Local Sequence Context Research Articles

MEDOC: A fast, scalable and mathematically exact algorithm for the site-specific prediction of the protonation degree in large disordered proteins.

Intrinsically disordered regions are found in most eukaryotic proteins and are enriched in positively and negatively charged residues. While it is often convenient to assume these residues follow their model-compound pK a values, recent work has shown that local charge effects (charge regulation) can upshift or downshift sidechain pK a values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions. The number of potential charge microstates that can be populated through acid/base regulation of a given number of ionizable residues in a sequence, N , scales as ∼2 N . This exponential scaling makes the assessment of the full charge landscape of most proteins computationally intractable. To address this problem, we developed MEDOC (Multisite Extent of Deprotonation Originating from Context) to determine the degree of protonation of a protein based on the local sequence context of each ionizable residue. We show that we can drastically reduce the number of parameters necessary to determine the full, analytic, Boltzmann partition function of the charge landscape at both global and site-specific levels. Our algorithm applies the structure of the q -canonical ensemble, combined with novel strategies to rapidly obtain the minimal set of parameters, thereby circumventing the combinatorial explosion of the number of charge microstates even for proteins containing a large number of ionizable amino acids. We apply MEDOC to several sequences, including a global analysis of the distribution of pK a values across the entire DisProt database. Our results show differences in the distribution of predicted pK a values for different amino acids, in agreement with NMR-measured distributions in proteins.

DNA flexibility regulates transcription factor binding to nucleosomes.

Cell fate decisions are controlled by sequence-specific transcription factors (TFs), referred to as 'pioneer' factors, that bind their target sites within nucleosomes ('pioneer binding') and thus initiate chromatin opening. However, pioneers bind just a minority of their recognition sequences present in the genome, suggesting that local sequence context features may regulate pioneer binding. Here, we developed PIONEAR-seq, a highly parallel sequencing-based biochemical assay for high-throughput analysis of TF binding to nucleosomes on nucleosome positioning sequences. Using PIONEAR-seq, we characterized the pioneer binding of 7 human pioneer TFs. Comparison of TF binding to nucleosomes based on the synthetic Widom 601 (W601) model sequence versus three different genomic sequences revealed that the positional preferences of these TFs' binding to nucleosomes (i.e., dyad, periodic and end binding) is determined by the broader sequence context of the nucleosome, rather than being a property intrinsic to the TF. We propose a model where the flexibility and rigidity within nucleosomal DNA regulate where pioneers bind within nucleosomes. Our results suggest that the broader physical properties of nucleosomal DNA represent another layer of cis-regulatory information read out by TFs in eukaryotic genomes.

Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules

Premature termination codons (PTCs) cause ~10–20% of inherited diseases and are a major mechanism of tumor suppressor gene inactivation in cancer. A general strategy to alleviate the effects of PTCs would be to promote translational readthrough. Nonsense suppression by small molecules has proven effective in diverse disease models, but translation into the clinic is hampered by ineffective readthrough of many PTCs. Here we directly tackle the challenge of defining drug efficacy by quantifying the readthrough of ~5,800 human pathogenic stop codons by eight drugs. We find that different drugs promote the readthrough of complementary subsets of PTCs defined by local sequence context. This allows us to build interpretable models that accurately predict drug-induced readthrough genome-wide, and we validate these models by quantifying endogenous stop codon readthrough. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.

Readthrough-induced misincorporated amino acid ratios guide mutant-specific therapeutic approaches for two CFTR nonsense mutations.

Cystic fibrosis (CF) is a monogenic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. Premature termination codons (PTCs) represent ∼9% of CF mutations that typically cause severe expression defects of the CFTR anion channel. Despite the prevalence of PTCs as the underlying cause of genetic diseases, understanding the therapeutic susceptibilities of their molecular defects, both at the transcript and protein levels remains partially elucidated. Given that the molecular pathologies depend on the PTC positions in CF, multiple pharmacological interventions are required to suppress the accelerated nonsense-mediated mRNA decay (NMD), to correct the CFTR conformational defect caused by misincorporated amino acids, and to enhance the inefficient stop codon readthrough. The G418-induced readthrough outcome was previously investigated only in reporter models that mimic the impact of the local sequence context on PTC mutations in CFTR. To identify the misincorporated amino acids and their ratios for PTCs in the context of full-length CFTR readthrough, we developed an affinity purification (AP)-tandem mass spectrometry (AP-MS/MS) pipeline. We confirmed the incorporation of Cys, Arg, and Trp residues at the UGA stop codons of G542X, R1162X, and S1196X in CFTR. Notably, we observed that the Cys and Arg incorporation was favored over that of Trp into these CFTR PTCs, suggesting that the transcript sequence beyond the proximity of PTCs and/or other factors can impact the amino acid incorporation and full-length CFTR functional expression. Additionally, establishing the misincorporated amino acid ratios in the readthrough CFTR PTCs aided in maximizing the functional rescue efficiency of PTCs by optimizing CFTR modulator combinations. Collectively, our findings contribute to the understanding of molecular defects underlying various CFTR nonsense mutations and provide a foundation to refine mutation-dependent therapeutic strategies for various CF-causing nonsense mutations.

RNA sequences that direct selective ADAR editing from a SELEX library bearing 8-azanebularine

Adenosine Deaminases Acting on RNA (ADARs) catalyze the deamination of adenosine to inosine in double-stranded RNA (dsRNA). ADARs’ ability to recognize and edit dsRNA is dependent on local sequence context surrounding the edited adenosine and the length of the duplex. A deeper understanding of how editing efficiency is affected by mismatches, loops, and bulges around the editing site would aid in the development of therapeutic gRNAs for ADAR-mediated site-directed RNA editing (SDRE). Here, a SELEX (systematic evolution of ligands by exponential enrichment) approach was employed to identify dsRNA substrates that bind to the deaminase domain of human ADAR2 (hADAR2d) with high affinity. A library of single-stranded RNAs was hybridized with a fixed-sequence target strand containing the nucleoside analog 8-azanebularine that mimics the adenosine deamination transition state. The presence of this nucleoside analog in the library biased the screen to identify hit sequences compatible with adenosine deamination at the site of 8-azanebularine modification. SELEX also identified non-duplex structural elements that supported editing at the target site while inhibiting editing at bystander sites.

Abstract 2850: Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma

Abstract Background and Aim: Neuroblastoma (NB) is the most aggressive solid tumor of infancy and arises from neural-crest-derived progenitor cells. It is composed by cell types reflecting developmental stages of the adrenergic lineage: mesenchymal (MES) and adrenergic (ADRN) cell types. These cell identities result from the activity of NB specific Core Regulatory Circuitries (CRCs), transcription factor (TF) sets that co-occupy regulatory regions together impacting the expression of their own genes and those of lineage-specific ones governing cell state transitions and cellular identities. Our hypothesis is that noncoding somatic single nucleotide variants (SNVs) located in NB CRCs TF binding sites (TFBSs) underlie NB CRCs activity, affecting cellular differentiation and promoting tumor onset. We aim to investigate such pattern of regulatory elements and identify putative driver SNVs, exploring their role and characterizing their action mechanism. Methods: Transcriptionally active TFBSs (aTFBSs) bound by ADRN CRC TFs (GATA3, HAND2, ISL1, TBX2, MYCN, PHOX2B, ASCL1 and LMO1) were identified integrating 27 ChIP-seq and 11 ATAC-seq experiments in 6 NB ADRN cell lines (SKNBE2C, Kelly, NGP, COGN415, LAN5 and NB1643). In particular, aTFBSs were defined as ChIP-seq peaks overlapping ATAC-seq signals in the same cell line. SNVs from 317 NB whole genomes were mapped on aTFBSs and the enrichment of mutations in core regions respect to flanking ones was tested using Fisher test. Observed mutation rate in aTFBSs was compared with background mutation rate due to local sequence context. SNVs in mutated aTFBSs were selected and their impact on TF binding affinity was evaluated with FABIAN-variant and motifbreakR tools. aTFBSs target-genes were identified through promoter capture HiC (CHiC) in ADRN NB cells (SKNBE2C and SHSY5Y). Results: We found a significant (FDR < 0.05) mutation enrichment in aTFBSs of ISL1, PHOX2B, TBX2, GATA3 and MYCN, suggesting a common somatic mutation burden for a subset of ADRN CRC TFBSs in ADRN NB cells. Starting from 839 SNVs falling in significantly mutated aTFBSs, we selected 269 mutations impacting the binding of ADRN CRC TFs. This mutations subset mapped in aTFBSs interacting with genes that significantly enriched pathways involved in neuronal development and migration (FDR < 0.05), thus suggesting their possible role in affecting cell identity definition. As an example, chr6:137753475:G>T mapped in aTFBSs interacting with the promoter of OLIG3, which takes part in neuronal differentiation. Interestingly, chr6:137753475:G>T was predicted to break the binding to ISL1 (FABIAN score: -0.24), a key regulator of ADRN CRC. Conclusions: These results demonstrate that somatic noncoding SNVs can act synergistically to promote NB onset, affecting CRCs activity and, in turn, the normal cell identity definition. Citation Format: Mario Capasso, Vincenzo Aievola, Vito Alessandro Lasorsa, Annalaura Montella, Ferdinando Bonfiglio, Marianna Avitabile, Teresa Maiorino, Matilde Tirelli, Giuseppe D'Alterio, Matthias Fischer, Frank Westermann, Achille Iolascon. Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2850.

Transcription factor interactions explain the context-dependent activity of CRX binding sites.

The effects of transcription factor binding sites (TFBSs) on the activity of a cis-regulatory element (CRE) depend on the local sequence context. In rod photoreceptors, binding sites for the transcription factor (TF) Cone-rod homeobox (CRX) occur in both enhancers and silencers, but the sequence context that determines whether CRX binding sites contribute to activation or repression of transcription is not understood. To investigate the context-dependent activity of CRX sites, we fit neural network-based models to the activities of synthetic CREs composed of photoreceptor TFBSs. The models revealed that CRX binding sites consistently make positive, independent contributions to CRE activity, while negative homotypic interactions between sites cause CREs composed of multiple CRX sites to function as silencers. The effects of negative homotypic interactions can be overcome by the presence of other TFBSs that either interact cooperatively with CRX sites or make independent positive contributions to activity. The context-dependent activity of CRX sites is thus determined by the balance between positive heterotypic interactions, independent contributions of TFBSs, and negative homotypic interactions. Our findings explain observed patterns of activity among genomic CRX-bound enhancers and silencers, and suggest that enhancers may require diverse TFBSs to overcome negative homotypic interactions between TFBSs.

A genomic mutational constraint map using variation in 76,156 human genomes.

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than thatfor non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.

Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level. Here we show that a single weakly supervised end-to-end multiple-instance-learning model with multi-headed attention can be trained to encode and aggregate the local sequence context or genomic position of somatic mutations, hence allowing for the modelling of the importance of individual measures for sample-level classification and thus providing enhanced explainability. The model solves synthetic tasks that conventional models fail at, and achieves best-in-class performance for the classification of tumour type and for predicting microsatellite status. By improving the performance of tasks that require aggregate information from genomic datasets, multiple-instance deep learning may generate biological insight.

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.

Statistical inference reveals the role of length, GC content, and local sequence in V(D)J nucleotide trimming.

To appropriately defend against a wide array of pathogens, humans somatically generate highly diverse repertoires of B cell and T cell receptors (BCRs and TCRs) through a random process called V(D)J recombination. Receptor diversity is achieved during this process through both the combinatorial assembly of V(D)J-genes and the junctional deletion and insertion of nucleotides. While the Artemis protein is often regarded as the main nuclease involved in V(D)J recombination, the exact mechanism of nucleotide trimming is not understood. Using a previously published TCRβ repertoire sequencing data set, we have designed a flexible probabilistic model of nucleotide trimming that allows us to explore various mechanistically interpretable sequence-level features. We show that local sequence context, length, and GC nucleotide content in both directions of the wider sequence, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Because GC nucleotide content is predictive of sequence-breathing, this model provides quantitative statistical evidence regarding the extent to which double-stranded DNA may need to be able to breathe for trimming to occur. We also see evidence of a sequence motif that appears to get preferentially trimmed, independent of GC-content-related effects. Further, we find that the inferred coefficients from this model provide accurate prediction for V- and J-gene sequences from other adaptive immune receptor loci. These results refine our understanding of how the Artemis nuclease may function to trim nucleotides during V(D)J recombination and provide another step toward understanding how V(D)J recombination generates diverse receptors and supports a powerful, unique immune response in healthy humans.

T Residues Preceded by Runs of G Are Hotspots of T→G Mutation in Bacteria.

The rate of mutation varies among positions in a genome. Local sequence context can affect the rate and has different effects on different types of mutation. Here, I report an effect of local context that operates to some extent in all bacteria examined: the rate of T→G mutation is greatly increased by preceding runs of three or more G residues. The strength of the effect increases with the length of the run. In Salmonella, in which the effect is strongest, a G run of length three 3 increases the rate by a factor of ∼26, a run of length 4 increases it by almost a factor of 100, and runs of length 5 or more increase it by a factor of more than 400 on average. The effect is much stronger when the T is on the leading rather than the lagging strand of DNA replication. Several observations eliminate the possibility that this effect is an artifact of sequencing error.

Chromatin structure influences rate and spectrum of spontaneous mutations in Neurospora crassa.

Although mutation rates have been extensively studied, variation in mutation rates throughout the genome is poorly understood. To understand patterns of genetic variation, it is important to understand how mutation rates vary. Chromatin modifications may be an important factor in determining variation in mutation rates in eukaryotic genomes. To study variation in mutation rates, we performed a mutation accumulation (MA) experiment in the filamentous fungus Neurospora crassa and sequenced the genomes of the 40 MA lines that had been propagated asexually for approximately 1015 [Formula: see text] mitoses. We detected 1322 mutations in total and observed that the mutation rate was higher in regions of low GC, in domains of H3K9 trimethylation, in centromeric regions, and in domains of H3K27 trimethylation. The rate of single-nucleotide mutations in euchromatin was [Formula: see text] In contrast, the mutation rate in H3K9me3 domains was 10-fold higher: 2.43 [Formula: see text] We also observed that the spectrum of single-nucleotide mutations was different between H3K9me3 and euchromatic domains. Our statistical model of mutation rate variation predicted a moderate amount of extant genetic variation, suggesting that the mutation rate is an important factor in determining levels of natural genetic variation. Furthermore, we characterized mutation rates of structural variants, complex mutations, and the effect of local sequence context on the mutation rate. Our study highlights that chromatin modifications are associated with mutation rates, and accurate evolutionary inferences should take variation in mutation rates across the genome into account.

Single molecule studies reveal that MutS's binding characteristics are mismatch type and context dependent and correlate with mismatch repair efficiency in vivo.

During DNA replication, DNA polymerases can incorrectly incorporate a non-complementary nucleotide against the parental DNA strand, leading to mutations and ultimately debilitating diseases if left unrepaired. All organisms possess a mismatch repair system that scans, recognizes, and repairs DNA mismatches. MutS is one of the first repair proteins to arrive at a mismatch. Upon recognition, MutS exchanges bound ADP molecules to ATP, and undergoes a conformational change to form a sliding clamp and slide away from the mismatch. MutS will then recruit other repair proteins to the site and resolve the error. Previous in vivo studies from our lab suggest that the mismatch repair efficiency in E. coli is hypervariable not only for different mismatched bases, but also for their local sequence context. Deletion studies showed that MutS is responsible for the observed hypervariability. To understand the molecular mechanism underlying sequence dependence of MutS, we employed single molecule FRET (fluorescence resonance energy transfer) to directly detect MutS binding to a mismatch, sliding clamp formation, and dissociation. The binding characteristics of E. coli MutS showed a striking dependence of the mismatch type and the overall residence time of MutS on a mismatch-containing DNA in vitro was strongly correlated with the in vivo mismatch repair efficiencies. Furthermore, the sliding clamp formation yield in the presence of ATP was correlated with in vivo mismatch repair efficiencies. These data support in vivo repair trends and help clarify mechanistic basis by which mismatches are differentially repaired. Future experiments will be conducted using additional DNA mismatches with different sequence contexts to understand the generalizability of this observation.

Single molecule FRET reveals distinct MutS conformational dynamics on different DNA mismatches.

DNA mismatch repair (MMR) is an essential biological process that is common to all living organisms. The proteins involved in the MMR pathway identify and correct base mismatch errors and small insertion/deletions that are made during replication as well as some types of DNA damage. MutS is the MMR protein that scans DNA and locates mismatches. Previous single molecule fluorescent resonant energy transfer (smFRET) studies have found that binding to a GT mismatch or T-insert loop in DNA induces Thermus aquaticus MutS to undergo ATP-dependent conformational changes that convert it to a long-lived sliding clamp. This post-mismatch-recognition state of MutS is associated with downstream repair signaling in many models. Because different mismatches are repaired with the varying efficiencies, here we extend the smFRET studies to characterize the interactions between MutS and several additional mismatches. We further examine some mismatches in different local sequence contexts. Our results reproduce the binding affinities expected for these mismatches and characterize a range of tendencies for sliding clamps to result from MutS interacting with them. We compare our results to reported repair efficiencies to correlate the conversion to sliding clamp with eventual mismatch repair.

Good fences make distant neighbors: Determining the effect of local sequence context on Met-Met interactions.

Residue-residue interactions extend beyond just oppositely charged residues; some hydrophobic residues can also undergo favorable interactions. One example of this kind of interaction occurs between two Methionine residues. Recently, a study done in our lab on the BDNF prodomain identified that the disease-causing effects of the Val66Met mutation stems from Met-Met interactions increasing the probability of contact between contiguously hydrophobic regions (termed “blobs”) not previously in contact. We’ve also shown that disease-causing SNPs are significantly enriched in hydrophobic blobs, indicating that changes on the blob-level can have a major impact on protein functionality. Currently, it is unknown to what extent Met-Met interaction pairs are significantly conserved by evolutionary mechanisms, or how different local sequence contexts influence the rate of these Met-Met interactions. Here, we quantify the enrichment of mutations leading to Met-Met interactions in a set of bacterial proteins using a large genome dataset. Additionally, we analyze the genomes of over 1,500 bacterial protein families to quantify enrichment of coevolving residue positions involving pairs of Methionine residues. We also ran molecular dynamics simulations of short peptides composed of poly-alanine, lysine, and threonine with two additional Methionines separated by varying distances, and detect contacts tested for enrichment of direct Met-Met contacts. We find that blob type influences the contact probability between Methionine residues in our short peptide simulations, determine how enriched such contacts are in an the bacterial protein dataset, and ultimately demonstrate that it is your neighbors that can make (or break) whom you come into contact with.

How does sequence influence charge regulation of disordered proteins?

Charge regulation, defined as the regulation of charge states of ionizable residues through the uptake or release of protons, can influence the conformational ensembles and distributions of charge states that are accessible to intrinsically disordered proteins. These influences are exerted through sequence- and conformational-context dependent pKa values. We recently developed and deployed a q-canonical ensemble that allows for the linked analyses of separate measurements, of charge and conformation as a function of pH. When combined with atomistic, q-canonical Monte Carlo simulations based on the ABSINTH implicit solvation model, we were able to uncover the pH-dependent interplay of charge state and conformational heterogeneity. However, the combinatorial increase in the number of charge states to consider has remained a persistent challenge. Here, we introduce a breakthrough that allows us to predict the extent of charge regulation to be expected for IDPs arbitrary numbers of ionizable residues. The algorithm leverages prior information regarding the effects of local sequence contexts on charge regulation. Longer sequences with arbitrary numbers of ionizable residues are built up using a chain-growth Monte Carlo procedure, each step building on extant information for peptide fragments. We have deployed the new methodology in a high-throughput investigation of sequences drawn from the IDRome, which is the sub-proteome of intrinsically disordered regions (IDRs). We find that the apparent charge, obtained by assuming model compound pKa values, is rarely the same as the real charge that one predicts based on an accounting of charge regulation effects. This work paves the way to understanding the sequence features that result in charge regulation effects, and their importance to biomolecular processes.

Development of Mild Chemical Catalysis Conditions for m1A-to-m6A Rearrangement on RNA.

The conversion of N1-methyladenosine (m1A) to N6-methyladenosine (m6A) on RNA is an important step for both allowing efficient reverse transcription read-though for sequencing analysis and mapping modifications in the transcriptome. Enzymatic transformation is often used, but the efficiency of the removal can depend on local sequence context. Chemical conversion through the application of the Dimroth rearrangement, in which m1A rearranges into m6A under heat and alkaline conditions, is an alternative, but the required alkaline conditions result in significant RNA degradation by hydrolysis of the phosphodiester backbone. Here, we report novel, mild pH conditions that catalyze m1A-to-m6A arrangement using 4-nitrothiophenol as a catalyst. We demonstrate the efficient rearrangement in mononucleosides, synthetic RNA oligonucleotides, and RNAs isolated from human cell lines, thereby validating a new approach for converting m1A-to-m6A in RNA samples for sequencing analyses.

Robust unsupervised deconvolution of linear motifs characterizes 68 protein modifications at proteome scale

The local sequence context is the most fundamental feature determining the post-translational modification (PTM) of proteins. Recent technological improvements allow for the detection of new and less prevalent modifications. We found that established state-of-the-art algorithms for the detection of PTM motifs in complex datasets failed to keep up with this technological development and are no longer robust. To overcome this limitation, we developed RoLiM, a new linear motif deconvolution algorithm and webserver, that enables robust and unbiased identification of local amino acid sequence determinants in complex biological systems demonstrated here by the analysis of 68 modifications found across 30 tissues in the human draft proteome map. Furthermore, RoLiM analysis of a large-scale phosphorylation dataset comprising 30 kinase inhibitors of 10 protein kinases in the EGF signalling pathway identified prospective substrate motifs for PI3K and EGFR.

Mutation rate and spectrum in obligately outcrossing Caenorhabditis elegans mutation accumulation lines subjected to RNAi-induced knockdown of the mismatch repair gene msh-2.

DNA mismatch repair (MMR), an evolutionarily conserved repair pathway shared by prokaryotic and eukaryotic species alike, influences molecular evolution by detecting and correcting mismatches, thereby protecting genetic fidelity, reducing the mutational load, and preventing lethality. Herein we conduct the first genome-wide evaluation of the alterations to the mutation rate and spectrum under impaired activity of the MutSα homolog, msh-2, in Caenorhabditis elegans male–female fog-2(lf) lines. We performed mutation accumulation (MA) under RNAi-induced knockdown of msh-2 for up to 50 generations, followed by next-generation sequencing of 19 MA lines and the ancestral control. msh-2 impairment in the male–female background substantially increased the frequency of nuclear base substitutions (∼23×) and small indels (∼328×) relative to wildtype hermaphrodites. However, we observed no increase in the mutation rates of mtDNA, and copy-number changes of single-copy genes. There was a marked increase in copy-number variation of rDNA genes under MMR impairment. In C. elegans, msh-2 repairs transitions more efficiently than transversions and increases the AT mutational bias relative to wildtype. The local sequence context, including sequence complexity, G + C-content, and flanking bases influenced the mutation rate. The X chromosome exhibited lower substitution and higher indel rates than autosomes, which can either result from sex-specific mutation rates or a nonrandom distribution of mutable sites between chromosomes. Provided the observed difference in mutational pattern is mostly due to MMR impairment, our results indicate that the specificity of MMR varies between taxa, and is more efficient in detecting and repairing small indels in eukaryotes relative to prokaryotes.