Identification of microbial pathogens in Neolithic Scandinavian humans.

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.

aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow.

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

Single-Cell RNA Analysis Reveals Cell-Intrinsic Functions of CAR T Cells Correlating with Response in a Phase II Study of Lymphoma Patients.

PURPOSE: Although CD19 chimeric antigen receptor T cells (CAR-T) therapy has shown remarkable success in B-cell malignancies, a substantial fraction of patients do not obtain a long-term clinical response. This could be influenced by the quality of the individual CAR-T infusion product. To shed some light on this, clinical outcome was correlated to characteristics of CAR-T infusion products. PATIENTS AND METHODS: In this phase II study, patients with B-cell lymphoma (n = 23) or leukemia (n = 1) received one or two infusions of third-generation CD19-directed CAR-Ts (2 × 108/m2). The clinical trial was registered at clinicaltrials.gov: NCT03068416. We investigated the transcriptional profile of individual CD19 CAR-T infusion products using targeted single-cell RNA sequencing and multicolor flow cytometry. RESULTS: Two CAR-T infusions were not better than one in the settings used in this study. As for the CAR-T infusion products, we found that effector-like CD8+CAR-Ts with a high polyfunctionality, high cytotoxic and cytokine production profile, and low dysfunctional signature were associated with clinical response. An extended ex vivo expansion time during CAR-T manufacturing negatively influenced the proportion of effector CD8+CAR-Ts in the infusion product. CONCLUSIONS: We identified cell-intrinsic characteristics of effector CD8+CAR-Ts correlating with response that could be used as an indicator for clinical outcome. The results in the study also serve as a guide to CAR-T manufacturing practices.

Ageing desexualizes the Drosophila brain transcriptome.

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.

Limited access to antigen drives generation of early B cell memory while restraining the plasmablast response.

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.

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

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.

InterLig: improved ligand-based virtual screening using topologically independent structural alignments.

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. AVAILABILITY AND IMPLEMENTATION: The program is available from http://wallnerlab.org/InterLig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

InterPep2: global peptide-protein docking using interaction surface templates.

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 Å 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 Å 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). AVAILABILITY AND IMPLEMENTATION: The program is available from: http://wallnerlab.org/InterPep2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments.

In the last 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 their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. 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 pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: 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 par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa.

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/ .

Topology independent structural matching discovers novel templates for protein interfaces.

Motivation: Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. While there are many structural alignment algorithms for individual proteins, very few have been developed for protein interfaces, and none that can align only the interface residues to other interfaces or surfaces of interacting monomer subunits in a topology independent (non-sequential) manner. Results: We present InterComp, a method for topology and sequence-order independent structural comparisons. The method is general and can be applied to various structural comparison applications. By representing residues as independent points in space rather than as a sequence of residues, InterComp can be applied to a wide range of problems including interface-surface comparisons and interface-interface comparisons. We demonstrate a use-case by applying InterComp to find similar protein interfaces on the surface of proteins. We show that InterComp pinpoints the correct interface for almost half of the targets (283 of 586) when considering the top 10 hits, and for 24% of the top 1, even when no templates can be found with regular sequence-order dependent structural alignment methods. Availability and implementation: The source code and the datasets are available at: http://wallnerlab.org/InterComp. Supplementary information: Supplementary data are available at Bioinformatics online.

Methods for estimation of model accuracy in CASP12.

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.

Solution NMR structure of the TRIM21 B-box2 and identification of residues involved in its interaction with the RING domain.

Tripartite motif-containing (TRIM) proteins are defined by the sequential arrangement of RING, B-box and coiled-coil domains (RBCC), where the B-box domain is a unique feature of the TRIM protein family. TRIM21 is an E3 ubiquitin-protein ligase implicated in innate immune signaling by acting as an autoantigen and by modifying interferon regulatory factors. Here we report the three-dimensional solution structure of the TRIM21 B-box2 domain by nuclear magnetic resonance (NMR) spectroscopy. The structure of the B-box2 domain, comprising TRIM21 residues 86-130, consists of a short α-helical segment with an N-terminal short ß-strand and two anti-parallel ß-strands jointly found the core, and adopts a RING-like fold. This ßßαß core largely defines the overall fold of the TRIM21 B-box2 and the coordination of one Zn2+ ion stabilizes the tertiary structure of the protein. Using NMR titration experiments, we have identified an exposed interaction surface, a novel interaction patch where the B-box2 is likely to bind the N-terminal RING domain. Our structure together with comparisons with other TRIM B-box domains jointly reveal how its different surfaces are employed for various modular interactions, and provides extended understanding of how this domain relates to flanking domains in TRIM proteins.

InterPred: A pipeline to identify and model protein-protein interactions.

Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred Proteins 2017; 85:1159-1170. © 2017 Wiley Periodicals, Inc.

Reconstructing protein structures by neural network pairwise interaction fields and iterative decoy set construction.

Predicting the fold of a protein from its amino acid sequence is one of the grand problems in computational biology. While there has been progress towards a solution, especially when a protein can be modelled based on one or more known structures (templates), in the absence of templates, even the best predictions are generally much less reliable. In this paper, we present an approach for predicting the three-dimensional structure of a protein from the sequence alone, when templates of known structure are not available. This approach relies on a simple reconstruction procedure guided by a novel knowledge-based evaluation function implemented as a class of artificial neural networks that we have designed: Neural Network Pairwise Interaction Fields (NNPIF). This evaluation function takes into account the contextual information for each residue and is trained to identify native-like conformations from non-native-like ones by using large sets of decoys as a training set. The training set is generated and then iteratively expanded during successive folding simulations. As NNPIF are fast at evaluating conformations, thousands of models can be processed in a short amount of time, and clustering techniques can be adopted for model selection. Although the results we present here are very preliminary, we consider them to be promising, with predictions being generated at state-of-the-art levels in some of the cases.

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks.

BACKGROUND: Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure.In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past. RESULTS: We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å.After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å.Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server. CONCLUSIONS: The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.

Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility.

SUMMARY: Protein secondary structure and solvent accessibility predictions are a fundamental intermediate step towards protein structure and function prediction. We present new systems for the ab initio prediction of protein secondary structure and solvent accessibility, Porter 4.0 and PaleAle 4.0. Porter 4.0 predicts secondary structure correctly for 82.2% of residues. PaleAle 4.0's accuracy is 80.0% for prediction in two classes with a 25% accessibility threshold. We show that the increasing training set sizes that come with the continuing growth of the Protein Data Bank keep yielding prediction quality improvements and examine the impact of protein resolution on prediction performances. AVAILABILITY: Porter 4.0 and PaleAle 4.0 are freely available for academic users at http://distillf.ucd.ie/porterpaleale/. Up to 64 kb of input in FASTA format can be processed in a single submission, with predictions now being returned to the user within a single web page and, optionally, a single email.

Neural network pairwise interaction fields for protein model quality assessment and ab initio protein folding.

In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the Cα trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NN-PIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the Cα trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.