration and exploration of protein analysis in older samples, to shed light on population continuity and ancient population structure in sub-Saharan Africa.
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| 9. |
- Malacrino, Antonino, et al.
(author)
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Ageing desexualizes the Drosophila brain transcriptome
- 2022
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In: Proceedings of the Royal Society of London. Biological Sciences. - London : The Royal Society Publishing. - 0962-8452 .- 1471-2954. ; 289:1980
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Journal article (peer-reviewed)abstract
- General evolutionary theory predicts that individuals in low condition should invest less in sexual traits compared to individuals in high condition. Whether this positive association between condition and investment also holds between young (high condition) and senesced (low condition) individuals is however less clear, since elevated investment into reproduction may be beneficial when individuals approach the end of their life. To address how investment into sexual traits changes with age, we study genes with sex-biased expression in the brain, the tissue from which sexual behaviours are directed. Across two distinct populations of Drosophila melanogaster, we find that old brains display fewer sex-biased genes, and that expression of both male-biased and female-biased genes converges towards a sexually intermediate phenotype owing to changes in both sexes with age. We further find that sex-biased genes in general show heightened age-dependent expression in comparison to unbiased genes and that age-related changes in the sexual brain transcriptome are commonly larger in males than females. Our results hence show that ageing causes a desexualization of the fruit fly brain transcriptome and that this change mirrors the general prediction that low condition individuals should invest less in sexual phenotypes.
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| 10. |
- Mirabello, Claudio, et al.
(author)
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InterLig: improved ligand-based virtual screening using topologically independent structural alignments
- 2020
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In: Bioinformatics. - : OXFORD UNIV PRESS. - 1367-4803 .- 1367-4811. ; 36:10, s. 3266-3267
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Journal article (peer-reviewed)abstract
- Motivation: 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 compares 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. Results: Here, we present InterLig, a new method for the comparison and superposition of small molecules using 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.
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