Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

SpecLoop predicts cell type-specific chromatin loop via transcription factor cooperation.

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop.

Structural insights into the interaction between adenovirus C5 hexon and human lactoferrin.

Adenovirus (AdV) infection of the respiratory epithelium is common but poorly understood. Human AdV species C types, such as HAdV-C5, utilize the Coxsackie-adenovirus receptor (CAR) for attachment and subsequently integrins for entry. CAR and integrins are however located deep within the tight junctions in the mucosa where they would not be easily accessible. Recently, a model for CAR-independent AdV entry was proposed. In this model, human lactoferrin (hLF), an innate immune protein, aids the viral uptake into epithelial cells by mediating interactions between the major capsid protein, hexon, and yet unknown host cellular receptor(s). However, a detailed understanding of the molecular interactions driving this mechanism is lacking. Here, we present a new cryo-EM structure of HAdV-5C hexon at high resolution alongside a hybrid structure of HAdV-5C hexon complexed with human lactoferrin (hLF). These structures reveal the molecular determinants of the interaction between hLF and HAdV-C5 hexon. hLF engages hexon primarily via its N-terminal lactoferricin (Lfcin) region, interacting with hexon's hypervariable region 1 (HVR-1). Mutational analyses pinpoint critical Lfcin contacts and also identify additional regions within hLF that critically contribute to hexon binding. Our study sheds more light on the intricate mechanism by which HAdV-C5 utilizes soluble hLF/Lfcin for cellular entry. These findings hold promise for advancing gene therapy applications and inform vaccine development. IMPORTANCE: Our study delves into the structural aspects of adenovirus (AdV) infections, specifically HAdV-C5 in the respiratory epithelium. It uncovers the molecular details of a novel pathway where human lactoferrin (hLF) interacts with the major capsid protein, hexon, facilitating viral entry, and bypassing traditional receptors such as CAR and integrins. The study's cryo-EM structures reveal how hLF engages hexon, primarily through its N-terminal lactoferricin (Lfcin) region and hexon's hypervariable region 1 (HVR-1). Mutational analyses identify critical Lfcin contacts and other regions within hLF vital for hexon binding. This structural insight sheds light on HAdV-C5's mechanism of utilizing soluble hLF/Lfcin for cellular entry, holding promise for gene therapy and vaccine development advancements in adenovirus research.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk.

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Human 2'-Deoxynucleoside 5'-Phosphate N-Hydrolase 1: The Catalytic Roles of Tyr24 and Asp80.

The human enzyme 2'-deoxynucleoside 5'-phosphate N-hydrolase 1 (HsDNPH1) catalyses the hydrolysis of 5-hydroxymethyl-2'-deoxyuridine 5'-phosphate to generate 5-hydroxymethyluracil and 2-deoxyribose-5-phosphate via a covalent 5-phospho-2-deoxyribosylated enzyme intermediate. HsDNPH1 is a promising target for inhibitor development towards anticancer drugs. Here, site-directed mutagenesis of conserved active-site residues, followed by HPLC analysis of the reaction and steady-state kinetics are employed to reveal the importance of each of these residues in catalysis, and the reaction pH-dependence is perturbed by each mutation. Solvent deuterium isotope effects indicate no rate-limiting proton transfers. Crystal structures of D80N-HsDNPH1 in unliganded and substrate-bound states, and of unliganded D80A- and Y24F-HsDNPH1 offer atomic level insights into substrate binding and catalysis. The results reveal a network of hydrogen bonds involving the substrate and the E104-Y24-D80 catalytic triad and are consistent with a proposed mechanism whereby D80 is important for substrate positioning, for helping modulate E104 nucleophilicity, and as the general acid in the first half-reaction. Y24 positions E104 for catalysis and prevents a catalytically disruptive close contact between E104 and D80.

Inferred regulons are consistent with regulator binding sequences in E. coli.

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins.

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Structural and functional insights into tRNA recognition by human tRNA guanine transglycosylase.

Eukaryotic tRNA guanine transglycosylase (TGT) is an RNA-modifying enzyme which catalyzes the base exchange of the genetically encoded guanine 34 of tRNAsAsp,Asn,His,Tyr for queuine, a hypermodified 7-deazaguanine derivative. Eukaryotic TGT is a heterodimer comprised of a catalytic and a non-catalytic subunit. While binding of the tRNA anticodon loop to the active site is structurally well understood, the contribution of the non-catalytic subunit to tRNA binding remained enigmatic, as no complex structure with a complete tRNA was available. Here, we report a cryo-EM structure of eukaryotic TGT in complex with a complete tRNA, revealing the crucial role of the non-catalytic subunit in tRNA binding. We decipher the functional significance of these additional tRNA-binding sites, analyze solution state conformation, flexibility, and disorder of apo TGT, and examine conformational transitions upon tRNA binding.

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.

Profiling of RNA-binding protein binding sites by in situ reverse transcription-based sequencing.

RNA-binding proteins (RBPs) regulate diverse cellular processes by dynamically interacting with RNA targets. However, effective methods to capture both stable and transient interactions between RBPs and their RNA targets are still lacking, especially when the interaction is dynamic or samples are limited. Here we present an assay of reverse transcription-based RBP binding site sequencing (ARTR-seq), which relies on in situ reverse transcription of RBP-bound RNAs guided by antibodies to identify RBP binding sites. ARTR-seq avoids ultraviolet crosslinking and immunoprecipitation, allowing for efficient and specific identification of RBP binding sites from as few as 20 cells or a tissue section. Taking advantage of rapid formaldehyde fixation, ARTR-seq enables capturing the dynamic RNA binding by RBPs over a short period of time, as demonstrated by the profiling of dynamic RNA binding of G3BP1 during stress granule assembly on a timescale as short as 10 minutes.

Altered Protein Dynamics and a More Reactive Catalytic Cysteine in a Neurodegeneration-associated UCHL1 Mutant.

A mutant of ubiquitin C-terminal hydrolase L1 (UCHL1) detected in early-onset neurodegenerative patients, UCHL1R178Q, showed higher catalytic activity than wild-type UCHL1 (UCHL1WT). Lying within the active-site pocket, the arginine is part of an interaction network that holds the catalytic histidine in an inactive arrangement. However, the structural basis and mechanism of enzymatic activation upon glutamine substitution was not understood. We combined X-ray crystallography, protein nuclear magnetic resonance (NMR) analysis, enzyme kinetics, covalent inhibition analysis, and biophysical measurements to delineate activating factors in the mutant. While the crystal structure of UCHL1R178Q showed nearly the same arrangement of the catalytic residues and active-site pocket, the mutation caused extensive alteration in the chemical environment and dynamics of more than 30 residues, some as far as 15 Å away from the site of mutation. Significant broadening of backbone amide resonances in the HSQC spectra indicates considerable backbone dynamics changes in several residues, in agreement with solution small-angle X-ray scattering (SAXS) analyses which indicate an overall increase in protein flexibility. Enzyme kinetics show the activation is due to a kcat effect despite a slightly weakened substrate affinity. In line with this, the mutant shows a higher second-order rate constant (kinact/Ki) in a reaction with a substrate-derived irreversible inhibitor, Ub-VME, compared to the wild-type enzyme, an observation indicative of a more reactive catalytic cysteine in the mutant. Together, the observations underscore structural plasticity as a factor contributing to enzyme kinetic behavior which can be modulated through mutational effects.

Expanded palette of RNA base editors for comprehensive RBP-RNA interactome studies.

RNA binding proteins (RBPs) are key regulators of RNA processing and cellular function. Technologies to discover RNA targets of RBPs such as TRIBE (targets of RNA binding proteins identified by editing) and STAMP (surveying targets by APOBEC1 mediated profiling) utilize fusions of RNA base-editors (rBEs) to RBPs to circumvent the limitations of immunoprecipitation (CLIP)-based methods that require enzymatic digestion and large amounts of input material. To broaden the repertoire of rBEs suitable for editing-based RBP-RNA interaction studies, we have devised experimental and computational assays in a framework called PRINTER (protein-RNA interaction-based triaging of enzymes that edit RNA) to assess over thirty A-to-I and C-to-U rBEs, allowing us to identify rBEs that expand the characterization of binding patterns for both sequence-specific and broad-binding RBPs. We also propose specific rBEs suitable for dual-RBP applications. We show that the choice between single or multiple rBEs to fuse with a given RBP or pair of RBPs hinges on the editing biases of the rBEs and the binding preferences of the RBPs themselves. We believe our study streamlines and enhances the selection of rBEs for the next generation of RBP-RNA target discovery.

Prediction and analysis of cis-regulatory elements in Dorsal and Ventral patterning genes of Tribolium castaneum and its comparison with Drosophila melanogaster.

Insect embryonic development and morphology are characterized by their anterior-posterior and dorsal-ventral (DV) patterning. In Drosophila embryos, DV patterning is mediated by a dorsal protein gradient which activates twist and snail proteins, the important regulators of DV patterning. To activate or repress gene expression, some regulatory proteins bind in clusters to their target gene at sites known as cis-regulatory elements or enhancers. To understand how variations in gene expression in different lineages might lead to different phenotypes, it is necessary to understand enhancers and their evolution. Drosophila melanogaster has been widely studied to understand the interactions between transcription factors and the transcription factor binding sites. Tribolium castaneum is an upcoming model animal which is catching the interest of biologists and the research on the enhancer mechanisms in the insect's axes patterning is still in infancy. Therefore, the current study was designed to compare the enhancers of DV patterning in the two insect species. The sequences of ten proteins involved in DV patterning of D. melanogaster were obtained from Flybase. The protein sequences of T. castaneum orthologous to those obtained from D. melanogaster were acquired from NCBI BLAST, and these were then converted to DNA sequences which were modified by adding 20 kb sequences both upstream and downstream to the gene. These modified sequences were used for further analysis. Bioinformatics tools (Cluster-Buster and MCAST) were used to search for clusters of binding sites (enhancers) in the modified DV genes. The results obtained showed that the transcription factors in Drosophila melanogaster and Tribolium castaneum are nearly identical; however, the number of binding sites varies between the two species, indicating transcription factor binding site evolution, as predicted by two different computational tools. It was observed that dorsal, twist, snail, zelda, and Supressor of Hairless are the transcription factors responsible for the regulation of DV patterning in the two insect species.

HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org.

Dissection of the role of a Src homology 3 domain in the evolution of binding preference of paralogous proteins.

Protein-protein interactions (PPIs) drive many cellular processes. Some interactions are directed by Src homology 3 (SH3) domains that bind proline-rich motifs on other proteins. The evolution of the binding specificity of SH3 domains is not completely understood, particularly following gene duplication. Paralogous genes accumulate mutations that can modify protein functions and, for SH3 domains, their binding preferences. Here, we examined how the binding of the SH3 domains of 2 paralogous yeast type I myosins, Myo3 and Myo5, evolved following duplication. We found that the paralogs have subtly different SH3-dependent interaction profiles. However, by swapping SH3 domains between the paralogs and characterizing the SH3 domains freed from their protein context, we find that very few of the differences in interactions, if any, depend on the SH3 domains themselves. We used ancestral sequence reconstruction to resurrect the preduplication SH3 domains and examined, moving back in time, how the binding preference changed. Although the most recent ancestor of the 2 domains had a very similar binding preference as the extant ones, older ancestral domains displayed a gradual loss of interaction with the modern interaction partners when inserted in the extant paralogs. Molecular docking and experimental characterization of the free ancestral domains showed that their affinity with the proline motifs is likely not the cause for this loss of binding. Taken together, our results suggest that a SH3 and its host protein could create intramolecular or allosteric interactions essential for the SH3-dependent PPIs, making domains not functionally equivalent even when they have the same binding specificity.

In Silico Prediction and Molecular Docking of SNPs in NRP1 Gene Associated with SARS-COV-2.

Neuropilin-1 (NRP1) which is a main transmembrane cell surface receptor acts as a host cell mediator resulting in increasing the SARS-Cov-2 infectivity and also plays a role in neuronal development, angiogenesis and axonal outgrowth. The goal of this study is to estimate the impact of single nucleotide polymorphisms (SNPs) in the NRP1 gene on the function, structure and stabilization of protein as well as on the miRNA-mRNA binding regions using bioinformatical tools. It is also aimed to investigate the changes caused by SNPs in NRP1 on interactions with drug molecule and spike protein. The missense type of SNPs was analyzed using SIFT, PolyPhen-2, SNAP2, PROVEAN, Mutation Assessor, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, STRING, Project HOPE, ConSurf, and PolymiRTS. Docking analyses were conducted by AutoDock Vina program. As a result, a total of 733 missense SNPs were determined within the NRP1 gene and nine SNPs were specified as damaging to the protein. The modelling results showed that wild and mutant type amino acids had some different properties such as size, charge, and hydrophobicity. Additionally, their three-dimensional structures of protein were utilized for confirmation of these differences. After evaluating the results, nine polymorphisms rs141633354, rs142121081, rs145954532, rs200028992, rs200660300, rs369312020, rs370117610, rs370551432, rs370641686 were determined to be damaging on the structure and function of NRP1 protein and located in conserved regions. The results of molecular docking showed that the binding affinity values are nearly the same for wild-type and mutant structures support that the mutations carried out are not in the focus of the binding site, therefore the ligand does not affect the binding energy. It is expected that the results will be useful for future studies.

Suppression of KLF5 basal expression in oral carcinoma-derived cells through three intact CREB1-binding sites in the silencer region.

OBJECTIVES: Krüppel-like factor (KLF)5, which is overexpressed in carcinomas such as oral cancer, inhibits epidermal differentiation. KLF5 induces dedifferentiation of carcinoma cells, which effectuates carcinoma progression; nevertheless, the regulatory mechanism affecting the transcription of the KLF5 gene remains ambiguous. METHODS: Transcriptional activity of the KLF5 silencer, specifically the 425-bp region (425-region), was examined using reporter assays. An additional analysis was conducted to assess the impact of the minimal essential region (MER) of KLF5 on its basal expression. The affinity of cAMP responsive element binding protein 1 (CREB1) for three potential CREB1-binding sites in the 425-region was analyzed using DNA pull-down and quantitative chromatin immunoprecipitation assays. Reporter assays employing a human oral squamous carcinoma cell line, HSC2, transfected with small interfering RNA or complementary DNA for CREB1, were performed to investigate the effect of CREB1 binding sites on MER activity. RESULTS: The 425-region exhibited no transcriptional activity and suppressed MER transcriptional activity. This region encodes three putative CREB1-binding sites, and CREB1 demonstrated equal binding affinity for all three sites. The deletion of each of these binding sites reduced CREB1 precipitation and enhanced MER activity. Endogenous CREB1 knockdown and overexpression elevated and reduced MER activity, respectively, at the intact sites. Conversely, site deletion hampered and improved MER activity upon CREB1 knockdown and overexpression, respectively. CONCLUSIONS: Suppression of KLF5 basal expression via CREB1 binding to the 425-region requires all three CREB1-binding sites to remain intact in oral carcinoma cells. Consequently, deletion of the CREB1-binding site relieves suppression of KLF5 basal expression.

Characteristics and catalytic mechanism of a novel multifunctional oxidase, CpmO, for chloramphenicols degradation from Sphingobium sp. WTD-1.

Chloramphenicols (CAPs) are ubiquitous emerging pollutants that threaten ecological environments and human health. Microbial and enzyme-based biodegradation strategies offer a cost-effective environmentally friendly approach for CAPs removal from contaminated sites. Here, CpmO, a novel multifunctional oxidase for CAP degradation was identified from the CAP-degrading strain Sphingobium sp. WTD-1. This enzyme was found to be responsible for both the oxidation of the C3-hydroxyl and oxidative cleavage of the C1-C2 bond of CAP, and the oxidative cleavage pathway of CAP was dominant. The catalytic efficiency of CpmO for CAP was 41.6 times that for thiamphenicol (TAP) under the optimal conditions (40 °C, pH 6.0). CpmO was identified as a member of the glucose-methanol-choline oxidoreductase family. Molecular docking and site-directed mutagenesis analysis indicated that CAP was connected to the key amino acid residues E231/E395, K277, and I273/A276 in CpmO through hydrogen bonding, nonclassical hydrogen bonding, and π-π stacking forces, respectively. The catalytic activities of the A276W, K277P, and E231S mutants were found to be 1.1 times, 6.4 times, and 13.2 times higher than that of the wild type, respectively. These findings provide genetic resources and theoretical guidance for future application in biotechnological and metabolic engineering efforts for the remediation of CAPs-contaminated environments.

Ascertainment Bias in the Genomic Test of Positive Selection on Regulatory Sequences.

Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF-binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of 3 TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here, we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied.

GCLiPP: global crosslinking and protein purification method for constructing high-resolution occupancy maps for RNA binding proteins.

GCLiPP is a global RNA interactome capture method that detects RNA-binding protein (RBP) occupancy transcriptome-wide. GCLiPP maps RBP-occupied sites at a higher resolution than phase separation-based techniques. GCLiPP sequence tags correspond with known RBP binding sites and are enriched for sites detected by RBP-specific crosslinking immunoprecipitation (CLIP) for abundant cytosolic RBPs. Comparison of human Jurkat T cells and mouse primary T cells uncovers shared peaks of GCLiPP signal across homologous regions of human and mouse 3' UTRs, including a conserved mRNA-destabilizing cis-regulatory element. GCLiPP signal overlapping with immune-related SNPs uncovers stabilizing cis-regulatory regions in CD5, STAT6, and IKZF1.