Motif Databases Research Articles

CompariPSSM: a PSSM-PSSM comparison tool for motif binding determinant analysis.

Short Linear Motifs (SLiMs) are compact functional modules that mediate low-affinity protein-protein interactions. SLiMs direct the function of many dynamic signalling and regulatory complexes playing a central role in most biological processes of the cell. Motif binding determinants describe the contribution of each residue in a motif-containing peptide to the affinity and specificity of binding to the motif-binding partner. Motif binding determinants are generally defined as a motif consensus pattern or a position-specific scoring matrix (PSSM) encoding quantitative preferences. Motif binding determinant comparison is an important motif analysis task and can be applied to motif annotation, classification, clustering, discovery and benchmarking. Currently, binding determinant comparison is generally performed by analysing consensus similarity, however, this ignores important quantitative information in both the consensus and non-consensus positions. We have created a new tool, CompariPSSM, that quantifies the similarity between motif binding determinants using sliding window PSSM-PSSM comparison and scores PSSM similarity using a randomisation-based probabilistic framework. The tool has been benchmarked on curated data from the Eukaryotic Linear Motif (ELM) database and experimental data from Proteomic Phage Display (ProP-PD). CompariPSSM can be used for peptide classification to validate motif classes, peptide clustering to group functionally related conserved disordered regions, and benchmarking experimental motif discovery methods. CompariPSSM is available at https://slim.icr.ac.uk/projects/comparipssm. Supplementary data are available at Bioinformatics online.

Bioinformatics Analysis and Spatiotemporal Distribution of the fliC Gene and Its Protein Isolated from Escherichia coli-Infected Patients in Eastern Algeria.

The fliC locus in Escherichia coli primarily encodes flagellar (H) antigens. Exploring fliC sequence diversity will shed light on the mechanisms of bacterial pathogenicity. This study examined the presence of fliC mutant strains of E. coli in infected patients from different age groups, sexes and sample types in eastern Algerian provinces over a span of 2 years. This retrospective, cross-sectional study involved three provinces in eastern Algeria: i) Bordj Bou Arreridj, ii) Setif and iii) Batna. A total of 75 E. coli isolates were obtained from the University State Hospital Centre. Two types of analyses were conducted: i) a bioinformatics analysis of the protein sequences translated from the fliC genes, specifically the fliC flagellar sequences and ii) a multifactorial statistical analysis (multiple correspondence analysis [MCA]) of the population of infected patients, considering various parameters. The fliC protein sequences were aligned using the Multiple Alignment using Fast Fourier Transform (MAFFT) programme. The alignment results were then visualised using the MView programme. Finally, a phylogenetic tree was constructed using the maximum likelihood algorithm in MEGA 11 software. Bioinformatics analysis highlighted the strong conservation of the structures of the fliC protein sequences, especially at the two N- and C-terminal ends, and strong variability in the central zone. This remarkable fliC intersequence similarity is corroborated by the presence of protein motifs identified in the PROSITE protein motif database. fliC mutations in E. coli were not detected in the clinical samples of patients from hospitals in the three Algerian Provinces. Our analysis revealed that all the samples exhibited characteristics of wild-type virulent bacteria without mutations. A multicentre study is warranted for epidemiological surveillance of fliC mutant strains for future preventive measures.

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs.

Endogenous peptides are an abundant and versatile class of biomolecules with vital roles pertinent to the functionality of the nervous, endocrine, and immune systems and others. Mass spectrometry stands as a premier technique for identifying endogenous peptides, yet the field still faces challenges due to the lack of optimized computational resources for reliable raw mass spectra analysis and interpretation. Current database searching programs can exhibit discrepancies due to the unique properties of endogenous peptides, which typically require specialized search considerations. Herein, we present a high throughput, novel scoring algorithm for the extraction and ranking of conserved amino acid sequence motifs within any endogenous peptide database. Motifs are conserved patterns across organisms, representing sequence moieties crucial for biological functions, including maintenance of homeostasis. MotifQuest, our novel motif database generation algorithm, is designed to work in partnership with EndoGenius, a program optimized for database searching of endogenous peptides and that is powered by a motif database to capitalize on biological context to produce identifications. MotifQuest aims to quickly develop motif databases without any prior knowledge, a laborious task not possible with traditional sequence alignment resources. In this work we illustrate the utility of MotifQuest to expand EndoGenius' identification utility to other endogenous peptides by showcasing its ability to identify antimicrobial peptides. Additionally, we discuss the potential utility of MotifQuest to parse out motifs from a FASTA database file that can be further validated as new peptide drug candidates.

Transcription of microRNAs is regulated by developmental signaling pathways and transcription factors.

In early embryonic development, the cross-regulation of transcription factors and signaling pathways are critical in mediating developmental and physiological processes. Additionally, many studies have shown the importance of post-transcriptional regulation of signaling and network components mediated by microRNAs (miRNAs); however, how miRNAs are transcriptionally regulated is poorly understood. miRNAs are critical fine-tuners of many biological processes and their dysregulation leads to a variety of diseases and developmental defects. Previously, we have shown that miRNAs are dynamically expressed throughout sea urchin development, suggesting that miRNAs are likely to be under transcriptional regulation. Here, we used pharmacological inhibitors, genetic constructs, and loss-of-function reagents to assess the impact of key signaling pathways (Wnt, Nodal, MAPK, Sonic Hedgehog, Delta/Notch, VEGF, and BMP) and transcription factors (Alx1, Ets1/2, and Tbr) on the transcript levels of the evolutionarily conserved miR-1, miR-31, miR-92 and miR-124; the invertebrate-specific miR-71; and the echinoderm-specific miR-2002, miR-2007, and miR-2012. We also used computational methods to identify potential transcription factor binding sites of these miRNAs. Lists of binding motifs for transcription factors (TFs) were acquired from the MEME-Suite Motif Database and used as inputs for the algorithm FIMO (Find Individual Motif Occurrences), which detects short nucleotide motifs within larger sequences. Based on experimental data on miRNA expression in conjunction with bioinformatic predictions, we propose that the transcription factors Tbr, Alx1, and Ets1 regulate SpmiR-1, SpmiR-31, and SpmiR-71, respectively. We additionally observed significant effects on miRNA levels as a result of perturbations to Wnt, Nodal, MAPK, and Sonic Hedgehog signaling pathways, while no significant change on miRNA levels were observed with perturbations to Delta/Notch, VEGF, or BMP signaling pathways. Overall, this study provides insights into the transcriptional regulation of miRNAs by signaling pathways and transcription factors and contribute to our overall understanding of the genetic regulation of developmental processes.

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

Multi-resolution sequence and structure feature extraction for binding site prediction

Circular ribonucleic acids (circRNAs) are single-stranded RNA molecules that form loops and are widely expressed in various cells and tissues. They interact with RNA-binding proteins (RBPs) and play a vital regulatory role in the onset and development of several diseases. Researchers have proposed various hybrid architecture prediction methods based on convolutional neural networks and recurrent neural networks to recognize the interactions and sites between circRNAs and RBPs and thus reveal the biological functions of circRNAs. However, existing methods usually ignore the structural information of circRNA, which may affect the modeling of circRNA and RBP binding modes. To address these problems, we propose a prediction model based on multi-resolution feature extraction. First, it generates sequence features using unsupervised word embedding and nucleotide density. Then, it uses implicit and explicit pseudo-secondary structure hybrid encoding to fuse sequence and structural information and better simulate circRNA-RBP binding patterns. Second, it uses an enhanced bidirectional sample convolution and interaction network encoder to capture and integrate high-order features of distinct resolutions from the multi-scale convolution module. This provides rich semantic input to the downstream bidirectional long short-term memory network to improve prediction accuracy. Experimental results on 37 circRNA and 31 linear RNA datasets show that our method has significant advantages in identifying RNA-RBP interactions. Furthermore, the four motifs learned by our method are verified against existing motif databases, indicating that it can discover biologically meaningful circRNA-RBP binding patterns.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets.

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data

BackgroundProtein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences.MethodsThis study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif ‘logos’. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site.ResultsA database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html). A stand-alone program, KinasePredictor, is provided (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site.ConclusionsThis study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue.-q3N7pjxaL8wVBVRsJ9wBTVideo

RhizoBindingSites v2.0 Is a Bioinformatic Database of DNA Motifs Potentially Involved in Transcriptional Regulation Deduced From Their Genomic Sites.

RhizoBindingSites is a de novo depurified database of conserved DNA motifs potentially involved in the transcriptional regulation of the Rhizobium, Sinorhizobium, Bradyrhizobium, Azorhizobium, and Mesorhizobium genera covering 9 representative symbiotic species, deduced from the upstream regulatory sequences of orthologous genes (O-matrices) from the Rhizobiales taxon. The sites collected with O-matrices per gene per genome from RhizoBindingSites were used to deduce matrices using the dyad-Regulatory Sequence Analysis Tool (RSAT) method, giving rise to novel S-matrices for the construction of the RizoBindingSites v2.0 database. A comparison of the S-matrix logos showed a greater frequency and/or re-definition of specific-position nucleotides found in the O-matrices. Moreover, S-matrices were better at detecting genes in the genome, and there was a more significant number of transcription factors (TFs) in the vicinity than O-matrices, corresponding to a more significant genomic coverage for S-matrices. O-matrices of 3187 TFs and S-matrices of 2754 TFs from 9 species were deposited in RhizoBindingSites and RhizoBindingSites v2.0, respectively. The homology between the matrices of TFs from a genome showed inter-regulation between the clustered TFs. In addition, matrices of AraC, ArsR, GntR, and LysR ortholog TFs showed different motifs, suggesting distinct regulation. Benchmarking showed 72%, 68%, and 81% of common genes per regulon for O-matrices and approximately 14% less common genes with S-matrices of Rhizobium etli CFN42, Rhizobium leguminosarum bv. viciae 3841, and Sinorhizobium meliloti 1021. These data were deposited in RhizoBindingSites and the RhizoBindingSites v2.0 database (http://rhizobindingsites.ccg.unam.mx/).

A New Strategy for Mapping Epitopes of LACK and PEPCK Proteins of Leishmania amazonensis Specific for Major Histocompatibility Complex Class I.

Leishmaniasis represents a complex of diseases with a broad clinical spectrum and epidemiological diversity, considered a major public health problem. Although there is treatment, there are still no vaccines for cutaneous leishmaniasis. Because Leishmania spp. is an intracellular protozoan with several escape mechanisms, a vaccine must provoke cellular and humoral immune responses. Previously, we identified the Leishmania homolog of receptors for activated C kinase (LACK) and phosphoenolpyruvate carboxykinase (PEPCK) proteins as strong immunogens and candidates for the development of a vaccine strategy. The present work focuses on the in silico prediction and characterization of antigenic epitopes that might interact with mice or human major histocompatibility complex class I. After immunogenicity prediction on the Immune Epitope Database (IEDB) and the Database of MHC Ligands and Peptide Motifs (SYFPEITHI), 26 peptides were selected for interaction assays with infected mouse lymphocytes by flow cytometry and ELISpot. This strategy identified nine antigenic peptides (pL1-H2, pPL3-H2, pL10-HLA, pP13-H2, pP14-H2, pP15-H2, pP16-H2, pP17-H2, pP18-H2, pP26-HLA), which are strong candidates for developing a peptide vaccine against leishmaniasis.

Ehrlichia effector SLiM-icry: Artifice of cellular subversion.

As an obligately intracellular bacterial pathogen that selectively infects the mononuclear phagocyte, Ehrlichia chaffeensis has evolved sophisticated mechanisms to subvert innate immune defenses. While the bacterium accomplishes this through a variety of mechanisms, a rapidly expanding body of evidence has revealed that E. chaffeensis has evolved survival strategies that are directed by the versatile, intrinsically disordered, 120 kDa tandem repeat protein (TRP120) effector. E. chaffeensis establishes infection by manipulating multiple evolutionarily conserved cellular signaling pathways through effector-host interactions to subvert innate immune defenses. TRP120 activates these pathways using multiple functionally distinct, repetitive, eukaryote-mimicking short linear motifs (SLiMs) located within the tandem repeat domain that have evolved in nihilo. Functionally, the best characterized TRP120 SLiMs mimic eukaryotic ligands (SLiM-icry) to engage pathway-specific host receptors and activate cellular signaling, thereby repurposing these pathways to promote infection. Moreover, E. chaffeensis TRP120 contains SLiMs that are targets of post-translational modifications such as SUMOylation in addition to many other validated SLiMs that are curated in the eukaryotic linear motif (ELM) database. This review will explore the extracellular and intracellular roles TRP120 SLiM-icry plays during infection - mediated through a variety of SLiMs - that enable E. chaffeensis to subvert mononuclear phagocyte innate defenses.

JUND Promotes Tumorigenesis via Specifically Binding on Enhancers of Multiple Oncogenes in Cervical Cancer.

Enhancers are genomic regulatory elements located distally from the target gene, which play a critical role in determining cell identity and function. Dysregulation of enhancers has been frequently observed in various types of cancer, including cervical cancer. However, the identity of enhancers and their associated transcriptional regulators that are involved in cervical cancer remains unclear. With bioinformatics and 3D genomics, we revealed the enhancers in cervical cancer cell line and calculated which transcription factor (TF) is specifically binding on them based on TFs motif database. We knockdowned this TF and studied its function in cervical cancer cell line in vivo and in vitro. We found 14,826 activated enhancers and predicted that JUND (JunD Proto-Oncogene) is relatively enriched in the sequences of these enhancers. Well-known oncogene MYC and JUN were regulated by JUND through enhancers. To further explore the roles of JUND in cervical cancer, we analyzed the gene expression data of clinical cervical cancer samples and knock-downed JUND by CRISPR-Cas9 in Hela cell line. We found JUND is over-expressed in cervical cancer and the expression of JUND increased along with the cervical cancer progresses. Knockdown of JUND decreased the proliferation of Hela cells in vitro and in vivo and blocked cell cycle in G1-phase. Transcriptome sequencing analysis revealed the identification of 2,231 differentially expressed genes in response to the JUND knockdown treatment. This perturbation resulted in the modulation of several biological processes and pathways that have been previously linked to cancer. These findings provide evidence for the significant involvement of JUND in cervical cancer pathogenesis, thereby positioning JUND as a potential therapeutic target for the treatment of this disease.

Predicted cellular interactors of the endogenous retrovirus-K protease enzyme

Retroviral proteases are essential enzymes for viral replication and drive changes within the cellular proteome. While several studies have demonstrated that protease (PR) enzymes from exogenous retroviruses cleave cellular proteins and modulate cellular signaling, the impact of PRs encoded by endogenous retroviruses within the human genome has been largely overlooked. One human symbiont called Endogenous retrovirus-K (ERVK) is pathologically associated with both neurological disease and cancers. Using a computational biology approach, we sought to characterize the ERVK PR interactome. The ERVK PR protein sequence was analyzed using the Eukaryotic Linear Motif (ELM) database and results compared to ELMs of other betaretroviral PRs and similar endogenated viral PRs. A list of putative ERVK PR cellular protein interactors was curated from the ELM list and submitted for STRING analysis to generate an ERVK PR interactome. Reactome analysis was used to identify key pathways potentially influenced by ERVK PR. Network analysis postulated that ERVK PR interacts at the apex of several ubiquitination pathways, as well as has a role in the DNA damage response, gene regulation, and intracellular trafficking. Among retroviral PRs, a predicted interaction with proliferating cell nuclear antigen (PCNA) was unique to ERVK PR. The most prominent disease-associated pathways identified were viral carcinogenesis and neurodegeneration. This strengthens the role of ERVK PR in these pathologies by putatively driving alterations in cellular signaling cascades via select protein-protein interactions.

Does Group Contact Shape Styles of Pictorial Representation? A Case Study of Australian Rock Art.

Image-making is a nearly universal human behavior, yet the visual strategies and conventions to represent things in pictures vary greatly over time and space. In particular, pictorial styles can differ in their degree of figurativeness, varying from intersubjectively recognizable representations of things to very stylized and abstract forms. Are there any patterns to this variability, and what might its ecological causes be? Experimental studies have shown that demography and the structure of interaction of cultural groups can play a key role: the greater the degree of contact with other groups, the more recognizable and less abstract are the representations. Here we test this hypothesis on a real-world dataset for the first time. We constructed a balanced database of Indigenous Australian rock art motifs from both isolated and contact Aboriginal groups (those often in contact with other groups). We then ran a survey asking participants to judge the recognizability of the motifs and to provide interpretations. Results show that motifs from contact Aboriginal groups were more likely to be judged as inter-subjectively recognizable and also elicited more convergent descriptions than motifs from isolated groups. This is consistent with the idea that intergroup contact is likely to be an important factor in the cultural evolution of pictorial representation. We discuss the implications of these findings for the archaeology and anthropology of art, and the parallels with language evolution.

RdRp-scan: A bioinformatic resource to identify and annotate divergent RNA viruses in metagenomic sequence data.

Despite a rapid expansion in the number of documented viruses following the advent of metagenomic sequencing, the identification and annotation of highly divergent RNA viruses remain challenging, particularly from poorly characterized hosts and environmental samples. Protein structures are more conserved than primary sequence data, such that structure-based comparisons provide an opportunity to reveal the viral 'dusk matter': viral sequences with low, but detectable, levels of sequence identity to known viruses with available protein structures. Here, we present a new open computational resource-RdRp-scan-that contains a standardized bioinformatic toolkit to identify and annotate divergent RNA viruses in metagenomic sequence data based on the detection of RNA-dependent RNA polymerase (RdRp) sequences. By combining RdRp-specific hidden Markov models (HMMs) and structural comparisons, we show that RdRp-scan can efficiently detect RdRp sequences with identity levels as low as 10 per cent to those from known viruses and not identifiable using standard sequence-to-sequence comparisons. In addition, to facilitate the annotation and placement of newly detected and divergent virus-like sequences into the diversity of RNA viruses, RdRp-scan provides new custom and curated databases of viral RdRp sequences and core motifs, as well as pre-built RdRp multiple sequence alignments. In parallel, our analysis of the sequence diversity detected by the RdRp-scan revealed that while most of the taxonomically unassigned RdRps fell into pre-established clusters, some fell into potentially new orders of RNA viruses related to the Wolframvirales and Tolivirales. Finally, a survey of the conserved A, B, and C RdRp motifs within the RdRp-scan sequence database revealed additional variations of both sequence and position that might provide new insights into the structure, function, and evolution of viral polymerases.

Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences.

RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. Supplementary data are available at Bioinformatics online.

Abstract 2692: Identification of functional single nucleotide polymorphisms in cryptic exon 3 of the androgen receptor gene

Abstract Castration resistant prostate cancer (CRPC) involves the upregulation of androgen receptor variants (AR-Vs), of which AR-V7 is clinically the most relevant. By lacking the ligand-binding domain, AR-V7 is constitutively active and may therefore bypass AR signaling inhibitors. Treating CRPC involves improved AR signaling inhibitors or taxane-based chemotherapy. However, the optimal treating sequence may differ from patient to patient. The presence of AR-V7 in the nucleus of circulating PCa cells, which is associated with AR targeted therapy resistance, is used as a therapy-guiding biomarker. This stratification has a high predictive power and specificity, but its sensitivity is unsatisfying and can only be done by highly specialized laboratories. A PCR/sequencing based test would improve the sensitivity of this biomarker, but it requires a better understanding of AR-V7’s functional regulation. Therefore, we aim here to analyze single nucleotide polymorphisms (SNPs) in the 3’ untranslated region (UTR) of AR-V7 that might have an impact on AR-V7’s expression and/or functional activity. The LD trait tool of NIH was used to search for trait association with AR cryptic exon (CE3) SNPs. Four RNA binding motif databases (oRNAment, ATtRACT, RBP map, and RBPDB) and SpliceAid v2 were used for evaluating SNPs in their capacity to alter motifs. By CRISPR/CAS9 mediated non-homologous end joining (NHEJ) these predictions were assessed experimentally using qPCR and Westen Blot. SNP rs5918762 was selected as it is in linkage disequilibrium with several PCa tagSNPs (rs5919393, rs5919432). rs5918762T/C is characterized by an unequal minor allele frequency (MAF) distribution among different populations. Using an in silico approach, this SNP is predicted to destroy/create a binding site for the splicing machinery involved protein SRSF9. Disrupting this SNP region via NHEJ resulted respectively in an increased and decreased expression of AR-FL and AR-V7 mRNA in 22RV1 cells (C allele). Additionally, analysis of the SU2C dataset (CRPC patients) correlated SRSF9 expression with AR-V7 expression. Subsequently, SRSF9 knock out (KO) resulted in reduced AR-V7 mRNA expression. Our data suggests that SNP rs5918762T/C is located in a region important for CE3 inclusion during AR splicing, which might involve SRSF9. Further validation of these results is in need and encompasses splicing assays by an AR-V7 mini-gene assay, protein-RNA interaction assays and an analysis of a lack of effect in cell lines expressing the T allele. Citation Format: Jasper Van Goubergen, Florian Handle, Marcus V. Cronauer, Frédéric R. Santer. Identification of functional single nucleotide polymorphisms in cryptic exon 3 of the androgen receptor gene [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2692.

Comparative Analysis of Structural Features in SLiMs from Eukaryotes, Bacteria, and Viruses with Importance for Host-Pathogen Interactions.

Protein-protein interactions drive functions in eukaryotes that can be described by short linear motifs (SLiMs). Conservation of SLiMs help illuminate functional SLiMs in eukaryotic protein families. However, the simplicity of eukaryotic SLiMs makes them appear by chance due to mutational processes not only in eukaryotes but also in pathogenic bacteria and viruses. Further, functional eukaryotic SLiMs are often found in disordered regions. Although proteomes from pathogenic bacteria and viruses have less disorder than eukaryotic proteomes, their proteins can successfully mimic eukaryotic SLiMs and disrupt host cellular function. Identifying important SLiMs in pathogens is difficult but essential for understanding potential host-pathogen interactions. We performed a comparative analysis of structural features for experimentally verified SLiMs from the Eukaryotic Linear Motif (ELM) database across viruses, bacteria, and eukaryotes. Our results revealed that many viral SLiMs and specific motifs found across viruses and eukaryotes, such as some glycosylation motifs, have less disorder. Analyzing the disorder and coil properties of equivalent SLiMs from pathogens and eukaryotes revealed that some motifs are more structured in pathogens than their eukaryotic counterparts and vice versa. These results support a varying mechanism of interaction between pathogens and their eukaryotic hosts for some of the same motifs.

Development of a bacterial regulatory motif database

O b j e c t i v e s . The amount of data generated by modern methods of high-throughput sequencing is such that their analysis is performed mainly in automatic mode. In particular, the use of newly decoded genomic sequences is possible only after the annotation of functional elements of the genome, which, as a rule, is performed by automatic pipelines. Such annotation pipelines do a good job to identify the genes, but none of them annotate regulatory elements. Without these elements it is not possible to understand when and how genes can be expressed. Information on the regulatory elements of bacteria is collected in several specialized databases (RegulonDB, CollecTF, Prodoric2, etc.), however, only a part of this information can be used for annotation of regulatory elements, and only for a very limited range of bacteria. Previously, we proposed a clear formal criterion for applying regulatory information to any bacterial genome. Such a criterion is the CR tag, a sequence of amino acid residues of a transcriptional regulator that specifically contacts the nitrogenous bases of regulatory element in genomic DNA. The mathematical model of a regulatory element (motif) associated with a CR tag can be correctly applied to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The accumulation of motifs associated with CR tags raised the question of their ordered storage for the convenience of subsequent use in the annotation of genomic sequences. Since no one of well-known databases uses the concept of CR tags, a new database ought to be developed. Thus, the goal of this work is to create a database with information about bacterial transcription factors and DNA sequences recognized by them, suitable for annotation of regulatory sequences in bacterial genomes.M e t h o d s . Infological modeling of the subject area was carried out using the IDEF1X methodology. The database was developed using the Microsoft SQL Server DBMS. A cross-platform application for importing data into a database is written in C++ using Qt technology.Re s u l t s . As a result of the study of the subject area, a relational data model was developed and implemented in the Microsoft SQL Server DBMS, which allows holistic storage of information about accumulated transcription regulation motifs in bacteria, including information about the publications confirming their correctness. To automate the process of entering accumulated data, a cross-platform application was developed for importing structured data on transcription factors.Co n c l u s i o n . The main difference of the developed database is the use of CR-tag concept. Records of mathematical models of regulatory elements (motifs) in the database are associated with a CR tag and, therefore, can be correctly used to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The developed database will provide structured and holistic data storage, as well as their quick search when used in the pipeline for automatic annotation of regulatory elements in bacterial genomic sequences.

Structure-conditioned amino-acid couplings: How contact geometry affects pairwise sequence preferences.

Relating a protein's sequence to its conformation is a central challenge for both structure prediction and sequence design. Statistical contact potentials, as well as their more descriptive versions that account for side‐chain orientation and other geometric descriptors, have served as simplistic but useful means of representing second‐order contributions in sequence–structure relationships. Here we ask what happens when a pairwise potential is conditioned on the fully defined geometry of interacting backbones fragments. We show that the resulting structure‐conditioned coupling energies more accurately reflect pair preferences as a function of structural contexts. These structure‐conditioned energies more reliably encode native sequence information and more highly correlate with experimentally determined coupling energies. Clustering a database of interaction motifs by structure results in ensembles of similar energies and clustering them by energy results in ensembles of similar structures. By comparing many pairs of interaction motifs and showing that structural similarity and energetic similarity go hand‐in‐hand, we provide a tangible link between modular sequence and structure elements. This link is applicable to structural modeling, and we show that scoring CASP models with structured‐conditioned energies results in substantially higher correlation with structural quality than scoring the same models with a contact potential. We conclude that structure‐conditioned coupling energies are a good way to model the impact of interaction geometry on second‐order sequence preferences.