Cleavage efficiency of the intramembrane protease γ-secretase is reduced by the palmitoylation of a substrate's transmembrane domain

The intramembrane protease γ-secretase has broad physiological functions, but also contributes to Notch-dependent tumors and Alzheimer's disease. While γ-secretase cleaves numerous membrane proteins, only few nonsubstrates are known. Thus, a fundamental open question is how γ-secretase distinguishes substrates from nonsubstrates and whether sequence-based features or post-translational modifications of membrane proteins contribute to substrate recognition. Using mass spectrometry-based proteomics, we identified several type I membrane proteins with short ectodomains that were inefficiently or not cleaved by γ-secretase, including 'pituitary tumor-transforming gene 1-interacting protein' (PTTG1IP). To analyze the mechanism preventing cleavage of these putative nonsubstrates, we used the validated substrate FN14 as a backbone and replaced its transmembrane domain (TMD), where γ-cleavage occurs, with the one of nonsubstrates. Surprisingly, some nonsubstrate TMDs were efficiently cleaved in the FN14 backbone, demonstrating that a cleavable TMD is necessary, but not sufficient for cleavage by γ-secretase. Cleavage efficiencies varied by up to 200-fold. Other TMDs, including that of PTTG1IP, were still barely cleaved within the FN14 backbone. Pharmacological and mutational experiments revealed that the PTTG1IP TMD is palmitoylated, which prevented cleavage by γ-secretase. We conclude that the TMD sequence of a membrane protein and its palmitoylation can be key factors determining substrate recognition and cleavage efficiency by γ-secretase.

Assembly-dependent Structure Formation Shapes Human Interleukin-23 versus Interleukin-12 Secretion

Interleukin 12 (IL-12) family cytokines connect the innate and adaptive branches of the immune system and regulate immune responses. A unique characteristic of this family is that each member is anα:βheterodimer. For human αsubunits it has been shown that they depend on theirβsubunit for structure formation and secretion from cells. Since subunits are shared within the family and IL-12 as well as IL-23 use the same βsubunit, subunit competition may influence cytokine secretion and thus downstream immunological functions. Here, we rationally design a folding-competent human IL-23α subunit that does not depend on itsβsubunit for structure formation. This engineered variant still forms a functional heterodimeric cytokine but shows less chaperone dependency and stronger affinity in assembly with its βsubunit. It forms IL-23 more efficiently than its natural counterpart, skewing the balance of IL-12 and IL-23 towards more IL-23 formation. Together, our study shows that folding-competent human IL-12 familyαsubunits are obtainable by only few mutations and compatible with assembly and function of the cytokine. These findings might suggest that human α subunits have evolved for assembly-dependent folding to maintain and regulate correct IL-12 family member ratios in the light of subunit competition.

Photoacoustic and absorption spectroscopy imaging analysis of human blood

Photoacoustic and absorption spectroscopy imaging are safe and non-invasive molecular quantification techniques, which do not utilize ionizing radiation and allow for repeated probing of samples without them being contaminated or damaged. Here we assessed the potential of these techniques for measuring biochemical parameters. We investigated the statistical association between 31 time and frequency domain features derived from photoacoustic and absorption spectroscopy signals and 19 biochemical blood parameters. We found that photoacoustic and absorption spectroscopy imaging features are significantly correlated with 14 and 17 individual biochemical parameters, respectively. Moreover, some of the biochemical blood parameters can be accurately predicted based on photoacoustic and absorption spectroscopy imaging features by polynomial regression. In particular, the levels of uric acid and albumin can be accurately explained by a combination of photoacoustic and absorption spectroscopy imaging features (adjusted R-squared > 0.75), while creatinine levels can be accurately explained by the features of the photoacoustic system (adjusted R-squared > 0.80). We identified a number of imaging features that inform on the biochemical blood parameters and can be potentially useful in clinical diagnosis. We also demonstrated that linear and non-linear combinations of photoacoustic and absorption spectroscopy imaging features can accurately predict some of the biochemical blood parameters. These results demonstrate that photoacoustic and absorption spectroscopy imaging systems show promise for future applications in clinical practice.

Molecular Mechanisms and Clinical Phenotypes of GJB2 Missense Variants

The GJB2 gene is the most common gene responsible for hearing loss (HL) worldwide, and missense variants are the most abundant type. GJB2 pathogenic missense variants cause nonsyndromic HL (autosomal recessive and dominant) and syndromic HL combined with skin diseases. However, the mechanism by which these different missense variants cause the different phenotypes is unknown. Over 2/3 of the GJB2 missense variants have yet to be functionally studied and are currently classified as variants of uncertain significance (VUS). Based on these functionally determined missense variants, we reviewed the clinical phenotypes and investigated the molecular mechanisms that affected hemichannel and gap junction functions, including connexin biosynthesis, trafficking, oligomerization into connexons, permeability, and interactions between other coexpressed connexins. We predict that all possible GJB2 missense variants will be described in the future by deep mutational scanning technology and optimizing computational models. Therefore, the mechanisms by which different missense variants cause different phenotypes will be fully elucidated.

Intramembrane client recognition potentiates the chaperone functions of calnexin

One-third of the human proteome is comprised of membrane proteins, which are particularly vulnerable to misfolding and often require folding assistance by molecular chaperones. Calnexin (CNX), which engages client proteins via its sugar-binding lectin domain, is one of the most abundant ER chaperones, and plays an important role in membrane protein biogenesis. Based on mass spectrometric analyses, we here show that calnexin interacts with a large number of nonglycosylated membrane proteins, indicative of additional nonlectin binding modes. We find that calnexin preferentially bind misfolded membrane proteins and that it uses its single transmembrane domain (TMD) for client recognition. Combining experimental and computational approaches, we systematically dissect signatures for intramembrane client recognition by calnexin, and identify sequence motifs within the calnexin TMD region that mediate client binding. Building on this, we show that intramembrane client binding potentiates the chaperone functions of calnexin. Together, these data reveal a widespread role of calnexin client recognition in the lipid bilayer, which synergizes with its established lectin-based substrate binding. Molecular chaperones thus can combine different interaction modes to support the biogenesis of the diverse eukaryotic membrane proteome.

Computational approaches for investigating disease-causing mutations in membrane proteins: database development, analysis and prediction

Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either -helical or -barrel structures in the lipid bilayer of a cell/organelle membrane. Mutations in membrane proteins alter their structure and function and may lead to diseases. Accumulation of data on disease-causing and neutral mutations in membrane proteins are available in MutHTP and TMSNP databases, which provide additional features based on sequence, structure, topology, and diseases. These databases have been effectively utilized for analysing sequence and structure-based features in disease-causing and neutral mutations in membrane proteins, exploring disease-causing mechanisms, elucidating the relationship between sequence/structural parameters with diseases, and developing computational tools. Further, machine learning based tools have been developed for identifying disease-causing mutations using diverse features such as evolutionary information, physicochemical properties, atomic contacts, contact potentials, atomic contacts, and contribution of different energetic terms. These membrane protein-specific tools are helpful to characterize the effect of new variants in whole human membrane proteome. In this review, we provide a discussion of the available databases for disease-causing mutations in membrane proteins followed by a statistical analysis of membrane protein mutations using sequence and structural features. In addition, available prediction tools for identifying disease-causing and neutral mutations in membrane proteins will be described with their performances. This comprehensive review provides deep insights to design mutation-specific strategies for different diseases.

Influenza A Virus Infection Reactivates Human Endogenous Retroviruses Associated with Modulation of Antiviral Immunity

Human endogenous retrovirus (HERVs), normally silenced by methylation or mutations, can be reactivated by multiple environmental factors, including infections with exogenous viruses. In this work, we investigated the transcriptional activity of HERVs in human A549 cells infected by two wild-type (PR8M, SC35M) and one mutated (SC35MΔNS1) strains of Influenza A virus (IAVs). We found that the majority of differentially expressed HERVs (DEHERVS) and genes (DEGs) were up-regulated in the infected cells, with the most significantly enriched biological processes associated with the genes differentially expressed exclusively in SC35MΔNS1 being linked to the immune system. Most DEHERVs in PR8M and SC35M are mammalian apparent LTR retrotransposons, while in SC35MΔNS1, more HERV loci from the HERVW9 group were differentially expressed. Furthermore, up-regulated pairs of HERVs and genes in close chromosomal proximity to each other tended to be associated with immune responses, which implies that specific HERV groups might have the potential to trigger specific gene networks and influence host immunological pathways.

TCRpair: prediction of functional pairing between HLA-A*02:01-restricted T cell receptor α and β chains

SUMMARY

The ability of a T cell to recognize foreign peptides is defined by a single α and a single β hypervariable complementarity determining region (CDR3), which together form the T cell receptor (TCR) heterodimer. In ∼30%-35% of T cells, two α chains are expressed at the mRNA level but only one α chain is part of the functional TCR. This effect can also be observed for β chains, although it is less common. The identification of functional α/β chain pairs is instrumental in high-throughput characterization of therapeutic TCRs. TCRpair is the first method that predicts whether an α and β chain pair forms a functional, HLA-A*02:01 specific TCR without requiring the sequence of a recognized peptide. By taking additional amino acids flanking the CDR3 regions into account, TCRpair achieves an AUC of 0.71.

AVAILABILITY

TCRpair is implemented in Python using TensorFlow 2.0 and is freely available at https://www.github.com/amoesch/TCRpair.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells

Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.

Prolonged norovirus infections correlate to quasispecies evolution resulting in structural changes of surface-exposed epitopes

In this study, we analyzed norovirus (NoV) evolution in sequential samples of six chronically infected patients. The capsid gene was amplified from stool samples, and deep sequencing was performed. The role of amino acid flexibility in structural changes and ligand binding was studied with molecular dynamics (MD) simulations. Concentrations of capsid-specific antibodies increased in sequential sera. Capsid sequences accumulated mutations during chronic infection, particularly in the surface-exposed antigenic epitopes A, D, and E. The number of quasispecies increased in infections lasting for >1 month. Interestingly, high genetic complexity and distances were followed by ongoing NoV replication, whereas lower genetic complexity and distances preceded cure. MD simulation revealed that surface-exposed amino acid substitutions of the P2 domain caused fluctuation of blockade epitopes. In conclusion, the capsid protein accumulates numerous mutations during chronic infection; however, only those on the protein surface change the protein structure substantially and may lead to immune escape.

An accessible insight into genetic findings for transplantation recipients with suspected genetic kidney disease

Determining the etiology of end-stage renal disease (ESRD) constitutes a great challenge in the context of renal transplantation. Evidence is lacking on the genetic findings for adult renal transplant recipients through exome sequencing (ES). Adult patients on kidney transplant waitlist were recruited from 2017 to 2019. Trio-ES was conducted for the families who had multiple affected individuals with nephropathy or clinical suspicion of a genetic kidney disease owing to early onset or extrarenal features. Pathogenic variants were confirmed in 62 from 115 families post sequencing for 421 individuals including 195 health family members as potential living donors. Seventeen distinct genetic disorders were identified confirming the priori diagnosis in 33 (28.7%) families, modified or reclassified the clinical diagnosis in 27 (23.5%) families, and established a diagnosis in two families with ESRD of unknown etiology. In 14.8% of the families, we detected promising variants of uncertain significance in candidate genes associated with renal development or renal disease. Furthermore, we reported the secondary findings of oncogenes in 4.4% of the patients and known single-nucleotide polymorphisms associated with pharmacokinetics in our cohort to predict the drug levels of tacrolimus and mycophenolate. The diagnostic utility of the genetic findings has provided new clinical insight in most families that help with preplanned renal transplantation.

Proline codon pair selection determines ribosome pausing strength and translation efficiency in bacteria

The speed of mRNA translation depends in part on the amino acid to be incorporated into the nascent chain. Peptide bond formation is especially slow with proline and two adjacent prolines can even cause ribosome stalling. While previous studies focused on how the amino acid context of a Pro-Pro motif determines the stalling strength, we extend this question to the mRNA level. Bioinformatics analysis of the Escherichia coli genome revealed significantly differing codon usage between single and consecutive prolines. We therefore developed a luminescence reporter to detect ribosome pausing in living cells, enabling us to dissect the roles of codon choice and tRNA selection as well as to explain the genome scale observations. Specifically, we found a strong selective pressure against CCC/U-C, a sequon causing ribosomal frameshifting even under wild-type conditions. On the other hand, translation efficiency as positive evolutionary driving force led to an overrepresentation of CCG. This codon is not only translated the fastest, but the corresponding prolyl-tRNA reaches almost saturating levels. By contrast, CCA, for which the cognate prolyl-tRNA amounts are limiting, is used to regulate pausing strength. Thus, codon selection both in discrete positions but especially in proline codon pairs can tune protein copy numbers.

Activation of HERV-K(HML-2) disrupts cortical patterning and neuronal differentiation by increasing NTRK3

The biological function and disease association of human endogenous retroviruses (HERVs) are largely elusive. HERV-K(HML-2) has been associated with neurotoxicity, but there is no clear understanding of its role or mechanistic basis. We addressed the physiological functions of HERV-K(HML-2) in neuronal differentiation using CRISPR engineering to activate or repress its expression levels in a human-pluripotent-stem-cell-based system. We found that elevated HERV-K(HML-2) transcription is detrimental for the development and function of cortical neurons. These effects are cell-type-specific, as dopaminergic neurons are unaffected. Moreover, high HERV-K(HML-2) transcription alters cortical layer formation in forebrain organoids. HERV-K(HML-2) transcriptional activation leads to hyperactivation of NTRK3 expression and other neurodegeneration-related genes. Direct activation of NTRK3 phenotypically resembles HERV-K(HML-2) induction, and reducing NTRK3 levels in context of HERV-K(HML-2) induction restores cortical neuron differentiation. Hence, these findings unravel a cell-type-specific role for HERV-K(HML-2) in cortical neuron development.

Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning

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Fast and accurate prediction of transmembrane protein interaction sites.

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First ever computational survey of interaction sites in membrane proteins.

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10-30% of amino acid positions predicted to be involved in interactions.

Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.

Deep learning model predicts water interaction sites on the surface of proteins using limited-resolution data

We develop a residual deep learning model, hotWater (https://pypi.org/project/hotWater/), to identify key water interaction sites on proteins for binding models and drug discovery. This is tested on new crystal structures, as well as cryo-EM and NMR structures from the PDB and in crystallographic refinement with promising results.

Neuronal Differentiation of LUHMES Cells Induces Substantial Changes of the Proteome

Neuronal cell lines are important model systems to study mechanisms of neurodegenerative diseases. One example is the Lund Human Mesencephalic (LUHMES) cell line, which can differentiate into dopaminergic-like neurons and is frequently used to study mechanisms of Parkinson's disease and neurotoxicity. Neuronal differentiation of LUHMES cells is commonly verified with selected neuronal markers, but little is known about the proteome-wide protein abundance changes during differentiation. Using mass spectrometry and label-free quantification (LFQ), the proteome of differentiated and undifferentiated LUHMES cells and of primary murine midbrain neurons are compared. Neuronal differentiation induced substantial changes of the LUHMES cell proteome, with proliferation-related proteins being strongly down-regulated and neuronal and dopaminergic proteins, such as L1CAM and α-synuclein (SNCA) being up to 1,000-fold up-regulated. Several of these proteins, including MAPT and SYN1, may be useful as new markers for experimentally validating neuronal differentiation of LUHMES cells. Primary midbrain neurons are slightly more closely related to differentiated than to undifferentiated LUHMES cells, in particular with respect to the abundance of proteins related to neurodegeneration. In summary, the analysis demonstrates that differentiated LUHMES cells are a suitable model for studies on neurodegeneration and provides a resource of the proteome-wide changes during neuronal differentiation. (ProteomeXchange identifier PXD020044).

Experimental determination and data-driven prediction of homotypic transmembrane domain interfaces

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Homotypic TMD interfaces identified by different techniques share strong similarities.

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The GxxxG motif is the feature most strongly associated with interfaces.

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Other features include conservation, polarity, coevolution, and depth in the membrane

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The role of each of each feature strongly depends on the individual protein.

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Machine-learning helps predict interfaces from evolutionary sequence data

Interactions between their transmembrane domains (TMDs) frequently support the assembly of single-pass membrane proteins to non-covalent complexes. Yet, the TMD-TMD interactome remains largely uncharted. With a view to predicting homotypic TMD-TMD interfaces from primary structure, we performed a systematic analysis of their physical and evolutionary properties. To this end, we generated a dataset of 50 self-interacting TMDs. This dataset contains interfaces of nine TMDs from bitopic human proteins (Ire1, Armcx6, Tie1, ATP1B1, PTPRO, PTPRU, PTPRG, DDR1, and Siglec7) that were experimentally identified here and combined with literature data. We show that interfacial residues of these homotypic TMD-TMD interfaces tend to be more conserved, coevolved and polar than non-interfacial residues. Further, we suggest for the first time that interface positions are deficient in β-branched residues, and likely to be located deep in the hydrophobic core of the membrane. Overrepresentation of the GxxxG motif at interfaces is strong, but that of (small)xxx(small) motifs is weak. The multiplicity of these features and the individual character of TMD-TMD interfaces, as uncovered here, prompted us to train a machine learning algorithm. The resulting prediction method, THOIPA (www.thoipa.org), excels in the prediction of key interface residues from evolutionary sequence data.

An optimized quantitative proteomics method establishes the cell type-resolved mouse brain secretome

To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the “high‐performance secretome protein enrichment with click sugars” (hiSPECS) method. To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS‐induced neuroinflammation and to establish the cell type‐resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer‐linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type‐specific biomarkers for CNS diseases.

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔT_m) were experimentally determined, we found that the average ΔT_m due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔT_m values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔT_m due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔT_m for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔT_m values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks

Accurate prediction of amino acid residue contacts is an important prerequisite for generating high-quality 3D models of transmembrane (TM) proteins. While a large number of compositional, evolutionary, and structural properties of proteins can be used to train contact prediction methods, recent research suggests that coevolution between residues provides the strongest indication of their spatial proximity. We have developed a deep learning approach, DeepHelicon, to predict inter-helical residue contacts in TM proteins by considering only coevolutionary features. DeepHelicon comprises a two-stage supervised learning process by residual neural networks for a gradual refinement of contact maps, followed by variance reduction by an ensemble of models. We present a benchmark study of 12 contact predictors and conclude that DeepHelicon together with the two other state-of-the-art methods DeepMetaPSICOV and Membrain2 outperforms the 10 remaining algorithms on all datasets and at all settings. On a set of 44 TM proteins with an average length of 388 residues DeepHelicon achieves the best performance among all benchmarked methods in predicting the top L/5 and L/2 inter-helical contacts, with the mean precision of 87.42% and 77.84%, respectively. On a set of 57 relatively small TM proteins with an average length of 298 residues DeepHelicon ranks second best after DeepMetaPSICOV. DeepHelicon produces the most accurate predictions for large proteins with more than 10 transmembrane helices. Coevolutionary features alone allow to predict inter-helical residue contacts with an accuracy sufficient for generating acceptable 3D models for up to 30% of proteins using a fully automated modeling method such as CONFOLD2.

AllesTM: predicting multiple structural features of transmembrane proteins

Background

This study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target methods for predicting several protein properties at once.

Results

We present AllesTM, an integrated tool to predict almost all structural features of transmembrane proteins that can be extracted from atomic coordinate data. It blends several machine learning algorithms: random forests and gradient boosting machines, convolutional neural networks in their original form as well as those enhanced by dilated convolutions and residual connections, and, finally, long short-term memory architectures. AllesTM outperforms other available methods in predicting residue depth in the membrane, flexibility, topology, relative solvent accessibility in its bound state, while in torsion angles, secondary structure and monomer relative solvent accessibility prediction it lags only slightly behind the currently leading technique SPOT-1D. High accuracy on a multitude of prediction targets and easy installation make AllesTM a one-stop shop for many typical problems in the structural bioinformatics of transmembrane proteins.

Conclusions

In addition to presenting a highly accurate prediction method and eliminating the need to install and maintain many different software tools, we also provide a comprehensive overview of the impact of different machine learning algorithms and parameter choices on the prediction performance.

AllesTM is freely available at https://github.com/phngs/allestm.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Family-specific analysis of variant pathogenicity prediction tools

Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants. We find that despite the high overall accuracy of all tested methods, each tool has its Achilles heel, i.e. protein families in which its predictions prove unreliable (expected accuracy does not exceed 51% in any method). As a proof of principle, we show that choosing the optimal tool and pathogenicity threshold at a protein family-individual level allows obtaining reliable predictions in all Pfam domains (accuracy no less than 68%). A functional analysis of the sets of protein domains annotated exclusively by neutral or pathogenic mutations indicates that specific protein functions can be associated with a high or low sensitivity to mutations, respectively. The highly sensitive sets of protein domains are involved in the regulation of transcription and DNA sequence-specific transcription factor binding, while the domains that do not result in disease when mutated are responsible for mediating immune and stress responses. These results suggest that future predictors of pathogenicity and especially variant prioritization tools may benefit from considering functional annotation.

Kinetics of hepatitis B surface antigen quasispecies during REP 2139-Ca therapy in HBeAg-positive chronic HBV infection

Chronic HBV infection results in various clinical manifestations due to different levels of immune response. In recent years, hepatitis B treatment has improved by long-term administration of nucleos(t)ide analogues (NUCs) and peg-interferon. Nucleic acid polymers (NAPs; REP 2139-Ca and REP 2139-Mg) are new antiviral drugs that block the assembly of subviral particles, thus preventing the release of HBsAg and allowing its clearance and restoration of functional control of infection when combined with various immunotherapies. In the REP 102 study (NCT02646189), 9 of 12 patients showed substantial reduction of HBsAg and seroconversion to anti-HBs in response to REP 2139-Ca, whereas 3 of 12 patients did not show responses (>1 log reduction of HBsAg and HBV DNA from baseline). We characterized the dynamic changes of HBV quasispecies (QS) within the major hydrophilic region (MHR) of the 'pre-S/S' open reading frame including the 'a' determinant in responders and nonresponders of the REP 102 study and four untreated matched controls. HBV QS complexity at baseline varied slightly between responders and nonresponders (P = .28). However, these responders showed significant decline in viral complexity (P = .001) as REP 2139-Ca therapy progressed but no significant change in complexity was observed among the nonresponders (P = .99). The MHR mutations were more frequently observed in responders than in nonresponders and matched controls. No mutations were observed in 'a' determinant of major QS population which may interfere with the detection of HBsAg by diagnostic assays. No specific mutations were found within the MHR which could explain patients' poor HBsAg response during REP 2139-Ca therapy.

Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors

In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.

SCOTCH: subtype A coreceptor tropism classification in HIV-1

Motivation

The V3 loop of the gp120 glycoprotein of the Human Immunodeficiency Virus 1 (HIV-1) is considered to be responsible for viral coreceptor tropism. gp120 interacts with the CD4 receptor of the host cell and subsequently V3 binds either CCR5 or CXCR4. Due to the fact that the CCR5 coreceptor is targeted by entry inhibitors, a reliable prediction of the coreceptor usage of HIV-1 is of great interest for antiretroviral therapy. Although several methods for the prediction of coreceptor tropism are available, almost all of them have been developed based on only subtype B sequences, and it has been shown in several studies that the prediction of non-B sequences, in particular subtype A sequences, are less reliable. Thus, the aim of the current study was to develop a reliable prediction model for subtype A viruses.

Results

Our new model SCOTCH is based on a stacking approach of classifier ensembles and shows a significantly better performance for subtype A sequences compared to other available models. In particular for low false positive rates (between 0.05 and 0.2, i.e. recommendation in the German and European Guidelines for tropism prediction), SCOTCH shows significantly better prediction performances in terms of partial area under the curves and diagnostic odds ratios compared to existing tools, and thus can be used to reliably predict coreceptor tropism for subtype A sequences.

Availability and implementation

SCOTCH can be downloaded/accessed at http://www.heiderlab.de.

Genetic diversity of hepatitis D virus genotype-1 in Europe allows classification into subtypes

Hepatitis delta virus (HDV) is an RNA virus which leads to both acute and chronic forms of hepatitis. At present, HDV isolates have been classified into eight major genotypes distributed over different geographical regions. Recent increase in HDV sequences in Europe and worldwide has enabled us to revisit the taxonomic classification of HDV. A total of 116 large hepatitis delta antigen (L-HDAg) nucleotide sequences and 13 full-length HDV genome sequences belonging to genotype-1 from our European cohort, as well as 621 L-HDAg nucleotide sequences belonging to genotype-1 to genotype-8 retrieved from the GenBank NCBI were included in this study. All 116 isolates of our cohort and 341 of 621 isolates (60%) account for genotype-1, while the remaining 40% of isolates were unevenly distributed across genotype-2 to genotype-8. Phylogenetic analysis of 98 L-HDAg sequences selected after elimination of redundant sequences of all 737 isolates was performed to identify plausible subtypes within HDV genotype-1. Pairwise genetic distances for L-HDAg sequences were calculated to estimate the inter-genotype and inter-subtype differences. The HDV genotype-1 isolates phylogenetically formed five distinct clusters (genotype 1a-1e), each of them corresponding to a distinct geographic region. Two distinct subtypes for HDV genotype-2 and -4 (ie -2a and -2b; -4a and -4b, respectively) could be identified based on isolate sequences from GenBank. The previously defined genotype-1 to genotype-8 have an inter-genotypic difference of ≥10%, while the newly defined subtypes of genotype-1, -2 and -4 show an inter-subtype difference of ≥3% to <10% from the average diversity. In addition, we identified unique amino acid residues, known as specificity-determining positions, amongst the proposed subtypes.

Switching the Post-translational Modification of Translation Elongation Factor EF-P

Tripeptides with two consecutive prolines are the shortest and most frequent sequences causing ribosome stalling. The bacterial translation elongation factor P (EF-P) relieves this arrest, allowing protein biosynthesis to continue. A seven amino acids long loop between beta-strands β3/β4 is crucial for EF-P function and modified at its tip by lysylation of lysine or rhamnosylation of arginine. Phylogenetic analyses unveiled an invariant proline in the -2 position of the modification site in EF-Ps that utilize lysine modifications such as Escherichia coli. Bacteria with the arginine modification like Pseudomonas putida on the contrary have selected against it. Focusing on the EF-Ps from these two model organisms we demonstrate the importance of the β3/β4 loop composition for functionalization by chemically distinct modifications. Ultimately, we show that only two amino acid changes in E. coli EF-P are needed for switching the modification strategy from lysylation to rhamnosylation.

Conserved Secondary Structures in Viral mRNAs

RNA secondary structure in untranslated and protein coding regions has been shown to play an important role in regulatory processes and the viral replication cycle. While structures in non-coding regions have been investigated extensively, a thorough overview of the structural repertoire of protein coding mRNAs, especially for viruses, is lacking. Secondary structure prediction of large molecules, such as long mRNAs remains a challenging task, as the contingent of structures a sequence can theoretically fold into grows exponentially with sequence length. We applied a structure prediction pipeline to Viral Orthologous Groups that first identifies the local boundaries of potentially structured regions and subsequently predicts their functional importance. Using this procedure, the orthologous groups were split into structurally homogenous subgroups, which we call subVOGs. This is the first compilation of potentially functional conserved RNA structures in viral coding regions, covering the complete RefSeq viral database. We were able to recover structural elements from previous studies and discovered a variety of novel structured regions. The subVOGs are available through our web resource RNASIV (RNA structure in viruses; http://rnasiv.bio.wzw.tum.de).

Evolutionary Interplay between Symbiotic Relationships and Patterns of Signal Peptide Gain and Loss

Can orthologous proteins differ in terms of their ability to be secreted? To answer this question, we investigated the distribution of signal peptides within the orthologous groups of Enterobacterales. Parsimony analysis and sequence comparisons revealed a large number of signal peptide gain and loss events, in which signal peptides emerge or disappear in the course of evolution. Signal peptide losses prevail over gains, an effect which is especially pronounced in the transition from the free-living or commensal to the endosymbiotic lifestyle. The disproportionate decline in the number of signal peptide-containing proteins in endosymbionts cannot be explained by the overall reduction of their genomes. Signal peptides can be gained and lost either by acquisition/elimination of the corresponding N-terminal regions or by gradual accumulation of mutations. The evolutionary dynamics of signal peptides in bacterial proteins represents a powerful mechanism of functional diversification.

Characterization of the Transmitted Virus in an Ongoing HIV-1 Epidemic Driven by Injecting Drug Use

Understanding features of the HIV-1 transmission process has the potential to inform biological interventions for prevention. We have examined the transmitted virus in a cohort of people who inject drugs and who are at risk of HIV-1 infection through blood contamination when injecting in a group. This study focused on seven newly infected participants in St. Petersburg, Russia, who were in acute or early infection. We used end-point dilution polymerase chain reaction to amplify single viral genomes to assess the complexity of the transmitted virus. We also used deep sequencing to further assess the complexity of the virus. We interpret the results as indicating that a single viral variant was transmitted in each case, consistent with a model where the exposure to virus during transmission was limited. We also looked at phenotypic properties of the viral Env protein in isolates from acute and chronic infection. Although differences were noted, there was no consistent pattern that distinguished the transmitted variants. Similarly, despite the reduced genetic heterogeneity of the more recent subtype A HIV-1 epidemic in St. Petersburg, we did not see reduced variance in the neutralization properties compared to isolates from the more mature subtype C HIV-1 epidemic. Finally, in looking at members of injecting groups related to the acute HIV-1 infection/early subjects, we found examples of sequence linkage consistent with ongoing and rapid spread of HIV-1 in these groups. These studies emphasize the dynamic nature of this epidemic and reinforce the idea that improved prevention methods are needed.

Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation

Translation of consecutive prolines causes ribosome stalling, which is alleviated but cannot be fully compensated by the elongation factor P. However, the presence of polyproline motifs in about one third of the E. coli proteins underlines their potential functional importance, which remains largely unexplored. We conducted an evolutionary analysis of polyproline motifs in the proteomes of 43 E. coli strains and found evidence of evolutionary selection against translational stalling, which is especially pronounced in proteins with high translational efficiency. Against the overall trend of polyproline motif loss in evolution, we observed their enrichment in the vicinity of translational start sites, in the inter-domain regions of multi-domain proteins, and downstream of transmembrane helices. Our analysis demonstrates that the time gain caused by ribosome pausing at polyproline motifs might be advantageous in protein regions bracketing domains and transmembrane helices. Polyproline motifs might therefore be crucial for co-translational folding and membrane insertion.

Polyproline motifs induce ribosome stalling during translation, but the functional significance of this effect remains unclear. Our evolutionary analysis of polyproline motifs reveals that they are disfavored in E. coli proteomes as a consequence of the reduced translation efficiency, supporting the conjecture that translation efficiency-based evolutionary pressure shapes protein sequences. Enrichment of polyproline motifs in the protein regions bracketing structural domains and transmembrane helices indicates their regulatory role in co-translational protein folding and transmembrane helix insertion. Polyproline motifs could thus serve as protein-level cis-acting elements, which directly regulate the rate of translation elongation.

Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues

BACKGROUND

Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression. The recent availability of proteome-wide human protein abundance information now facilitates a more direct approach for CR prediction. Here we present a new version 2.0 of Expitope, which computes all naturally possible epitopes of a peptide sequence and the corresponding CR indices using both protein and transcript abundance levels weighted by a proposed hierarchy of importance of various human tissues.

RESULTS

We tested the tool in two case studies: The first study quantitatively assessed the potential CR of the epitopes used for cancer immunotherapy. The second study evaluated HLA-A*02:01-restricted epitopes obtained from the Immune Epitope Database for different disease groups and demonstrated for the first time that there is a high variation in the background CR depending on the disease state of the host: compared to a healthy individual the CR index is on average two-fold higher for the autoimmune state, and five-fold higher for the cancer state.

CONCLUSIONS

The ability to predict potential side effects in normal tissues helps in the development and selection of safer antigens, enabling more successful immunotherapy of cancer and other diseases.

Melting temperature highlights functionally important RNA structure and sequence elements in yeast mRNA coding regions

Secondary structure elements in the coding regions of mRNAs play an important role in gene expression and regulation, but distinguishing functional from non-functional structures remains challenging. Here we investigate the dependence of sequence–structure relationships in the coding regions on temperature based on the recent PARTE data by Wan et al. Our main finding is that the regions with high and low thermostability (high T_m and low T_m regions) are under evolutionary pressure to preserve RNA secondary structure and primary sequence, respectively. Sequences of low T_m regions display a higher degree of evolutionary conservation compared to high T_m regions. Low T_m regions are under strong synonymous constraint, while high T_m regions are not. These findings imply that high T_m regions contain thermo-stable functionally important RNA structures, which impose relaxed evolutionary constraint on sequence as long as the base-pairing patterns remain intact. By contrast, low thermostability regions contain single-stranded functionally important conserved RNA sequence elements accessible for binding by other molecules. We also find that theoretically predicted structures of paralogous mRNA pairs become more similar with growing temperature, while experimentally measured structures tend to diverge, which implies that the melting pathways of RNA structures cannot be fully captured by current computational approaches.

Annotation of the Domestic Pig Genome by Quantitative Proteogenomics

The pig is one of the earliest domesticated animals in the history of human civilization and represents one of the most important livestock animals. The recent sequencing of the Sus scrofa genome was a major step toward the comprehensive understanding of porcine biology, evolution, and its utility as a promising large animal model for biomedical and xenotransplantation research. However, the functional and structural annotation of the Sus scrofa genome is far from complete. Here, we present mass spectrometry-based quantitative proteomics data of nine juvenile organs and six embryonic stages between 18 and 39 days after gestation. We found that the data provide evidence for and improve the annotation of 8176 protein-coding genes including 588 novel and 321 refined gene models. The analysis of tissue-specific proteins and the temporal expression profiles of embryonic proteins provides an initial functional characterization of expressed protein interaction networks and modules including as yet uncharacterized proteins. Comparative transcript and protein expression analysis to human organs reveal a moderate conservation of protein translation across species. We anticipate that this resource will facilitate basic and applied research on Sus scrofa as well as its porcine relatives.

Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites

Phosphorylation is the most widespread and well studied reversible posttranslational modification. Discovering tissue-specific preferences of phosphorylation sites is important as phosphorylation plays a role in regulating almost every cellular activity and disease state. Here we present a comprehensive analysis of global and tissue-specific sequence and structure properties of phosphorylation sites utilizing recent proteomics data. We identified tissue-specific motifs in both sequence and spatial environments of phosphorylation sites. Target site preferences of kinases across tissues indicate that, while many kinases mediate phosphorylation in all tissues, there are also kinases that exhibit more tissue-specific preferences which, notably, are not caused by tissue-specific kinase expression. We also demonstrate that many metabolic pathways are differentially regulated by phosphorylation in different tissues.