Structural Features Of Proteins Research Articles

Genome-wide identification of the bHLH transcription factor family and the regulatory roles of PbbHLH74 in response to drought stress in Phoebe bournei.

Phoebe species constitute a large portion of subtropical forestry, which are key players in biomass resources. However, abiotic stresses such as drought stress severely limit the growth and development of P. bournei, and even lead to its death. It has been shown that basic helix-loop-helix (bHLH) as the second largest transcription factor family plays essential roles in response to multiple stresses in plants. However, little information of bHLH family is available in P. bournei. In this study, 130 PbbHLHs were identified and classified into 24 subfamilies. Then, the bHLH domain, conserved motifs and gene structures, evolutionary patterns and protein structural features were probed. The expression levels of 17 PbbHLHs were differentially induced by PEG and ABA by RT-qPCR analysis, indicating that they may be involved in drought stress response. Characterization of the drought candidate gene PbbHLH74 showed that it was transcriptionally active and localized in the nucleus. Heterologous transformation of PbbHLH74 into yeast improved cellular tolerance to drought stress. Meanwhile, overexpression of PbbHLH74 in Arabidopsis showed higher seed germination, plant biomass and expression levels of stress-related genes under drought conditions. Through the hairy root technique, overexpression of PbbHLH74 in P. bournei improved drought tolerance by enhancing root development and expression levels of genes involved in ABA-dependent and ROS scavenging pathways. Moreover, PbbHLH74 might positively regulate the expression of PbPOD by Y1H and dual-luciferase reporter assays. Overall, these results elucidated the structure and evolution of the PbbHLH family, in which PbbHLH74 could be applied to molecular assisted breeding for drought tolerance in P. bournei.

Functional characteristics and molecular structural modification of plant proteins. Review

Protein preparations from plant raw materials are widely used in the food industry as improvers, replacers or enrichers for products. However, their functional properties, as a rule, are less pronounced than those of proteins of animal origin. The aim of the review is to analyze and summarize the results of investigations dedicated to studying and characterizing the main functional properties of plant proteins (hydration, solubility, water binding capacity, fat binding capacity and gel-forming capacity, stability of emulsions, foams, rheological properties, texturization) and their modification. The objects of the research were scientific publications, most of which were published in 2017–2024. Functional properties of proteins were characterized; their dependence on the nature and variety of crop, methods of extraction, technological factors of processing and methods of modification was revealed. The search and selection of papers were carried out in the bibliographic databases eLIBRARY.RU, RSCI, Google Scholar, Scopus, Web of Science, Elsevier, and PubMed. Data analysis was performed with their systematization, generalization, intermediate conclusions and general conclusion. The special attention was paid to chemical, physical, physico-chemical and enzymatic methods for modification of protein properties, their advantages and disadvantages, as well as the interrelation of structural and physico-chemical features of proteins with their functional properties. The main regularities were revealed for an effect of modification methods on the secondary and tertiary structures of proteins, surface hydrophobicity, the ratio from fractions, aggregation, denaturation and biological activity obtained by the modern methods of investigations (IR‑spectroscopy, fluorescent microscopy, SDS-PAGE, circular dichroism, spectrophotometry and so on). It has been concluded that it is necessary to carry out further investigations aimed to studying interrelation of molecular structural features of proteins with indicators of functionality and regularities of their behavior in food systems due to an increase in the production of protein preparations from alternative raw materials (pea, chickpea, sunflower, kidney bean, rice and others). Based on the revealed and newly obtained theoretical information, the targeted modification and regulation of properties of protein ingredients for production of high-quality foods are possible.

Molecular characterization and expression pattern analysis of Sirtuin family genes in the eastern honeybee, Apis cerana

Sirtuin proteins are a family of evolutionarily conserved NAD+-dependent deacetylase enzymes which play crucial roles in regulating insect metabolism, maintaining genome stability, and lifespan. However, there is no report on the genome-wide analysis of the Sirtuin gene family in the eastern honeybee, Apis cerana. Here, 6 Sirtuin genes (AcSirt1, AcSirt2, AcSirt4 ~ AcSirt7) were obtained in the A. cerana genome, which were located on 6 different chromosomes and were all single-copy genes. Gene structure analysis revealed that these 6 genes were all multi-exon genes. The Sirtuin family genes contained 9 conserved motifs and 5 conserved domains, which exhibited typical Sirtuin protein structural characteristics. The phylogenetic analysis of the Sirtuin family genes indicates that A. cerana lacked the Sirt3 gene, and the 6 Sirtuin encoding genes could be clustered into 5 groups and 6 branches, showing high homology with the Apis mellifera and the Apis dorsata. The results of the tissue and period expression profiles by qRT-PCR indicated that the 6 Sirtuin family genes showed transcriptional activity in different tissues of worker bees, and the gene expression level reached the peak in the 4-day-old larvae and pupa stages. It was speculated that Sirtuin genes expression level was affected by fasting behavior, caloric restriction and organ differentiation during different developmental stages of A. cerana. Meanwhile, the differential expression patterns of Sirtuin genes in various tissues further demonstrated the diversity and complexity of their functions, providing a basis for target selection in future studies on the functionality of Sirtuin genes.

Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature

We present a novel system that leverages curators in the loop to develop a dataset and model for detecting structure features and functional annotations at residue-level from standard publication text. Our approach involves the integration of data from multiple resources, including PDBe, EuropePMC, PubMedCentral, and PubMed, combined with annotation guidelines from UniProt, and LitSuggest and HuggingFace models as tools in the annotation process. A team of seven annotators manually curated ten articles for named entities, which we utilized to train a starting PubmedBert model from HuggingFace. Using a human-in-the-loop annotation system, we iteratively developed the best model with commendable performance metrics of 0.90 for precision, 0.92 for recall, and 0.91 for F1-measure. Our proposed system showcases a successful synergy of machine learning techniques and human expertise in curating a dataset for residue-level functional annotations and protein structure features. The results demonstrate the potential for broader applications in protein research, bridging the gap between advanced machine learning models and the indispensable insights of domain experts.

Dealkylation of Macromolecules by Eukaryotic α-Ketoglutarate-Dependent Dioxygenases from the AlkB-like Family.

Alkylating modifications induced by either exogenous chemical agents or endogenous metabolites are some of the main types of damage to DNA, RNA, and proteins in the cell. Although research in recent decades has been almost entirely devoted to the repair of alkyl and in particular methyl DNA damage, more and more data lately suggest that the methylation of RNA bases plays an equally important role in normal functioning and in the development of diseases. Among the most prominent participants in the repair of methylation-induced DNA and RNA damage are human homologs of Escherichia coli AlkB, nonheme Fe(II)/α-ketoglutarate-dependent dioxygenases ABH1-8, and FTO. Moreover, some of these enzymes have been found to act on several protein targets. In this review, we present up-to-date data on specific features of protein structure, substrate specificity, known roles in the organism, and consequences of disfunction of each of the nine human homologs of AlkB. Special attention is given to reports about the effects of natural single-nucleotide polymorphisms on the activity of these enzymes and to potential consequences for carriers of such natural variants.

PH-induced interface protein structure changes to adjust the stability of tilapia protein isolate emulsion prepared by high-pressure homogenization

The pH is a crucial external factor affecting the structure and emulsification characteristics of proteins. The current study aimed to reveal the correlation between the secondary structure changes and tilapia protein isolate (TPI) emulsion stability under different pH (3.0–10.0) prepared by high-pressure homogenization. The results showed that TPI with significantly increased solubility and emulsifying properties when the pH keep away from the isoelectric point (pH 5.0). Meanwhile, TPI emulsions presented significantly enhanced stability (with decreased particle size, increased zeta potential, creaming index close to 0, and uniform dispersion of droplets) at pH 3.0 and 10.0. Interface-adsorbed protein mainly consists of a myosin-heavy chain and actin, and the secondary structure was significantly influenced by pH and high-pressure homogenization. The α-helix will be transformed into β-sheet and β-turn when pH is closer to pH 5.0. However, the high-pressure homogenization induced α-helix conversion to β-sheet. The correlation analysis revealed that emulsion stability is positively correlated with α-helix and negatively correlated with β-sheet. This work provides a deep insight into the correlation between secondary structure changes and the stability of TPI emulsion as affected by pH to offer an alternative way to enhance TPI emulsion stability.

Ultrasound as Green Technology for the Valorization of Pumpkin Leaves: Intensification of Protein Recovery.

The recovery of valuable nutritional compounds, like proteins, from waste streams and by-products is a key strategy for enhancing production sustainability and opening up new market potential. This research aimed to use high-intensity ultrasound as an innovative technique to extract the soluble proteins from the pumpkin leaves. The impact of various sonication amplitudes and duration periods on protein yield, functional properties, antioxidant qualities, and structural characteristics, were studied. Utilization of ultrasound technology significantly increased the yield of pumpkin leaf protein by up to 40%-six times higher than maceration. The ultrasound extraction provided a RuBisCO-rich protein fraction with high radical scavenging and chelating activities, especially at 40% amplitude. Cavitation modified the tertiary and secondary structures of leaf proteins: the amount of α-helix changed based on amplitude (12.3-37.7%), the amount of random coil increased to 20.4%, and the amount of β-turn reduced from 31 to 18.6%. The alteration of the protein fluorescence spectrum (blue shift in spectrum) provides further evidence that ultrasound alters the proteins' molecular structure in comparation with maceration; the maximum tryptophan fluorescence intensity decreased from 22.000 to 17.096. The hydrophobicity values of 76.8-101.5 were substantially higher than the maceration value of 53.4, indicating that ultrasound improved the hydrophobicity of protein surfaces. Ultrasound resulted in a significant increase in solubility in an acidic environment with the increase in sonication amplitude. A 2.4-fold increase in solubility at pH 2 becomes apparent (20% amplitude; 43.1%) versus maceration (18.2%). The emulsifying ability decreases from 6.62 to 5.13 m2/g once the sonication amplitude increases by 20-70%. By combining the ultrasound periods and amplitudes, it is possible to create high-value protein leaf extracts with improved properties which can find real application as food additives and dietary supplements.

Effects of ultrasonic treatment on the structure and functional characteristics of myofibrillar proteins from black soldier fly

In the process of utilizing black soldier fly larvae (BSFL) lipids to develop biodiesel, many by-products will be produced, especially the underutilized protein components. These proteins can be recycled through appropriate treatment and technology, such as the preparation of feed, biofertilizers or other kinds of bio-products, so as to achieve the efficient use of resources and reduce the generation of waste. Myofibrillar protein (MP), as the most important component of protein, is highly susceptible to environmental influences, leading to oxidation and deterioration, which ultimately affects the overall performance of the protein and product quality. For it to be high-quality and fully exploited, in this study, black soldier fly myofibrillar protein (BMP) was extracted and primarily subjected to ultrasonic treatment to investigate the impact of varying ultrasonic powers (300, 500, 700, 900 W) on the structure and functional properties of BMP. The results indicated that as ultrasonic power increased, the sulfhydryl content and turbidity of BMP decreased, leading to a notable improvement in the stability of the protein emulsion system. SEM images corroborated the changes in the microstructure of BMP. Moreover, the enhancement of ultrasound power induced modifications in the intrinsic fluorescence spectra and FTIR spectra of BMP. Additionally, ultrasonic treatment resulted in an increase in carbonyl content and emulsifying activity of BMP, with both peaking at 500 W. It was noteworthy that BMP treated with ultrasound exhibited stronger digestibility compared to the untreated. In summary, 500 W was determined as the optimal ultrasound parameter for this study. Overall, ultrasound modification of insect MPs emerges as a dependable technique capable of altering the structure and functionality of BMP.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser.

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Thirteen New Patients of PPP2R5D Gene Mutation and the Fine Profile of Genotype-Phenotype Correlation Unraveling the Pathogenic Mechanism Underlying Macrocephaly Phenotype.

Neurodevelopmental disorders (NDDs) are a group of diseases that severely affect the physical and mental health of children. The PPP2R5D gene encodes B56δ, the regulatory subunit of protein phosphatase 2A (PP2A). NDDs related to the PPP2R5D gene have recently been defined as Houge-Janssens syndrome 1. Clinical/whole exome sequencing was performed on approximately 3000 patients with NDDs from 2017 to 2023. In vitro experiments were performed to assess the impairment of variants to protein expression and the assembly of PP2A holoenzyme. The genetic information and phenotypes of the reported patients, as well as patients in this study, were summarized, and the genotype-phenotype relationship was analyzed. The probability of pathogenic missense variants in PPP2R5D was predicted using AlphaMissense (AM), and the relationship between certain phenotype and 3D protein structural features were analyzed. Thirteen new patients carrying twelve PPP2R5D gene variants were detected, including five novel missense variants and one novel frameshift variant. In vitro experiments revealed that the frameshift variant p.H463Mfs*3 resulted in a ~50 kDa truncated protein with lower expression level. Except for E420K and T536R, other missense variants impaired holoenzyme assembly. Furthermore, we found that pathogenic/likely pathogenic (P/LP) variants that have been reported so far were all missense variants and clustered in three conserved regions, and the likelihood of P/LP mutations located in these conserved regions was extremely high. In addition, the macrocephaly phenotype was related to negatively charged residues involved in substrate recruitment. We reported thirteen new patients with PPP2R5D gene variants and expanded the PPP2R5D variant spectrum. We confirmed the pathogenicity of novel variants through in vitro experiments. Our findings in genotype-phenotype relationship provide inspiration for genetic counseling and interpretation of variants. We also provide directions for further research on the mechanism of macrocephaly phenotype.

Deep learning of antibody epitopes using positional permutation vectors

BackgroundThe accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. ResultsHere we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate “induced fit” biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. ConclusionsWith the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

ACDMBI: A deep learning model based on community division and multi-source biological information fusion predicts essential proteins

Accurately identifying essential proteins is vital for drug research and disease diagnosis. Traditional centrality methods and machine learning approaches often face challenges in accurately discerning essential proteins, primarily relying on information derived from protein-protein interaction (PPI) networks. Despite attempts by some researchers to integrate biological data and PPI networks for predicting essential proteins, designing effective integration methods remains a challenge. In response to these challenges, this paper presents the ACDMBI model, specifically designed to overcome the aforementioned issues. ACDMBI is comprised of two key modules: feature extraction and classification. In terms of capturing relevant information, we draw insights from three distinct data sources. Initially, structural features of proteins are extracted from the PPI network through community division. Subsequently, these features are further optimized using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Moving forward, protein features are extracted from gene expression data utilizing Bidirectional Long Short-Term Memory networks (BiLSTM) and a multi-head self-attention mechanism. Finally, protein features are derived by mapping subcellular localization data to a one-dimensional vector and processing it through fully connected layers. In the classification phase, we integrate features extracted from three different data sources, crafting a multi-layer deep neural network (DNN) for protein classification prediction. Experimental results on brewing yeast data showcase the ACDMBI model’s superior performance, with AUC reaching 0.9533 and AUPR reaching 0.9153. Ablation experiments further reveal that the effective integration of features from diverse biological information significantly boosts the model’s performance.

Effect of oxidized starch on the storage stability of frozen raw noodles: Water distribution, protein structure, and quality attributes.

Freezing is a popular method of food preservation with multiple advantages. However, it may change the internal composition and quality of food. This study aimed to investigate the effect of modified starch on the storage stability of frozen raw noodles (FRNs) under refrigerated storage conditions. Oxidized starch (OS), a modified starch, is widely used in the food industry. In the present study, texture and cooking loss rate analyses showed that the hardness and chewiness of FRNs with added OS increased and the cooking loss rate decreased during the frozen storage process. Low-field nuclear magnetic resonance characterization confirmed that the water-holding capacity of FRNs with OS was enhanced. When 6% OS was added, the maximum freezable water content of FRNs was lower than the minimum freezable water content (51%) of FRNs without OS during freezing. Fourier-transform infrared spectroscopy showed that after the addition of OS, the secondary structures beneficial for structural maintenance were increased, forming a denser protein network and improving the microstructure of FRNs. In summary, the water state, protein structure, and quality characteristics of FRNs were improved by the addition of OS within an appropriate range.

Thawing of Frozen Hairtail (Trichiurus lepturus) with Graphene Nanoparticles Combined with Radio Frequency: Variations in Protein Aggregation, Structural Characteristics, and Stability.

Efficient thawing can preserve the quality of frozen hairtail (Trichiurus lepturus) close to that of fresh hairtail. In contrast to air thawing (AT) and radio-frequency thawing (RT), this study looked at how graphene oxide (GO) and graphene magnetic (GM) nanoparticles paired with RT affect the microstructure and protein conformation of hairtails after thawing. The results suggested that GM-RT can reduce the myofibrillar protein (MP) damage and be more effective than other thawing treatments, like AT, RT, and GO-RT, in maintaining the microstructure of hairtail. The particle size and zeta potential showed that GM-RT could reduce the aggregation of MP during the thawing process compared to other thawing methods. Moreover, the texture of the hairtail after GM-RT exhibited higher hardness (1185.25 g), elasticity (2.25 mm), and chewiness (5.75 mJ) values compared to other thawing treatments. Especially compared with RT, the GM-RT treatment displayed significant improvements in hardness (27.24%), a considerable increase in springiness (92.23%), and an increase in chewiness (57.96%). GO-RT and GM-RT significantly reduced the centrifugal loss. The scanning electron microscopy results demonstrated that the effect of GM-RT was more akin to that of a fresh sample (FS) and characterized by a well-organized microstructure. In conclusion, GM-RT effectively diminished the MP aggregation and improved the texture of thawed fish. It can be regarded as a viable alternative thawing technique to enhance MP stability, which is vital for preserving meat quality.

Int&in: A machine learning-based web server for active split site identification in inteins.

Inteins are proteins that excise themselves out of host proteins and ligate the flanking polypeptides in an auto-catalytic process called protein splicing. In nature, inteins are either contiguous or split. In the case of split inteins, the two fragments must first form a complex for the splicing to occur. Contiguous inteins have previously been artificially split in two fragments because split inteins allow for distinct applications than contiguous ones. Even naturally split inteins have been split at unnatural split sites to obtain fragments with reduced affinity for one another, which are useful to create conditional inteins or to study protein-protein interactions. So far, split sites in inteins have been heuristically identified. We developed Int&in, a web server freely available for academic research (https://intein.biologie.uni-freiburg.de) that runs a machine learning model using logistic regression to predict active and inactive split sites in inteins with high accuracy. The model was trained on a dataset of 126 split sites generated using the gp41-1, Npu DnaE and CL inteins and validated using 97 split sites extracted from the literature. Despite the limited data size, the model, which uses various protein structural features, as well as sequence conservation information, achieves an accuracy of 0.79 and 0.78 for the training and testing sets, respectively. We envision Int&in will facilitate the engineering of novel split inteins for applications in synthetic and cell biology.

Effects of the extraction conditions on functional and structural characteristics of proteins from fenugreek seeds.

The aim of this study is to optimize the extraction process and characterize the proteins found in fenugreek seeds. The water and oil holding capacities, coagulated protein content, foaming and emulsification properties of the isolated proteins at all extraction conditions were investigated. Also, solubility, molecular weights, structural and thermal properties were determined. In the extraction processes carried out at different pHs (pH 6.0-12.0) and solid:solvent ratios (20-60 g/L), it was determined that the highest extraction yield (94.3±0.3%) was achieved when the pH was 11.47 and the solid-solvent ratio was 34.50 g/L. Three distinct bands (46, 59 and 80 kDa) in the range of 22-175 kDa were determined for the fenugreek seed protein isolate obtained at optimum extraction conditions. Protein secondary structures were achieved using Fourier Transform Infrared (FT-IR) spectra and it was determined that β-sheet structures were highly present. In addition, denaturation temperatures and denaturation enthalpy were calculated as ~119°C and 28 mJ/g, respectively.

Genome mining of Mycobacterium tuberculosis: targeting SufD as a novel drug candidate through in silico characterization and inhibitor screening.

Tuberculosis (TB) stands as the second most fatal infectious disease globally, causing 1.3 million deaths in 2022. The resurgence of TB and the alarming rise of antibiotic resistance demand urgent call to develop novel antituberculosis drugs. Despite concerted efforts to control TB, the disease persists and spreads rapidly on a global scale. Targeting stress response pathways in Mycobacterium tuberculosis (Mtb) has become imperative to achieve complete eradication. This study employs subtractive genomics to identify and prioritize potential drug targets among the hypothetical proteins of Mtb, focusing on indispensable pathways. Amongst 177 essential hypothetical proteins, 152 were nonhomologous to human. These proteins participated in 34 pathways, and a 20-fold enrichment of SUF pathway genes led to its selection as a target pathway. Fe-S clusters are fundamental, widely distributed protein cofactors involved in vital cellular processes. The survival of Mtb in a hypoxic environment relies on the iron-sulfur (Fe-S) cluster biogenesis pathway for the repair of damaged Fe-S clusters. It also protects pathogen against drugs, ensuring controlled iron utilization and contributing to drug resistance. In Mtb, six proteins of Fe-S cluster assembly pathway are encoded by the suf operon. The present study was focused on SufD because of its role in iron acquisition and prevention of Fenton reaction. The research further delves into the in silico characterization of SufD, utilizing bioinformatics tools for sequence and structure based analysis. The protein's structural features, including the identification of conserved regions, motifs, and 3D structure prediction enhanced functional annotation. Target based virtual screening of compounds from the ChEMBL database resulted in 12 inhibitors with best binding affinities. Drug likeness and ADMET profiling of potential inhibitors identified promising compounds with favorable drug-like properties. The study also involved cloning in SUMO-pRSF-Duet1 expression vector, overexpression, and purification of recombinant SufD from E. coli BL21 (DE3) cells. Optimization of expression conditions resulted in soluble production, and subsequent purification highlighting the efficacy of the SUMO fusion system for challenging Mtb proteins in E. coli. These findings provide valuable insights into pharmacological targets for future experimental studies, holding promise for the development of targeted therapy against Mtb.

Identification of the GAox gene family in potato (Solanum tuberosum L.) and its expression analysis in response to drought stress

BackgroundGAox is a key enzyme involved in GA biosynthesis pathway and plays an important role in regulating various processes in plant life cycle. However, it has not been systematic, studies have been conducted in potato, which is the world's fourth largest food crop.MethodsIn this work, we systematically identified GAox gene family (StGAox) in potato by analyzing the potato genome sequence using a set of bioinformatics approaches, and analyze their physical, chemical properties, distribution on chromosomes, gene structure, conserved motifs, gene duplication events and expression patterns were analyzed.ResultsThe results showed that a total of 33 GAox proteins were identified and unevenly distributed on 10 chromosomes. Based on their protein structure and phylogenetic characteristics, these 33 StGAoxes were divided into 5 distinct subclasses. Collinearity analysis revealed that there were 5 pairs of duplicated genes in the StGAox gene family, and all of which evolved under purifying selection. Analysis of RNA-seq data of double haploid (DM) potatoes under different tissues, abiotic stresses and hormone treatments showed that PG0002068, PG0024249 and PG0027963 were higher expressed in leaves, PG009427, PG0026762, PG0009021 and PG0021095 were higher expressed in tubers, PG2003479, PG0024249, PG0005698, and PG0009021were higher expressed in shoots than those of other tissues. In addition, the expression of PG0002068, PG2003479, PG0032156, PG0024249, and PG0021292 were up-regulated under mannitol and drought stress.ConclusionsComparative genome-wide analysis of StGAox genes and their expression analyses revealed that members of this family may be involved in tissue-specific developmental and abiotic stress responses.Graphical

Prioritizing genomic variants pathogenicity via DNA, RNA, and protein-level features based on extreme gradient boosting.

Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains essential but challenging to understand the functional consequences of these genetic variants, especially the noncoding ones, due to the lack of corresponding annotations. While many computational methods have been proposed to identify the risk variants. Most of them have only curated DNA-level and protein-level annotations to predict the pathogenicity of the variants, and others have been restricted to missense variants exclusively. In this study, we have curated DNA-, RNA-, and protein-level features to discriminate disease-causing variants in both coding and noncoding regions, where the features of protein sequences and protein structures have been shown essential for analyzing missense variants in coding regions while the features related to RNA-splicing and RBP binding are significant for variants in noncoding regions and synonymous variants in coding regions. Through the integration of these features, we have formulated the Multi-level feature Genomic Variants Predictor (ML-GVP) using the gradient boosting tree. The method has been trained on more than 400,000 variants in the Sherloc-training set from the 6th critical assessment of genome interpretation with superior performance. The method is one of the two best-performing predictors on the blind test in the Sherloc assessment, and is further confirmed by another independent test dataset of de novo variants.