Transient non-specific DNA binding dominates the target search of bacterial DNA-binding proteins.

Despite their diverse biochemical characteristics and functions, all DNA-binding proteins share the ability to accurately locate their target sites among the vast excess of non-target DNA. Toward identifying universal mechanisms of the target search, we used single-molecule tracking of 11 diverse DNA-binding proteins in living Escherichia coli. The mobility of these proteins during the target search was dictated by DNA interactions rather than by their molecular weights. By generating cells devoid of all chromosomal DNA, we discovered that the nucleoid is not a physical barrier for protein diffusion but significantly slows the motion of DNA-binding proteins through frequent short-lived DNA interactions. The representative DNA-binding proteins (irrespective of their size, concentration, or function) spend the majority (58%-99%) of their search time bound to DNA and occupy as much as ∼30% of the chromosomal DNA at any time. Chromosome crowding likely has important implications for the function of all DNA-binding proteins.

Sentinels of chromatin: chromodomain helicase DNA-binding proteins in development and disease.

Chromatin is highly dynamic, undergoing continuous global changes in its structure and type of histone and DNA modifications governed by processes such as transcription, repair, replication, and recombination. Members of the chromodomain helicase DNA-binding (CHD) family of enzymes are ATP-dependent chromatin remodelers that are intimately involved in the regulation of chromatin dynamics, altering nucleosomal structure and DNA accessibility. Genetic studies in yeast, fruit flies, zebrafish, and mice underscore essential roles of CHD enzymes in regulating cellular fate and identity, as well as proper embryonic development. With the advent of next-generation sequencing, evidence is emerging that these enzymes are subjected to frequent DNA copy number alterations or mutations and show aberrant expression in malignancies and other human diseases. As such, they might prove to be valuable biomarkers or targets for therapeutic intervention.

PlDBPred: a novel computational model for discovery of DNA binding proteins in plants.

DNA-binding proteins (DBPs) play crucial roles in numerous cellular processes including nucleotide recognition, transcriptional control and the regulation of gene expression. Majority of the existing computational techniques for identifying DBPs are mainly applicable to human and mouse datasets. Even though some models have been tested on Arabidopsis, they produce poor accuracy when applied to other plant species. Therefore, it is imperative to develop an effective computational model for predicting plant DBPs. In this study, we developed a comprehensive computational model for plant specific DBPs identification. Five shallow learning and six deep learning models were initially used for prediction, where shallow learning methods outperformed deep learning algorithms. In particular, support vector machine achieved highest repeated 5-fold cross-validation accuracy of 94.0% area under receiver operating characteristic curve (AUC-ROC) and 93.5% area under precision recall curve (AUC-PR). With an independent dataset, the developed approach secured 93.8% AUC-ROC and 94.6% AUC-PR. While compared with the state-of-art existing tools by using an independent dataset, the proposed model achieved much higher accuracy. Overall results suggest that the developed computational model is more efficient and reliable as compared to the existing models for the prediction of DBPs in plants. For the convenience of the majority of experimental scientists, the developed prediction server PlDBPred is publicly accessible at https://iasri-sg.icar.gov.in/pldbpred/.The source code is also provided at https://iasri-sg.icar.gov.in/pldbpred/source_code.php for prediction using a large-size dataset.

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

Generation of site-specifically labelled fluorescent human XPA to investigate DNA binding dynamics during nucleotide excision repair.

Nucleotide excision repair (NER) promotes genomic integrity by removing bulky DNA adducts introduced by external factors such as ultraviolet light. Defects in NER enzymes are associated with pathological conditions such as Xeroderma Pigmentosum, trichothiodystrophy, and Cockayne syndrome. A critical step in NER is the binding of the Xeroderma Pigmentosum group A protein (XPA) to the ss/ds DNA junction. To better capture the dynamics of XPA interactions with DNA during NER we have utilized the fluorescence enhancement through non-canonical amino acids (FEncAA) approach. 4-azido-L-phenylalanine (4AZP or pAzF) was incorporated at Arg-158 in human XPA and conjugated to Cy3 using strain-promoted azide-alkyne cycloaddition. The resulting fluorescent XPA protein (XPACy3) shows no loss in DNA binding activity and generates a robust change in fluorescence upon binding to DNA. Here we describe methods to generate XPACy3 and detail in vitro experimental conditions required to stably maintain the protein during biochemical and biophysical studies.

E. coli cells advance into phase-separated (biofilm-simulating) extracellular polymeric substance containing DNA, HU, and lipopolysaccharide.

We have previously shown that the nucleoid-associated protein, HU, uses its DNA-binding surfaces to bind to bacterial outer-membrane lipopolysaccharide (LPS), causing HU to act as a glue aiding the adherence of DNA to bacteria, e.g., in biofilms. We have also previously shown that HU and DNA coacervate into a state of liquid-liquid phase separation (LLPS), within bacterial cells and also in vitro. Here, we show that HU and free LPS (which is ordinarily shed by bacteria) also condense into a state of phase separation. Coacervates of HU, DNA, and free LPS are less liquid-like than coacervates of HU and DNA. Escherichia coli cells bearing LPS on their surfaces are shown to adhere to (as well as advance into) coacervates of HU and DNA. HU appears to play a role, therefore, in maintaining both intracellular and extracellular states of phase separation with DNA that are compatible with LPS and LPS-bearing E. coli, with LPS determining the liquidity of the biofilm-simulating coacervate. IMPORTANCE: Understanding the constitution and behavior of biofilms is crucial to understanding how to deal with persistent biofilms. This study, together with other recent studies from our group, elucidates a novel aspect of the extracellular polymeric substance (EPS) of Escherichia coli biofilms, by creating a simulacrum of the EPS and then demonstrating that its formation involves liquid-liquid phase separation (LLPS) of HU, DNA, and lipopolysaccharide (LPS) components, with LPS determining the liquidity of this EPS simulacrum. The findings provide insight into the nature of biofilms and into how the interplay of HU, DNA, and LPS could modulate the structural integrity and functional dynamics of biofilms.

The CTP-binding domain is disengaged from the DNA-binding domain in a cocrystal structure of Bacillus subtilis Noc-DNA complex.

In Bacillus subtilis, a ParB-like nucleoid occlusion protein (Noc) binds specifically to Noc-binding sites (NBSs) on the chromosome to help coordinate chromosome segregation and cell division. Noc does so by binding to CTP to form large membrane-associated nucleoprotein complexes to physically inhibit the assembly of the cell division machinery. The site-specific binding of Noc to NBS DNA is a prerequisite for CTP-binding and the subsequent formation of a membrane-active DNA-entrapped protein complex. Here, we solve the structure of a C-terminally truncated B. subtilis Noc bound to NBS DNA to reveal the conformation of Noc at this crucial step. Our structure reveals the disengagement between the N-terminal CTP-binding domain and the NBS-binding domain of each DNA-bound Noc subunit; this is driven, in part, by the swapping of helices 4 and 5 at the interface of the two domains. Site-specific crosslinking data suggest that this conformation of Noc-NBS exists in solution. Overall, our results lend support to the recent proposal that parS/NBS binding catalyzes CTP binding and DNA entrapment by preventing the reengagement of the CTP-binding domain and the DNA-binding domain from the same ParB/Noc subunit.

Interactions between Fkh1 monomers stabilize its binding to DNA replication origins.

Eukaryotic DNA replication is initiated from multiple genomic origins, which can be broadly categorized as firing early or late in the S phase. Several factors can influence the temporal usage of origins to determine the timing of their firing. In budding yeast, the Forkhead family proteins Fkh1 and Fkh2 bind to a subset of replication origins and activate them at the beginning of the S phase. In these origins, the Fkh1/2 binding sites are arranged in a strict configuration, suggesting that Forkhead factors must bind the origins in a specific manner. To explore these binding mechanisms in more detail, we mapped the domains of Fkh1 that were required for its role in DNA replication regulation. We found that a short region of Fkh1 near its DNA binding domain was essential for the protein to bind and activate replication origins. Analysis of purified Fkh1 proteins revealed that this region mediates dimerization of Fkh1, suggesting that intramolecular contacts of Fkh1 are required for efficient binding and regulation of DNA replication origins. We also show that the Sld3-Sld7-Cdc45 complex is recruited to Forkhead-regulated origins already in the G1 phase and that Fkh1 is constantly required to keep these factors bound on origins before the onset of the S phase. Together, our results suggest that dimerization-mediated stabilization of DNA binding by Fkh1 is crucial for its ability to activate DNA replication origins.

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks.

The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conventional experimental methods for identifying DBPs are usually time-consuming and expensive. Therefore, there is an urgent need to develop rapid and efficient computational methods for the prediction of DBPs. In this study, we proposed a novel predictor named PreDBP-PLMs to further improve the identification accuracy of DBPs by fusing the pre-trained protein language model (PLM) ProtT5 embedding with evolutionary features as input to the classic convolutional neural network (CNN) model. Firstly, the ProtT5 embedding was combined with different evolutionary features derived from the position-specific scoring matrix (PSSM) to represent protein sequences. Then, the optimal feature combination was selected and input to the CNN classifier for the prediction of DBPs. Finally, the 5-fold cross-validation (CV), the leave-one-out CV (LOOCV), and the independent set test were adopted to examine the performance of PreDBP-PLMs on the benchmark datasets. Compared to the existing state-of-the-art predictors, PreDBP-PLMs exhibits an accuracy improvement of 0.5 % and 5.2 % on the PDB186 and PDB2272 datasets, respectively. It demonstrated that the proposed method could serve as a useful tool for the recognition of DBPs.

Assessment of ID family proteins expression in colorectal cancer of Iraqi patients.

BACKGROUND: Colorectal cancer (CRC) is the second most deathly worldwide and third most common cancer, CRC is a very heterogeneous disease where tumors can form by both environmental and genetic risk factors and includes epigenetic and genetic alternations. Inhibitors of DNA binding proteins (ID) are a class of helix-loop-helix transcription regulatory factors; these proteins are considered a family of four highly preserved transcriptional regulators (ID1-4), shown to play significant roles in many processes that are associated with tumor development. ID family plays as negatively dominant antagonists of other essential HLH proteins, concluding the creation of non-functional heterodimers and regulation of the transcription process. MATERIALS AND METHODS: 120 Fresh tissue and blood samples Forty (40) samples of fresh tissue and blood were collected from patients diagnosed with CRC, twenty (20) samples were collected from a patient diagnosed as healthy. The (qRT-PCR) method is a sensitive technique for the quantifying of steady-state mRNA levels that used to evaluation the expression levels of ID (1-4) gene. RESULTS: The findings indicate downregulation in ID1 in tissue with a highly significant change between patients and control groups, where upregulation in the ID1 gene is shown in blood samples.ID2 gene also demonstrated high significant change where show upregulation in tissue and downregulation in blood sample. ID3 and ID4 genes show downregulation in tissue and blood samples with a significant change in ID3 blood samples between patient and blood groups. CONCLUSION: Because of the regulation function of the ID family in many processes, the up or down regulation of IDs genes in tumors Proves how important its tumor development, and therefore those proteins can be used as an indicator for CRC.

Molecular mechanism of the repressive phase of the mammalian circadian clock.

The mammalian circadian clock consists of a transcription-translation feedback loop (TTFL) composed of CLOCK-BMAL1 transcriptional activators and CRY-PER transcriptional repressors. Previous work showed that CRY inhibits CLOCK-BMAL1-activated transcription by a "blocking"-type mechanism and that CRY-PER inhibits CLOCK-BMAL1 by a "displacement"-type mechanism. While the mechanism of CRY-mediated repression was explained by both in vitro and in vivo experiments, the CRY-PER-mediated repression in vivo seemed in conflict with the in vitro data demonstrating PER removes CRY from the CLOCK-BMAL1-E-box complex. Here, we show that CRY-PER participates in the displacement-type repression by recruiting CK1δ to the nucleus and mediating an increased local concentration of CK1δ at CLOCK-BMAL1-bound promoters/enhancers and thus promoting the phosphorylation of CLOCK and dissociation of CLOCK-BMAL1 along with CRY from the E-box. Our findings bring clarity to the role of PER in the dynamic nature of the repressive phase of the TTFL.

Double Stranded DNA Binding Stapled Peptides: An Emerging Tool for Transcriptional Regulation.

Stapled peptides have rapidly established themselves as a powerful technique to mimic α-helical interactions with a short peptide sequence. There are many examples of stapled peptides that successfully disrupt α-helix-mediated protein-protein interactions, with an example currently in clinical trials. DNA-protein interactions are also often mediated by α-helices and are involved in all transcriptional regulation processes. Unlike DNA-binding small molecules, which typically lack DNA sequence selectivity, DNA-binding proteins bind with high affinity and high selectivity. These are ideal candidates for the design DNA-binding stapled peptides. Despite the parallel to protein-protein interaction disrupting stapled peptides and the need for sequence specific DNA binders, there are very few DNA-binding stapled peptides. In this review we examine all the known DNA-binding stapled peptides. Their design concepts are compared to stapled peptides that disrupt protein-protein interactions and based on the few examples in the literature, DNA-binding stapled peptide trends are discussed.

An in silico approach to identification, categorization and prediction of nucleic acid binding proteins.

The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein-nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation.

LysR-type transcriptional regulators: state of the art.

The LysR-type transcriptional regulators (LTTRs) are DNA-binding proteins present in bacteria, archaea, and in algae. Knowledge about their distribution, abundance, evolution, structural organization, transcriptional regulation, fundamental roles in free life, pathogenesis, and bacteria-plant interaction has been generated. This review focuses on these aspects and provides a current picture of LTTR biology.

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is imperative to design an efficient and accurate identification tool. Considering the time-consuming and expensive traditional experimental technology and the insufficient number of samples in the biological computing method based on structural information, we proposed a machine learning algorithm based on sequence information to identify DNA binding proteins, named multi-view Least Squares Support Vector Machine via Hilbert-Schmidt Independence Criterion (multi-view LSSVM via HSIC). This method took 6 feature sets as multi-view input and trains a single view through the LSSVM algorithm. Then, we integrated HSIC into LSSVM as a regular term to reduce the dependence between views and explored the complementary information of multiple views. Subsequently, we trained and coordinated the submodels and finally combined the submodels in the form of weights to obtain the final prediction model. On training set PDB1075, the prediction results of our model were better than those of most existing methods. Independent tests are conducted on the datasets PDB186 and PDB2272. The accuracy of the prediction results was 85.5% and 79.36%, respectively. This result exceeded the current state-of-the-art methods, which showed that the multi-view LSSVM via HSIC can be used as an efficient predictor.

Unravelling How Single-Stranded DNA Binding Protein Coordinates DNA Metabolism Using Single-Molecule Approaches.

Single-stranded DNA-binding proteins (SSBs) play vital roles in DNA metabolism. Proteins of the SSB family exclusively and transiently bind to ssDNA, preventing the DNA double helix from re-annealing and maintaining genome integrity. In the meantime, they interact and coordinate with various proteins vital for DNA replication, recombination, and repair. Although SSB is essential for DNA metabolism, proteins of the SSB family have been long described as accessory players, primarily due to their unclear dynamics and mechanistic interaction with DNA and its partners. Recently-developed single-molecule tools, together with biochemical ensemble techniques and structural methods, have enhanced our understanding of the different coordination roles that SSB plays during DNA metabolism. In this review, we discuss how single-molecule assays, such as optical tweezers, magnetic tweezers, Förster resonance energy transfer, and their combinations, have advanced our understanding of the binding dynamics of SSBs to ssDNA and their interaction with other proteins partners. We highlight the central coordination role that the SSB protein plays by directly modulating other proteins' activities, rather than as an accessory player. Many possible modes of SSB interaction with protein partners are discussed, which together provide a bigger picture of the interaction network shaped by SSB.

How structural biology transformed studies of transcription regulation.

The past 4 decades have seen remarkable advances in our understanding of the structural basis of gene regulation. Technological advances in protein expression, nucleic acid synthesis, and structural biology made it possible to study the proteins that regulate transcription in the context of ever larger complexes containing proteins bound to DNA. This review, written on the occasion of the 50th anniversary of the founding of the Protein Data Bank focuses on the insights gained from structural studies of protein-DNA complexes and the role the PDB has played in driving this research. I cover highlights in the field, beginning with X-ray crystal structures of the first DNA-binding domains to be studied, through recent cryo-EM structures of transcription factor binding to nucleosomal DNA.

Assessment of the breadth of binding promiscuity of heme towards human proteins.

Heme regulates important biological processes by transient interactions with many human proteins. The goal of the present study was to assess extends of protein binding promiscuity of heme. To this end we evaluated interaction of heme with >9000 human proteins. Heme manifested high binding promiscuity by binding to most of the proteins in the array. Nevertheless, some proteins have outstanding heme binding capacity. Bioinformatics analyses revealed that apart from typical haemoproteins, these proteins are frequently involved in metal binding or have the potential to recognize DNA. This study can contribute for understanding the regulatory functions of labile heme.

bZIP transcription factors PcYap1 and PcRsmA link oxidative stress response to secondary metabolism and development in Penicillium chrysogenum.

BACKGROUND: Reactive oxygen species (ROS) trigger different morphogenic processes in filamentous fungi and have been shown to play a role in the regulation of the biosynthesis of some secondary metabolites. Some bZIP transcription factors, such as Yap1, AtfA and AtfB, mediate resistance to oxidative stress and have a role in secondary metabolism regulation. In this work we aimed to get insight into the molecular basis of this regulation in the industrially important fungus Penicillium chrysogenum through the characterization of the role played by two effectors that mediate the oxidative stress response in development and secondary metabolism. RESULTS: In P. chrysogenum, penicillin biosynthesis and conidiation are stimulated by the addition of H2O2 to the culture medium, and this effect is mediated by the bZIP transcription factors PcYap1 and PcRsmA. Silencing of expression of both proteins by RNAi resulted in similar phenotypes, characterized by increased levels of ROS in the cell, reduced conidiation, higher sensitivity of conidia to H2O2 and a decrease in penicillin production. Both PcYap1 and PcRsmA are able to sense H2O2-generated ROS in vitro and change its conformation in response to this stimulus. PcYap1 and PcRsmA positively regulate the expression of brlA, the first gene of the conidiation central regulatory pathway. PcYap1 binds in vitro to a previously identified regulatory sequence in the promoter of the penicillin gene pcbAB: TTAGTAA, and to a TTACTAA sequence in the promoter of the brlA gene, whereas PcRsmA binds to the sequences TGAGACA and TTACGTAA (CRE motif) in the promoters of the pcbAB and penDE genes, respectively. CONCLUSIONS: bZIP transcription factors PcYap1 and PcRsmA respond to the presence of H2O2-generated ROS and regulate oxidative stress response in the cell. Both proteins mediate ROS regulation of penicillin biosynthesis and conidiation by binding to specific regulatory elements in the promoters of key genes. PcYap1 is identified as the previously proposed transcription factor PTA1 (Penicillin Transcriptional Activator 1), which binds to the regulatory sequence TTAGTAA in the pcbAB gene promoter. This is the first report of a Yap1 protein directly regulating transcription of a secondary metabolism gene. A model describing the regulatory network mediated by PcYap1 and PcRsmA is proposed.

An Overview of Dps: Dual Acting Nanovehicles in Prokaryotes with DNA Binding and Ferroxidation Properties.

DNA binding proteins under starvation (Dps) are proteins belonging to the ferritin family with the capacity for DNA binding, in addition to iron storage and ferroxidation. Present only in the prokaryotes, these multifaceted proteins have been assigned with a number of roles, from pathogenesis to nucleoid condensation and protection. They have a significant role in protecting the cells from free radical assaults, indirectly by sequestration of iron and by directly binding to the DNA. Due to their symmetry, stability and biomineralization capacity, these proteins have ever increasing potential applications in biotechnology and drug delivery. This chapter tries to bring together all these aspects of Dps in the view of current understanding and older perspectives by studies of our group as well as other experts in the field.