Protein-DNA interactions: amino acid conservation and the effects of mutations on binding specificity

Stability and dynamics of extradenticle modulates its function

Extradenticle (EXD) is a partner protein of the HOX transcription factors and plays an important role in the development of Drosophila. It confers increased affinity and specificity of DNA-binding to the HOX proteins. However, the DNA-binding homeodomain of EXD has a significantly weaker affinity to DNA compared to the HOX homeodomains. Here, we show that a glycine residue (G290) in the middle of the EXD DNA-binding helix primarily results in this weaker binding. Glycine destabilizes helices. To probe its role in the stability and function of the protein, G290 was mutated to alanine. The intrinsic stability of the DNA-binding helix increased in the G290A mutant as observed by NMR studies and molecular dynamics (MD) simulation. Also, NMR dynamics and MD simulation show that dynamic motions present in the wild-type protein are quenched in the mutant. This in turn resulted in increased stability of the entire homeodomain (ΔΔGG→A of -2.6 kcal/mol). Increased protein stability resulted in three-fold better DNA-binding affinity of the mutant as compared to the wild-type protein. Molecular mechanics with generalized Born and surface area solvation (MMGBSA) analysis of our MD simulation on DNA-bound models of both wild-type and mutant proteins shows that the contribution to binding is enhanced for most of the interface residues in the mutant compared to the wild-type. Interestingly, the flexible N-terminal arm makes more stable contact with the DNA minor groove in the mutant. We found that the two interaction sites i.e. the DNA-binding helix and the unstructured N-terminal arm influence each other via the bound DNA. These results provide an interesting conundrum: alanine at position 290 enhances both the stability and the DNA-binding affinity of the protein, however, evolution prefers glycine at this position. We have provided several plausible explanations for this apparent conundrum. The function of the EXD as a HOX co-factor requires its ability to discriminate similar DNA sequences, which is most likely comprom.

Analysis of the interaction of a non-canonical twin half-site of Cyclic AMP-Response Element (CRE) with CRE-binding protein

Differential regulation of a gene having either canonical or non-canonical cyclic AMP response element (CRE) in its promoter is primarily accomplished by its interactions with CREB (cAMP-response element binding protein). The present study aims to delineate the mechanism of the CREB-CRE interactions at the Oncostatin-M (osm) promoter by in vitro and in silico approaches. The non-canonical CREosm consists of two half-CREs separated by a short intervening sequence of 9 base pairs. In this study, in vitro binding assays revealed that out of the two CRE half-sites, the right half-CRE was indispensable for binding of CREB, while the left sequence showed weaker binding ability and specificity. Genome-wide modeling and high throughput free energy calculations for the energy-minimized models containing CREB-CREosm revealed that there was no difference in the binding of CREB to the right half of CREosm site when compared to the entire CREosm. These results were in accordance with the in vitro studies, confirming the indispensable role of the right half-CREosm site in stable complex formation with the CREB protein. Additionally, conversion of the right half-CREosm site to a canonical CRE palindrome showed stronger CREB binding, irrespective of the presence or absence of the left CRE sequence. Thus, the present study establishes an interesting insight into the interaction of CREB with a CRE variant located at the far end of a TATA-less promoter of a cytokine-encoding gene, which in turn could be involved in the regulation of transcription under specific conditions.

Genome-wide identification and expression analysis of DREB family genes in cotton

BACKGROUND

Dehydration responsive element-binding (DREB) transcription factors are widely present in plants, and involve in signalling transduction, plant growth and development, and stress response. DREB genes have been characterized in multiple species. However, only a few DREB genes have been studied in cotton, one of the most important fibre crops. Herein, the genome‑wide identification, phylogeny, and expression analysis of DREB family genes are performed in diploid and tetraploid cotton species.

RESULTS

In total, 193, 183, 80, and 79 putative genes containing the AP2 domain were identified using bioinformatics approaches in G. barbadense, G. hirsutum, G. arboretum, and G. raimondii, respectively. Phylogenetic analysis showed that based on the categorization of Arabidopsis DREB genes, 535 DREB genes were divided into six subgroups (A1-A6) by using MEGA 7.0. The identified DREB genes were distributed unevenly across 13/26 chromosomes of A and/or D genomes. Synteny and collinearity analysis confirmed that during the evolution, the whole genome duplications, segmental duplications, and/or tandem duplications occurred in cotton DREB genes, and then DREB gene family was further expanded. Further, the evolutionary trees with conserved motifs, cis-acting elements, and gene structure of cotton DREB gene family were predicted, and these results suggested that DREB genes might be involved in the hormone and abiotic stresses responses. The subcellular localization showed that in four cotton species, DREB proteins were predominantly located in the nucleus. Further, the analysis of DREB gene expression was carried out by real-time quantitative PCR, confirming that the identified DREB genes of cotton were involved in response to early salinity and osmotic stress.

CONCLUSIONS

Collectively, our results presented a comprehensive and systematic understanding in the evolution of cotton DREB genes, and demonstrated the potential roles of DREB family genes in stress and hormone response.

Computational and atomistic studies applied to the understanding of the structural and behavioral features of the immune checkpoint HLA-G molecule and gene

We took advantage of the increasingly evolving approaches for in silico studies concerning protein structures, protein molecular dynamics (MD), protein-protein and protein-DNA docking to evaluate: (i) the structure and MD characteristics of the HLA-G well-recognized isoforms, (ii) the impact of missense mutations at HLA-G receptor genes (LILRB1/2), and (iii) the differential binding of the hypoxia-inducible factor 1 (HIF1) to hypoxia-responsive elements (HRE) at the HLA-G gene. Besides reviewing these topics, they were revisited including the following novel results: (i) the HLA-G6 isoforms were unstable docked or not with β₂-microglobulin or peptide, (ii) missense mutations at LILRB1/2 genes, exchanging amino acids at the intracellular domain, particularly those located within and around the ITIM motifs, may impact the HLA-G binding strength, and (iii) HREs motifs at the HLA-G promoter or exon 2 regions exhibiting a guanine at their third position present a higher affinity for HIF1 when compared to an adenine at the same position. These data shed some light into the functional aspects of HLA-G, particularly how polymorphisms may influence the role of the molecule. Computational and atomistic studies have provided alternative tools for experimental physical methodologies, which are time-consuming, expensive, demanding large quantities of purified proteins, and exhibit low output.

Arabidopsis LSH10 transcription factor and OTLD1 histone deubiquitinase interact and transcriptionally regulate the same target genes

Histone ubiquitylation/deubiquitylation plays a major role in the epigenetic regulation of gene expression. In plants, OTLD1, a member of the ovarian tumor (OTU) deubiquitinase family, deubiquitylates histone 2B and represses the expression of genes involved in growth, cell expansion, and hormone signaling. OTLD1 lacks the intrinsic ability to bind DNA. How OTLD1, as well as most other known plant histone deubiquitinases, recognizes its target genes remains unknown. Here, we show that Arabidopsis transcription factor LSH10, a member of the ALOG protein family, interacts with OTLD1 in living plant cells. Loss-of-function LSH10 mutations relieve the OTLD1-promoted transcriptional repression of the target genes, resulting in their elevated expression, whereas recovery of the LSH10 function results in down-regulated transcription of the same genes. We show that LSH10 associates with the target gene chromatin as well as with DNA sequences in the promoter regions of the target genes. Furthermore, without LSH10, the degree of H2B monoubiquitylation in the target promoter chromatin increases. Hence, our data suggest that OTLD1-LSH10 acts as a co-repressor complex potentially representing a general mechanism for the specific function of plant histone deubiquitinases at their target chromatin.

Structural basis of direct and inverted DNA sequence repeat recognition by helix-turn-helix transcription factors

Some transcription factors bind DNA motifs containing direct or inverted sequence repeats. Preference for each of these DNA topologies is dictated by structural constraints. Most prokaryotic regulators form symmetric oligomers, which require operators with a dyad structure. Binding to direct repeats requires breaking the internal symmetry, a property restricted to a few regulators, most of them from the AraC family. The KorA family of transcriptional repressors, involved in plasmid propagation and stability, includes members that form symmetric dimers and recognize inverted repeats. Our structural analyses show that ArdK, a member of this family, can form a symmetric dimer similar to that observed for KorA, yet it binds direct sequence repeats as a non-symmetric dimer. This is possible by the 180° rotation of one of the helix-turn-helix domains. We then probed and confirmed that ArdK shows affinity for an inverted repeat, which, surprisingly, is also recognized by a non-symmetrical dimer. Our results indicate that structural flexibility at different positions in the dimerization interface constrains transcription factors to bind DNA sequences with one of these two alternative DNA topologies.

ProDFace: A web-tool for the dissection of protein-DNA interfaces

Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein-DNA docking methods. Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.

Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches

Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding proteins from being attacked in case it is wrongly recognized as an anomaly. In addition to their critical roles in genome stability and integrity, it has been demonstrated that ssDNA and SSB-ssDNA interactions play critical roles in transcriptional regulation in all three domains of life and viruses. In this review, we present our current knowledge of the structure and function of SSBs and the structural features for SSB binding specificity. We then discuss the machine learning-based approaches that have been developed for the prediction of SSBs from double-stranded DNA (dsDNA) binding proteins (DSBs).

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Structural Characterization of the Interaction of Hypoxia Inducible Factor-1 with Its Hypoxia Responsive Element at the -964G > A Variation Site of the HLA-G Promoter Region

Human Antigen Leukocyte-G (HLA-G) gene encodes an immune checkpoint molecule that has restricted tissue expression in physiological conditions; however, the gene may be induced in hypoxic conditions by the interaction with the hypoxia inducible factor-1 (HIF1). Hypoxia regulatory elements (HRE) located at the HLA-G promoter region and at exon 2 are the major HIF1 target sites. Since the G allele of the −964G > A transversion induces higher HLA-G expression when compared to the A allele in hypoxic conditions, here we analyzed HIF1-HRE complex interaction at the pair-atom level considering both −964G > A polymorphism alleles. Mouse HIF2 dimer crystal (Protein Data Bank ID: 4ZPK) was used as template to perform homology modelling of human HIF1 quaternary structure using MODELLER v9.14. Two 3D DNA structures were built from 5′GCRTG’3 HRE sequence containing the −964G/A alleles using x3DNA. Protein-DNA docking was performed using the HADDOCK v2.4 server, and non-covalent bonds were computed by DNAproDB server. Molecular dynamic simulation was carried out per 200 ns, using Gromacs v.2019. HIF1 binding in the HRE containing −964G allele results in more hydrogen bonds and van der Waals contact formation than HRE with −964A allele. Protein-DNA complex trajectory analysis revealed that HIF1-HRE-964G complex is more stable. In conclusion, HIF1 binds in a more stable and specific manner at the HRE with G allele.

Computational investigation to identify potent inhibitors of the GTPase-Kirsten RAt sarcoma virus (K-Ras) mutants G12C and G12D

K-Ras mutations are frequent in various cancer types, and according to recent research, K-Ras possesses four-drug targeting sites. This increased our interest in finding potential small molecule inhibitors with anticancer activity to treat K-Ras-driven cancers. We utilized integrated bioinformatic strategies, such as XP docking, MM-GBSA, cell-line cytotoxicity prediction, ADMET, and molecular simulation, to discover potential inhibitors of G12C and G12D mutants compared to sotorasib, which is a recent FDA-approved inhibitor of G12C. We identified compounds, such as flupentixol, amlodipine, and fluvoxamine, for the G12C mutant and paroxetine, flupentixol, and zuclopenthixol for the G12D mutant with significant inhibitory functions. All five compounds bound to the H95 cryptic groove of mutant K-Ras with high efficiency and, like sotorasib, retained a novel binding mechanism with additional hydrophobic interactions at the molecular level. Furthermore, the simulation studies suggested that the binding of flupentixol and amlodipine to G12C stabilizes switch I and switch II. In contrast, paroxetine and flupentixol to G12D showed a similar trend compared to sotorasib complexes. Thus, despite the very dynamic functionality of K-Ras switches I and II, the binding of shortlisted compounds is highly stable. Therefore, the reported study provides potential drug candidates for K-Ras inhibition that can be further developed with in vitro and in vivo evidence for targeted therapy.

Drugging the 'undruggable'. Therapeutic targeting of protein-DNA interactions with the use of computer-aided drug discovery methods

Transcription factors (TFs) act as major oncodrivers in many cancers and are frequently regarded as high-value therapeutic targets. The functionality of TFs relies on direct protein-DNA interactions, which are notoriously difficult to target with small molecules. However, this prior view of the 'undruggability' of protein-DNA interfaces has shifted substantially in recent years, in part because of significant advances in computer-aided drug discovery (CADD). In this review, we highlight recent examples of successful CADD campaigns resulting in drug candidates that directly interfere with protein-DNA interactions of several key cancer TFs, including androgen receptor (AR), ETS-related gene (ERG), MYC, thymocyte selection-associated high mobility group box protein (TOX), topoisomerase II (TOP2), and signal transducer and activator of transcription 3 (STAT3). Importantly, these findings open novel and compelling avenues for therapeutic targeting of over 1600 human TFs implicated in many conditions including and beyond cancer.

Comparative Analysis of the IclR-Family of Bacterial Transcription Factors and Their DNA-Binding Motifs: Structure, Positioning, Co-Evolution, Regulon Content

The IclR-family is a large group of transcription factors (TFs) regulating various biological processes in diverse bacteria. Using comparative genomics techniques, we have identified binding motifs of IclR-family TFs, reconstructed regulons and analyzed their content, finding co-occurrences between the regulated COGs (clusters of orthologous genes), useful for future functional characterizations of TFs and their regulated genes. We describe two main types of IclR-family motifs, similar in sequence but different in the arrangement of the half-sites (boxes), with GKTYCRYW_3-4RYGRAMC and TGRAACAN_1-2TGTTYCA consensuses, and also predict that TFs in 32 orthologous groups have binding sites comprised of three boxes with alternating direction, which implies two possible alternative modes of dimerization of TFs. We identified trends in site positioning relative to the translational gene start, and show that TFs in 94 orthologous groups bind tandem sites with 18-22 nucleotides between their centers. We predict protein-DNA contacts via the correlation analysis of nucleotides in binding sites and amino acids of the DNA-binding domain of TFs, and show that the majority of interacting positions and predicted contacts are similar for both types of motifs and conform well both to available experimental data and to general protein-DNA interaction trends.

Aptamer BC 007's Affinity to Specific and Less-Specific Anti-SARS-CoV-2 Neutralizing Antibodies

COVID-19 is a pandemic respiratory disease that is caused by the highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Anti-SARS-CoV-2 antibodies are essential weapons that a patient with COVID-19 has to combat the disease. When now repurposing a drug, namely an aptamer that interacts with SARS-CoV-2 proteins for COVID-19 treatment (BC 007), which is, however, a neutralizer of pathogenic autoantibodies in its original indication, the possibility of also binding and neutralizing anti-SARS-CoV-2 antibodies must be considered. Here, the highly specific virus-neutralizing antibodies have to be distinguished from the ones that also show cross-reactivity to tissues. The last-mentioned could be the origin of the widely reported SARS-CoV-2-induced autoimmunity, which should also become a target of therapy. We, therefore, used enzyme-linked immunosorbent assay (ELISA) technology to assess the binding of well-characterized publicly accessible anti-SARS-CoV-2 antibodies (CV07-209 and CV07-270) with BC 007. Nuclear magnetic resonance spectroscopy, isothermal calorimetric titration, and circular dichroism spectroscopy were additionally used to test the binding of BC 007 to DNA-binding sequence segments of these antibodies. BC 007 did not bind to the highly specific neutralizing anti-SARS-CoV-2 antibody but did bind to the less specific one. This, however, was a lot less compared to an autoantibody of its original indication (14.2%, range 11.0–21.5%). It was also interesting to see that the less-specific anti-SARS-CoV-2 antibody also showed a high background signal in the ELISA (binding on NeutrAvidin-coated or activated but noncoated plastic plate). These initial experiments suggest that the risk of binding and neutralizing highly specific anti-SARS CoV-2 antibodies by BC 007 should be low.

Bacterial Growth Inhibition Screen (BGIS) identifies a loss-of-function mutant of the DEK oncogene, indicating DNA modulating activities of DEK in chromatin

The DEK oncoprotein regulates cellular chromatin function via a number of protein-protein interactions. However, the biological relevance of its unique pseudo-SAP/SAP-box domain, which transmits DNA modulating activities in vitro, remains largely speculative. As hypothesis-driven mutations failed to yield DNA-binding null (DBN) mutants, we combined random mutagenesis with the Bacterial Growth Inhibition Screen (BGIS) to overcome this bottleneck. Re-expression of a DEK-DBN mutant in newly established human DEK knockout cells failed to reduce the increase in nuclear size as compared to wild type, indicating roles for DEK-DNA interactions in cellular chromatin organization. Our results extend the functional roles of DEK in metazoan chromatin and highlight the predictive ability of recombinant protein toxicity in E. coli for unbiased studies of eukaryotic DNA modulating protein domains.

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.

How human serum albumin-selective DNA aptamer binds to bovine and canine serum albumins

Serum albumin (SA) is the most abundant carrier protein in blood. SA carries a diverse range of nutrients, drugs, and metal ions. It has wide clinical and biochemical applications. Human serum albumin (HSA) can be used as a biomarker for kidney and liver diseases. Aptasensor is one of potential HSA detection methods. HSA-specific aptamer was selected for HSA detection. In animals, bovine serum albumin (BSA) and canine serum albumins (CSA) share high sequence similarities to HSA. Thus, it is interesting to explore the possibility of using HSA-selective aptamer for BSA and CSA aptasensor. In this study, molecular dynamics (MD) simulations were initially employed to investigate the binding of aptamer to BSA and CSA in comparison to HSA. Like HSA, both BSA and CSA can bind aptamer, but different binding affinities are observed. BSA shows the tighter binding to aptamer than CSA. Domain III is found to be the aptamer-binding domain although no specific aptamer conformation is captured. However, in all cases, the aptamer utilizes the 3'-end to attach on an albumin surface. Both nucleobases and phosphate backbones on a DNA aptamer are important for albumin-aptamer complexation. Our results imply the possibility of using HSA-specific aptamer for BSA detection due to tighter binding observed, but may be less effective in CSA. However, the test in actual complicated condition must be further studied.

Mutational landscape of K-Ras substitutions at 12th position-a systematic molecular dynamics approach

K-Ras is a small GTPase and acts as a molecular switch by recruiting GEFs and GAPs, and alternates between the inert GDP-bound and the dynamic GTP-bound forms. The amino acid at position 12 of K-Ras is a hot spot for oncogenic mutations (G12A, G12C, G12D, G12R, G12S, and G12V), disturbing the active fold of the protein, leading to cancer development. This study aimed to investigate the potential conformational changes induced by these oncogenic mutations at the 12^th position, impairing GAP-mediated GTP hydrolysis. Comprehensive computational tools (iStable, FoldX, SNPeffect, DynaMut, and CUPSAT) were used to evaluate the effect of these six mutations on the stability of wild type K-Ras protein. The docking of GTP with K-Ras was carried out using AutoDock4.2, followed by molecular dynamics simulations. Furthermore, on comparison of binding energies between the wild type K-Ras and the six mutants, we have demonstrated that the G12A and G12V mutants exhibited the strongest binding efficiency compared to the other four mutants. Trajectory analyses of these mutations revealed that G12A encountered the least deviation, fluctuation, intermolecular H-bonds, and compactness compared to the wildtype, which was supported by the lower Gibbs free energy value. Our study investigates the molecular dynamics simulations of the mutant K-Ras forms at the 12^th position, which expects to provide insights about the molecular mechanisms involved in cancer development, and may serve as a platform for targeted therapies against cancer. Communicated by Ramaswamy H. Sarma.

Genome-Wide Analysis of the Role of NAC Family in Flower Development and Abiotic Stress Responses in Cleistogenes songorica

Plant-specific NAC (NAM, ATAF, CUC) transcription factor (TF) family plays important roles in biological processes such as plant growth and response to stress. Nevertheless, no information is known about NAC TFs in Cleistogenes songorica, a prominent xerophyte desert grass in northwestern China. In this study, 162 NAC genes were found from the Cleistogenes songorica genome, among which 156 C. songoricaNAC (CsNAC) genes (96.3%) were mapped onto 20 chromosomes. The phylogenetic tree constructed by CsNAC and rice NAC TFs can be separated into 14 subfamilies. Syntenic and Ka/Ks analyses showed that CsNACs were primarily expanded by genomewide replication events, and purifying selection was the primary force driving the evolution of CsNAC family genes. The CsNAC gene expression profiles showed that 36 CsNAC genes showed differential expression between cleistogamous (CL) and chasmogamous (CH) flowers. One hundred and two CsNAC genes showed differential expression under heat, cold, drought, salt and ABA treatment. Twenty-three CsNAC genes were commonly differentially expressed both under stress responses and during dimorphic floret development. Gene Ontology (GO) annotation, coexpression network and qRT-PCR tests revealed that these CsNAC genes may simultaneously regulate dimorphic floret development and the response to stress. Our results may help to characterize the NAC transcription factors in C. songorica and provide new insights into the functional research and application of the NAC family in crop improvement, especially in dimorphic floret plants.

Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps

While protein-DNA interactions are crucial for a wide range of cellular functions, only a small fraction of these interactions was annotated to date. One solution to close this annotation gap is to employ computational methods that accurately predict protein-DNA interactions from widely available protein sequences. We present and empirically test first-of-its-kind predictor of DNA-binding residues in local segments of protein sequences that relies on the Fuzzy Cognitive Map (FCM) model. The FCM model uses information about putative solvent accessibility, evolutionary conservation, and relative propensities of amino acid to interact with DNA to generate putative DNA-binding residues. Empirical tests on a benchmark dataset reveal that the FCM model secures AUC = 0.72 and outperforms recently released hybridNAP predictor and several popular machine learning methods including Support Vector Machines, Naïve Bayes, and k-Nearest Neighbor. The improvements in the predictive performance result from an intrinsic feature of FCMs that incorporate relations between the input features, besides the relations between the inputs and output that are modelled by other algorithms. We also empirically demonstrate that use of a short sliding window results in further improvements in the predictive quality. The funDNApred webserver that implements the FCM predictor is available at http://biomine.cs.vcu.edu/servers/funDNApred/.

Mutagenic Analysis of a DNA Translocating Tube's Interior Surface

Bacteriophage ϕX174 uses a decamer of DNA piloting proteins to penetrate its host. These proteins oligomerize into a cell wall-spanning tube, wide enough for genome passage. While the inner surface of the tube is primarily lined with inward-facing amino acid side chains containing amide and guanidinium groups, there is a 28 Å-long section near the tube’s C-terminus that does not exhibit this motif. The majority of the inward-facing residues in this region are conserved across the three ϕX174-like clades, suggesting that they play an important role during genome delivery. To test this hypothesis, and explore the general function of the tube’s inner surface, non-glutamine residues within this region were mutated to glutamine, while existing glutamine residues were changed to serine. Four of the resulting mutants had temperature-dependent phenotypes. Virion assembly, host attachment, and virion eclipse, defined as the cell’s ability to inactivate the virus, were not affected. Genome delivery, however, was inhibited. The results support a model in which a balance of forces governs genome delivery: potential energy provided by the densely packaged viral genome and/or an osmotic gradient move the genome into the cell, while the tube’s inward facing glutamine residues exert a frictional force, or drag, that controls genome release.

New insights into protein-DNA binding specificity from hydrogen bond based comparative study

Knowledge of protein-DNA binding specificity has important implications in understanding DNA metabolism, transcriptional regulation and developing therapeutic drugs. Previous studies demonstrated hydrogen bonds between amino acid side chains and DNA bases play major roles in specific protein-DNA interactions. In this paper, we investigated the roles of individual DNA strands and protein secondary structure types in specific protein-DNA recognition based on side chain-base hydrogen bonds. By comparing the contribution of each DNA strand to the overall binding specificity between DNA-binding proteins with different degrees of binding specificity, we found that highly specific DNA-binding proteins show balanced hydrogen bonding with each of the two DNA strands while multi-specific DNA binding proteins are generally biased towards one strand. Protein-base pair hydrogen bonds, in which both bases of a base pair are involved in forming hydrogen bonds with amino acid side chains, are more prevalent in the highly specific protein-DNA complexes than those in the multi-specific group. Amino acids involved in side chain-base hydrogen bonds favor strand and coil secondary structure types in highly specific DNA-binding proteins while multi-specific DNA-binding proteins prefer helices.

Comprehensive in silico screening and molecular dynamics studies of missense mutations in Sjogren-Larsson syndrome associated with the ALDH3A2 gene

Sjögren-Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.

Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

Interactions between proteins and nucleic acids are at the heart of many essential biological processes. Despite increasing structural information about how these interactions may take place, our understanding of the usage made of protein surfaces by nucleic acids is still very limited. This is in part due to the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures and compared it to several machine-learning state-of-the-art methods. Our approach achieves a higher sensitivity compared to the other methods, with a similar precision. Importantly, we show that it is able to unravel ‘hidden’ binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is also applicable to the detection of RNA-binding sites, without significant loss of performance. This confirms that DNA and RNA-binding sites share similar properties. Our method is implemented as a fully automated tool, JETDNA2, freely accessible at: http://www.lcqb.upmc.fr/JET2DNA. We also provide a new dataset of 187 protein-DNA complex structures, along with a subset of 82 associated unbound structures. The set represents the largest body of high-resolution crystallographic structures of protein-DNA complexes, use biological protein assemblies as DNA-binding units, and covers all major types of protein-DNA interactions. It is available at: http://www.lcqb.upmc.fr/PDNAbenchmarks.

Protein-DNA interactions are essential to living organisms and their impairment is associated to many diseases. For these reasons, they have become increasingly important therapeutic targets. Experimental structure determination has revealed different binding motifs and modes, associated to different functions. Yet, the available structural data gives us only a glimpse of the multiplicity and complexity of protein surface usage by DNA. In this work, we use a three-layer model to describe and predict DNA-binding sites at protein surfaces. Given a protein, we consider the way its residues are conserved through evolution, their physico-chemical properties and geometrical shapes to decrypt its surface. We are able to detect a large portion of interacting residues with good precision, even when they are ‘hidden’ by conformational changes. We highlight cases where one protein binds DNA via distinct regions to perform different functions. We are able to uncover the alternative binding sites and relate their properties with their specific roles. Our work can help guiding mutagenesis experiments and the development of new drugs specifically targeting one site while limiting possible side effects.

Extreme divergence between one-to-one orthologs: the structure of N15 Cro bound to operator DNA and its relationship to the λ Cro complex

The gene cro promotes lytic growth of phages through binding of Cro protein dimers to regulatory DNA sites. Most Cro proteins are one-to-one orthologs, yet their sequence, structure and binding site sequences are quite divergent across lambdoid phages. We report the cocrystal structure of bacteriophage N15 Cro with a symmetric consensus site. We contrast this complex with an orthologous structure from phage λ, which has a dissimilar binding site sequence and a Cro protein that is highly divergent in sequence, dimerization interface and protein fold. The N15 Cro complex has less DNA bending and smaller DNA-induced changes in protein structure. N15 Cro makes fewer direct contacts and hydrogen bonds to bases, relying mostly on water-mediated and Van der Waals contacts to recognize the sequence. The recognition helices of N15 Cro and λ Cro make mostly nonhomologous and nonanalogous contacts. Interface alignment scores show that half-site binding geometries of N15 Cro and λ Cro are less similar to each other than to distantly related CI repressors. Despite this divergence, the Cro family shows several code-like protein–DNA sequence covariations. In some cases, orthologous genes can achieve a similar biological function using very different specific molecular interactions.

Recent development of nucleic acid nanosensors to detect sequence-specific binding interactions: From metal ions, small molecules to proteins and pathogens

DNA carries important genetic instructions and plays vital roles in regulating biological activities in living cells. Proteins such as transcription factors binds to DNA to regulate the biological functions of DNA, and similarly many drug molecules also bind to DNA to modulate its functions. Due to the importance of protein-DNA and drug-DNA binding, there has been intense effort in developing novel nanosensors in the same length scale as DNA, to effectively study these binding interactions in details. In addition, aptamers can be artificially selected to detect metal ions and pathogens such as bacteria and viruses, making nucleic acid nanosensors more versatile in detecting a large variety of analytes. In this minireview, we first explained the different types and binding modes of protein-DNA and drug-DNA interactions in the biological systems, as well as aptamer-target binding. This was followed by the review of five types of nucleic acid nanosensors based on optical or electrochemical detection. The five types of nucleic acid nanosensors utilizing colorimetric, dynamic light scattering (DLS), surface-enhanced Raman spectroscopy (SERS), fluorescence and electrochemical detections have been recently developed to tackle some of the challenges in high-throughput screening technology for large scale analysis, which is especially useful for drug development and mass screening for pandemic outbreak such as SARS or COVID-19.

EL_LSTM: Prediction of DNA-Binding Residue from Protein Sequence by Combining Long Short-Term Memory and Ensemble Learning

Most past works for DNA-binding residue prediction did not consider the relationships between residues. In this paper, we propose a novel approach for DNA-binding residue prediction, referred to as EL_LSTM, which includes two main components. The first component is the Long Short-Term Memory (LSTM), which learns pairwise relationships between residues through a bi-gram model and then learns feature vectors for all residues. The second component is an ensemble learning based classifier introduced to tackle the data imbalance problem in binding residue predictions. We use a variant of the bagging strategy in ensemble learning to achieve balanced samples. Evaluations on PDNA-224 and DBP-123 show that adding feature relationships performs better than classifiers without feature relationships by at least 0.028 on MCC, 1.18 percent on ST and 0.012 on AUC. This indicates the usefulness of feature relationships for DNA-binding residue predictions. Evaluation on using ensemble learning indicates that the improvement can reach at least 0.021 on MCC, 1.32 percent on ST, and 0.018 on AUC compared to the use of a single LSTM classifier. Comparisons with the state-of-the-art predictors show that our proposed EL_LSTM outperforms them significantly. Further feature analysis validates the effectiveness of LSTM for the prediction of DNA-binding residues.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.

African swine fever virus replication events and cell nucleus: New insights and perspectives

African swine fever (ASF) is currently matter for major concerns in global swine industry as it is highly contagious and causes acute fatal haemorrhagic fever in domestic pigs and wild boar. The absence of effective vaccines and treatments pushes ASF control to relay on strict sanitary and stamping out measures with costly socio-economic impacts. The current epidemic scenario of fast spreading throughout Asiatic countries impels further studies on prevention and combat strategies against ASF. Herein we review knowledge on African Swine Fever Virus (ASFV) interactions with the host cell nucleus and on the functional properties of different viral DNA-replication related proteins. This entails, the confirmation of an intranuclear viral DNA replication phase, the characterization of cellular DNA damage responses (DDR), the subnuclear compartments disruption due to viral modulation, and the unravelling of the biological role of several viral proteins (A104R, I215 L, P1192R, QP509 L and Q706 L), so to contribute to underpin rational strategies for vaccine candidates development.

Genome-wide analyses and expression patterns under abiotic stress of NAC transcription factors in white pear (Pyrus bretschneideri)

BACKGROUND

Although the genome of Chinese white pear ('Dangshansuli') has been released, little is known about the functions, evolutionary history and expression patterns of NAC families in this species to date.

RESULTS

In this study, we identified a total of 183 NAC transcription factors (TFs) in the pear genome, among which 146 pear NAC (PbNAC) members were mapped onto 16 chromosomes, and 37 PbNAC genes were located on scaffold contigs. No PbNAC genes were mapped to chromosome 2. Based on gene structure, protein motif analysis, and topology of the phylogenetic tree, the pear PbNAC family was classified into 33 groups. By comparing and analyzing the unique NAC subgroups in Rosaceae, we identified 19 NAC subgroups specific to pear. We also found that whole-genome duplication (WGD)/segmental duplication played critical roles in the expansion of the NAC family in pear, such as the 83 PbNAC duplicated gene pairs dated back to the two WGD events. Further, we found that purifying selection was the primary force driving the evolution of PbNAC family genes. Next, we used transcriptomic data to study responses to drought and cold stresses in pear, and we found that genes in groups C2f, C72b, and C100a were related to drought and cold stress response.

CONCLUSIONS

Through the phylogenetic, evolutionary, and expression analyses of the NAC gene family in Chinese white pear, we indentified 11 PbNAC TFs associated with abiotic stress in pear.

Structural study to analyze the DNA-binding properties of DsrC protein from the dsr operon of sulfur-oxidizing bacterium Allochromatium vinosum

Our environment is densely populated with various beneficial sulfur-oxidizing prokaryotes (SOPs). These organisms are responsible for the proper maintenance of biogeochemical sulfur cycles to regulate the turnover of biological sulfur substrates in the environment. Allochromatium vinosum strain DSM 180^T is a gamma-proteobacterium and is a member of SOP. The organism codes for the sulfur-oxidizing dsr operon, which is comprised of dsrABEFHCMKLJOPNRS genes. The Dsr proteins formed from dsr operon are responsible for formation of sulfur globules. However, the molecular mechanism of the regulation of the dsr operon is not yet fully established. Among the proteins encoded by dsr genes, DsrC is known to have some regulatory functions. DsrC possesses a helix-turn-helix (HTH) DNA-binding motif. Interestingly, the structural details of this interaction have not yet been fully established. Therefore, we tried to analyze the binding interactions of the DsrC protein with the promoter DNA structure of the dsr operon as well as a random DNA as the control. We also performed molecular dynamics simulations of the DsrC-DNA complexes. This structure-function relationship investigation revealed the most probable binding interactions of the DsrC protein with the promoter region present upstream of the dsrA gene in the dsr operon. As expected, the random DNA structure could not properly interact with DsrC. Our analysis will therefore help researchers to predict a plausible biochemical mechanism for the sulfur oxidation process. Graphical Abstract Interaction of Allochromatium vinosum DsrC protein with the promoter region present upstream of the dsrA gene.

Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver

Motivation

Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments.

Results

Here we report a new method Single Amino acid Mutation binding free energy change of Protein-DNA Interaction (SAMPDI). The method utilizes modified Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge-based terms delivered from investigations of the physicochemical properties of protein-DNA complexes. The method is benchmarked against experimentally determined binding free energy changes caused by 105 mutations in 13 proteins (compiled ProNIT database and data from recent references), and results in correlation coefficient of 0.72.

Availability and implementation

http://compbio.clemson.edu/SAMPDI.

Contact

ealexov@clemson.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

DNA Motif Recognition Modeling from Protein Sequences

Although the existing works on DNA motif discovery on DNA sequences are plethoric, mechanistic knowledge to infer DNA motifs from protein sequences across multiple DNA-binding domain families without conducting any wet-lab experiments is still lacking. Therefore, the k-spectrum recognition modeling is proposed to address the issues at the highest possible resolutions. The k-spectrum model can capture DNA motif patterns from protein sequences at the resolution in which local sequence context and nucleotide dependency can be taken into account completely. Multiple evaluation metrics are adopted and measured on millions of k-mer binding intensities from 92 proteins across 5 DNA-binding families (i.e., bHLH, bZIP, ETS, Forkhead, and Homeodomain), demonstrating its competitive edges. In addition, it not only can contribute to DNA motif recognition modeling but also can help prioritize the observed or even unobserved binding of single nucleotide variants on transcription factor binding sites in a genome-wide manner.

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DNA motif modeling from protein is fundamental for understanding gene regulation

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A framework is proposed at the highest possible sequence resolution for the first time

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It is validated on millions of k-mer intensities from 92 proteins across 5 families

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It can prioritize the unobserved regulatory single nucleotide variants on DNA motifs

Evolutionary potential of transcription factors for gene regulatory rewiring

Gene regulatory networks evolve through rewiring of individual components-that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor-DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor-DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution.

Design and implementation of structural bioinformatics projects for biological sciences undergraduate students

Contemporary biology is currently undergoing a revolution, driven by the availability of high-throughput technologies and a wide variety of bioinformatics tools. However, bioinformatics education and practice is still in its infancy in most of the African continent. Consequently, concerted efforts have been made in recent years to incorporate bioinformatics modules into biological sciences curriculum of African Universities. Despite this, one aspect of bioinformatics that is yet to be incorporated is structural bioinformatics. In this article, we report on a structural bioinformatics project carried out by final year project students in a Nigerian university. The target protein was the thermoacidophilic Sulfolobus islandicus rod-shaped virus 1 (SIRV1) Rep protein, which was further characterized using various free, user-friendly and online sequence-based and structure-based bioinformatics tools. This exercise gave students the opportunity to generate new data, interpret the data, and acquire collaborative research skills. In this report, emphasis is placed on analysis of the data generated to further encourage analytical skills. By sharing this experience, it is anticipated that other similar institutions would adopt parallel strategies to expose undergraduate students to structural biology, and increase awareness of freely available bioinformatics tools for tackling pertinent biological questions. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):547-554, 2018.

A novel method for improved accuracy of transcription factor binding site prediction

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

Crystal structure of NucB, a biofilm-degrading endonuclease

Bacterial biofilms are a complex architecture of cells that grow on moist interfaces, and are held together by a molecular glue of extracellular proteins, sugars and nucleic acids. Biofilms are particularly problematic in human healthcare as they can coat medical implants and are thus a potential source of disease. The enzymatic dispersal of biofilms is increasingly being developed as a new strategy to treat this problem. Here, we have characterized NucB, a biofilm-dispersing nuclease from a marine strain of Bacillus licheniformis, and present its crystal structure together with the biochemistry and a mutational analysis required to confirm its active site. Taken together, these data support the categorization of NucB into a unique subfamily of the ββα metal-dependent non-specific endonucleases. Understanding the structure and function of NucB will facilitate its future development into an anti-biofilm therapeutic agent.

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains

Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.

Identification of a suitable promoter for the sigma factor of Mycobacterium tuberculosis

Promoter binding specificity is one of the important characteristics of transcription by Mycobacterium tuberculosis (Mtb) sigma (σ) factors, which remains unexplored due to limited structural evidence. Our previous study on the structural features of Mtb-SigH, consisting of three alpha helices, and its interaction with core RNA polymerase has been extended herein to determine the little known DNA sequence recognition pattern involving its cognate promoters. Herein, high resolution X-ray crystallographic structures of the protein-DNA complexes were inspected to determine the tentative DNA-binding helix of the σ factor. The binding interface in the available crystal structures is found to be populated mainly with specific residues such as Arg, Asn, Lys, Gln, and Ser. We uncovered the helix 3 of Mtb-SigH containing most of these amino acids, which ranged from Arg 64 to Arg 75, forming the predicted active site. The complex of Mtb-SigH:DNA is modelled with 20 promoter sequences. The binding affinity is predicted by scoring these protein-DNA complexes through proximity and interaction parameters obtained by molecular dynamics simulations. The promoters are ranked considering hydrogen bonding, energy of interaction, buried surface area, and distance between centers of masses in interaction with the protein. The ranking is validated through in vitro transcription assays. The trends of these selected promoter interactions have shown variations parallel to the experimental evaluation, emphasizing the success of the active site determination along with screening of the promoter strength. The promoter interaction of Mtb-SigH can be highly beneficial for understanding the regulation of gene expression of a pathogen and also extends a solid platform to predict promoters for other bacterial σ factors.

Evaluation of Sox2 binding affinities for distinct DNA patterns using steered molecular dynamics simulation

Transcription factors (TFs) are gene expression regulators that bind to DNA in a sequence‐specific manner and determine the functional characteristics of the gene. It is worthwhile to study the unique characteristics of such specific TF‐binding pattern in DNA. Sox2 recognizes a 6‐ to 7‐base pair consensus DNA sequence; the central four bases of the binding site are highly conserved, whereas the two to three flanking bases are variable. Here, we attempted to analyze the binding affinity and specificity of the Sox2 protein for distinct DNA sequence patterns via steered molecular dynamics, in which a pulling force is employed to dissociate Sox2 from Sox2–DNA during simulation to study the behavior of a complex under nonequilibrium conditions. The simulation results revealed that the first two stacking bases of the binding pattern have an exclusive impact on the binding affinity, with the corresponding mutant complexes showing greater binding and longer dissociation time than the experimental complexes do. In contrast, mutation of the conserved bases tends to reduce the affinity, and mutation of the complete conserved region disrupts the binding. It might pave the way to identify the most likely binding pattern recognized by Sox2 based on the affinity of each configuration. The α2‐helix of Sox2 was found to be the key player in the Sox2–DNA association. The characterization of Sox2's binding patterns for the target genes in the genome helps in understanding of its regulatory functions.

EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation

Background

Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues.

Results

In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02–0.07 for MCC, 4.18–21.47% for ST and 0.013–0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues.

Conclusions

We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT (http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/) is provided for free access to the biological research community.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1792-8) contains supplementary material, which is available to authorized users.

DNA-Binding Properties of African Swine Fever Virus pA104R, a Histone-Like Protein Involved in Viral Replication and Transcription

African swine fever virus (ASFV) codes for a putative histone-like protein (pA104R) with extensive sequence homology to bacterial proteins that are implicated in genome replication and packaging. Functional characterization of purified recombinant pA104R revealed that it binds to single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA) over a wide range of temperatures, pH values, and salt concentrations and in an ATP-independent manner, with an estimated binding site size of about 14 to 16 nucleotides. Using site-directed mutagenesis, the arginine located in pA104R's DNA-binding domain, at position 69, was found to be relevant for efficient DNA-binding activity. Together, pA104R and ASFV topoisomerase II (pP1192R) display DNA-supercoiling activity, although none of the proteins by themselves do, indicating that the two cooperate in this process. In ASFV-infected cells, A104R transcripts were detected from 2 h postinfection (hpi) onward, reaching a maximum concentration around 16 hpi. pA104R was detected from 12 hpi onward, localizing with viral DNA replication sites and being found exclusively in the Triton-insoluble fraction. Small interfering RNA (siRNA) knockdown experiments revealed that pA104R plays a critical role in viral DNA replication and gene expression, with transfected cells showing lower viral progeny numbers (up to a reduction of 82.0%), lower copy numbers of viral genomes (-78.3%), and reduced transcription of a late viral gene (-47.6%). Taken together, our results strongly suggest that pA104R participates in the modulation of viral DNA topology, probably being involved in viral DNA replication, transcription, and packaging, emphasizing that ASFV mutants lacking the A104R gene could be used as a strategy to develop a vaccine against ASFV.IMPORTANCE Recently reintroduced in Europe, African swine fever virus (ASFV) causes a fatal disease in domestic pigs, causing high economic losses in affected countries, as no vaccine or treatment is currently available. Remarkably, ASFV is the only known mammalian virus that putatively codes for a histone-like protein (pA104R) that shares extensive sequence homology with bacterial histone-like proteins. In this study, we characterized the DNA-binding properties of pA104R, analyzed the functional importance of two conserved residues, and showed that pA104R and ASFV topoisomerase II cooperate and display DNA-supercoiling activity. Moreover, pA104R is expressed during the late phase of infection and accumulates in viral DNA replication sites, and its downregulation revealed that pA104R is required for viral DNA replication and transcription. These results suggest that pA104R participates in the modulation of viral DNA topology and genome packaging, indicating that A104R deletion mutants may be a good strategy for vaccine development against ASFV.

TK, C. GPD, R. S, Zayed H. Determining the role of missense mutations in the POU domain of HNF1A that reduce the DNA-binding affinity: A computational approach

Maturity-onset diabetes of the young type 3 (MODY3) is a non-ketotic form of diabetes associated with poor insulin secretion. Over the past years, several studies have reported the association of missense mutations in the Hepatocyte Nuclear Factor 1 Alpha (HNF1A) with MODY3. Missense mutations in the POU homeodomain (POUH) of HNF1A hinder binding to the DNA, thereby leading to a dysfunctional protein. Missense mutations of the HNF1A were retrieved from public databases and subjected to a three-step computational mutational analysis to identify the underlying mechanism. First, the pathogenicity and stability of the mutations were analyzed to determine whether they alter protein structure and function. Second, the sequence conservation and DNA-binding sites of the mutant positions were assessed; as HNF1A protein is a transcription factor. Finally, the biochemical properties of the biological system were validated using molecular dynamic simulations in Gromacs 4.6.3 package. Two arginine residues (131 and 203) in the HNF1A protein are highly conserved residues and contribute to the function of the protein. Furthermore, the R131W, R131Q, and R203C mutations were predicted to be highly deleterious by in silico tools and showed lower binding affinity with DNA when compared to the native protein using the molecular docking analysis. Triplicate runs of molecular dynamic (MD) simulations (50ns) revealed smaller changes in patterns of deviation, fluctuation, and compactness, in complexes containing the R131Q and R131W mutations, compared to complexes containing the R203C mutant complex. We observed reduction in the number of intermolecular hydrogen bonds, compactness, and electrostatic potential, as well as the loss of salt bridges, in the R203C mutant complex. Substitution of arginine with cysteine at position 203 decreases the affinity of the protein for DNA, thereby destabilizing the protein. Based on our current findings, the MD approach is an important tool for elucidating the impact and affinity of mutations in DNA-protein interactions and understanding their function.

Discovering Protein-DNA Binding Cores by Aligned Pattern Clustering

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and gene regulation. Limited by expensive experiments, it is promising to discover them with variations directly from sequence data. Although existing computational methods have produced satisfactory results, they are one-to-one mappings with no site-specific information on residue/nucleotide variations, where these variations in binding cores may impact binding specificity. This study presents a new representation for modeling binding cores by incorporating variations and an algorithm to discover them from only sequence data. Our algorithm takes protein and DNA sequences from TRANSFAC (a Protein-DNA Binding Database) as input; discovers from both sets of sequences conserved regions in Aligned Pattern Clusters (APCs); associates them as Protein-DNA Co-Occurring APCs; ranks the Protein-DNA Co-Occurring APCs according to their co-occurrence, and among the top ones, finds three-dimensional structures to support each binding core candidate. If successful, candidates are verified as binding cores. Otherwise, homology modeling is applied to their close matches in PDB to attain new chemically feasible binding cores. Our algorithm obtains binding cores with higher precision and much faster runtime ( ≥ 1,600x) than that of its contemporaries, discovering candidates that do not co-occur as one-to-one associated patterns in the raw data.

AVAILABILITY

http://www.pami.uwaterloo.ca/~ealee/files/tcbbPnDna2015/Release.zip.