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The structure and activity of enzymes are influenced by pH value of their surroundings. Although many enzymes work well in the pH range from 6 to 8, some specific enzymes have good efficiencies only in acidic (pH<5) or alkaline (pH>9) solution. Studies have demonstrated that the activities of enzymes correlate with their primary sequences. It is cr...
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Citations
... With this information, the capability of the orthologous enzyme to bind the substrate of interest can be preliminarily evaluated. In addition, generalized predictors of industrially relevant properties, such as solubility, [51,52] thermostability, [53][54][55][56] and pH stability [57] may be included in the selection process. However, the bioinformatic workflow heavily relies on data-informed or experience-guided assessments of the models to avoid undesirable trade-offs, such as increased protein stability at the cost of significantly lower expression, solubility, or activity (Fig. 2, Table 1). ...
Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.
... Determining the optimal pH for the enzymatic activity of the proteins is very important for determining the pH for the media suitable for enhanced protein expression in heterologous systems. Amino acid sequences of test [28] proteins were submitted to "AcalPred" (http://lin-group.cn/server/AcalPred) with default parameters to predict a probability value that most likely determines maximum enzyme activity of protein at particular acidic or alkaline pH. ...
Bovine trichomonosis is one of the neglected tropical diseases of cattle that is resulting in severe reproductive failure. With present knowledge, disease diagnosis and maintaining the infected animals in the quarantine are the only available strategies. Several spillover incidences of Trichomonas foetus had also resulted in zoonotic transmission to humans. In spite of above circumstances, till date there are no point of care diagnostics developed for screening bovine trichomoniasis in cattle. In the light of above circumstances, there exists a demand for cost-effective diagnostic kits to be provided to farming community. This current study highlights evaluation of few surface proteins of Trichomonas foetus for the suitability as sero-diagnostic markers. Few target Proteins such as Adhesin, Immuno-dominant variable surface antigen-like protein, Polymorphic membrane protein - like protein, GP-63-like (Clan MA, family M8) protein and Hypothetical protein (OHS95735.1) were evaluated for suitable pH, Signal Peptide, protein glycosylation pattern using freely available Bioinformatics tools. Mapping of potential epitopes of all the target proteins was done using immunoinformatics tools. Among the above proteins, GP63-like protein, immuno-dominant variable antigenic domain-like protein, and polymorphic membrane protein-like proteins are most suitable as diagnostic targets, owing to their higher levels of glycosylation, large epitope domains, and showing structural similarities with the domains of known toxic proteins. On the other hand, adhesin protein has the potential to be exploited as a vaccine candidate. The above proteins are suitable to be expressed in suitable host system and validated the immunogenic potential by animal inoculation and by testing with the real samples.
... Sherief et al., (2012) reported maximum enzyme production by A. fumigatus and A. terreus at pH 6.0 and 7.0. The optimal pH for the activity and growth of organisms usually occurs within a range of pH and this influences the transport systems of enzymes across cell membranes (Lin et al., 2013) In solid state fermentation, microbial growth and activity is critically affected by an appropriate level of moisture (Almowallad, et al., 2022). In this research, most cultures exhibited better activities at medium moisture contents 65%. ...
The use of microorganisms for xylanase production plays important role in the bioconversion of lignocelluloses and also required in huge amount for industrial level application. This necessitates the need to select potent microorganisms for xylanase production, followed by optimization of media components for enhanced production. The effects of altering cultural fermentation conditions on the xylanase production ability in maize cobs were investigated. A consortium of four fungi; Lenzites betulina, Trichoderma reesei, Lachnocladium specie and Aspergillus niger were used to carry out single and mixed solid-state fermentation on NaOH pretreated maize cobs. Optimization of fermentation factors were carried out from ten groups of individual and co-fermented fungal combinations. L. flavidum was found to be the most effective xylanase producer with optimal conditions at pH of 5.5, moisture 75%, inoculum concentration at 5-6 x 103 spores/ml, incubation period of 7-9 days and 1% peptone as the best nitrogen media supplement. Variation to different degrees in the degradation of the maize cobs were observed. A 10% decrease in cellulose was observed with co- cultures of T. reesei and A. niger and a 15% decrease in the hemicellulose fraction. The biotechnological potential of corn cobs has been enhanced by the screening and optimizing the culture conditions.
... One possibility is that the pH level may not have been considered an important factor in the study or may not have been relevant to the specific research question. But it is known that different enzymes activity can be influenced by solubility, temperature and pH value and most enzymes remain high activity in the pH range between 6 and 8, but others need extremely acidic or alkaline conditions such as pepsin and alkaline phosphatases [56]- [58]. ...
During food production significant amounts of organic waste is generated annually that can have a negative effect on the environment due to lack of efficient utilisation solutions and insufficient disposal practices. Fungi and their remarkable abilities to produce enzymes can be applied for hydrolysing different types of food waste in simpler sugars. Under optimal conditions, fungal hydrolysis of food waste can be rapid and efficient. Currently, the capacity of this process has only been briefly demonstrated in previous studies. This review describes different practices demonstrating the potential of fungal hydrolysis use for efficient resource management. The focus was on what organisms, waste substrates and parameters as temperature, pH level, have been applied in previous studies as well as glucose recovery yields. It was concluded that food waste can be efficiently hydrolysed and used as a substrate for the downstream production of value-added products using sequential fermentation. The optimal temperature was concluded to be above 45 °C, but the optimal pH level may vary depending on used organism and substate. In future research the possibility of optimizing fungal strains, creating mutants with enhanced enzyme-producing abilities, and application of more GRAS fungal species should be investigated. To conduct valorisation tests on new residues for fungal hydrolysis researchers must collaborate with manufacturers, thus exploring the suitability of a wider range of waste residues for fungal hydrolysis.
... In our previous study, 587 genes encoding 316 cellulases, 259 hemicellulases, and 12 pectinesterases and pectate lyases were observed in the C. gestroi gut based on KEGG database annotation of the metagenomic DNA of free-living prokaryotes [8,16]. Using the AcalPred tool [17], 59% of lignocellulolytic enzymes have been predicted to be alkaline [18]. The abundant orders and species of free-living bacteria in the termite gut have been analyzed in the past [8], but the overall picture of the bacte-the termite gut has recently emerged as a particularly intriguing topic for researchers all over the world [7,34]. ...
Simple Summary
The lower termite Coptotermes gestroi is widely distributed in Northern Vietnam and is an important urban pest that destroys wooden constructions such as pagodas, temples, and furniture. Although the metagenomic DNA of prokaryotes freely living in the termite gut has been sequenced and analyzed, the overall picture of the prokaryotic diversity, including archaea and their function, has not been investigated. The present study, for the first time, revealed the differences in the structure and function of free-living prokaryotes in the termite gut. The bacterial community was formed and adapted to aid the hosts’ survival and development even in the presence of pesticides. Beyond the potential function of the bacteria towards lignocellulose digestion, free-living bacteria and archaea also harbor diverse genes coding for the enzymes/proteins involved in reductive acetogenesis, methanogenesis, methane and sulfur metabolism, and nitrogen fixation and recycling to supply energy for the host; the synthesis of antibiotics for host defense; and the detoxification of aromatic compounds. The present study provides a valuable scientific basis for the mining of novel bacterial genes and the isolation of bacteria from the termite gut for agricultural, environmental, pharmaceutical, and medical applications.
Abstract
Termites’ digestive systems, particularly in lower termites with the presence of protozoa, are unique ecological niches that shelter a diverse microbiota with a variety of functions for the host and the environment. In 2012, the metagenomic DNA (5.4 Gb) of the prokaryotes that freely live in the gut of the lower termite Coptotermes gestroi were sequenced. A total of 125,431 genes were predicted and analyzed in order to mine lignocellulolytic genes. however, the overall picture of the structure, diversity, and function of the prokaryotic gut microbiota was not investigated. In the present study, these 125,431 genes were taxonomically classified by MEGAN and functionally annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and by the Carbohydrate-Active enZYmes (CAZy) and HMMER databases. As a result, 95,751 bacterial genes were classified into 35 phyla. The structure of the bacteria, typified by a high ratio of Firmicutes to Bacterioidetes, was distinct from the structure of the entirety of the bacteria in the lower or higher termites’ guts. The archaea (533 genes) were distributed into 4 phyla, 10 classes, 15 orders, 21 families, 47 genera, and 61 species. Although freely living in the guts, the prokaryotic community was formed, developed, and adapted to exhibit unique interactions in order to perform mutual roles of benefit to their hosts. Methanobacteriales, accounting for 61% of the archaea symbionts, seem to play an important role in methanogenesis. Concomitantly, bacterial methanotrophs in the gut utilize methane and combine with other bacterial groups, including potential lignocellulolytic degraders, acetogens, sulfur bacteria, and nitrogen-recycling bacteria, to efficiently convert wood with little nitrogen into acetates via certain pathway modules specified by prokaryotes that freely live in the gut. This forms an important energy source for the termites. Furthermore, bacteria carry 2223 genes involved in the biosynthesis of 17 antibiotic groups. The gut bacteria also possess genes for the degradation of 18 toxic aromatic compounds, of which four are commercial pesticides against termites commonly used for the preservation of wooden constructions. Eight of the eighteen pathways were the first to be reported from the termite gut. Overall, this study sheds light on the roles of the freely living bacteria and archaea in the C. gestroi gut, providing evidence that the gut microbiome acts as the second host genome, contributing both nutrients and immunity to support the host’s existence, growth, and development.
... However, the cost of creating reaction conditions with strong alkaline is high and unsuitable for industrial mass production (Wang et al. 2020b). Most enzymes can maintain high activity in the pH range between 6 and 8; some specific enzymes work well only in highly acidic (i.e., pH 5.0) or alkaline (i.e., pH 9.0) conditions (Lin et al. 2013). To obtain high enzyme activity at a wide range of pH values, modification of the acidic-alkaline tolerance of the enzyme is required (Lancaster et al. 2018). ...
... Enzymes in the organism of acidophilic microorganisms are likely to be resistant to an acidic environment (Raddadi et al. 2015). It has also been shown that the activity and stability of enzymes are related to their primary sequences (Lin et al. 2013). Therefore, acid-resistant enzymes can be mined from acidophilic microorganisms and enzyme database (Fig. 2a). ...
... With the development of molecular biology and computer technology, enzyme databases are becoming larger and larger. The primary databases are GenBank, BRENDA, UniProt, etc. Lin et al. developed a sequence-based method to discriminate enzymes with an acidic or alkaline pH opt , which can correctly predict 96.3% and 97.1% enzymes with an acidic and alkaline pH opt , respectively, and achieved an overall accuracy of 96.7% (Lin et al. 2013). Machine learning methods have been Strategies for mining, modification, and de novo synthesis of acid-resistant enzymes. ...
Enzymes have promising applications in chemicals, food, pharmaceuticals, and other variety products because of their high efficiency, specificity, and environmentally friendly properties. However, due to the complexity of raw materials, pH, temperature, solvents, etc., the application range of enzymes is greatly limited in the industry. Protein engineering and enzyme immobilization are classical strategies to overcome the limitations of industrial applications. Although the pH tendency of enzymes has been extensively researched, the mechanism underlying enzyme acid resistance is unclear, and a less practical strategy for altering the pH propensity of enzymes has been suggested. This review proposes that the optimum pH of enzyme is determined by the pKa values of active center ionizable amino acid residues. Three levels of acquiring acid-resistant enzymes are summarized: mining from extreme environments and enzyme databases, modification with protein engineering and enzyme microenvironment engineering, and de novo synthesis. The industrial applications of acid-resistant enzymes in chemicals, food, and pharmaceuticals are also summarized.
Key points
• The mechanism of enzyme acid resistance is fundamentally determined.
• The three aspects of the method for acquiring acid-resistant enzymes are summarized.
• Computer-aided strategies and artificial intelligence are used to obtain acid-resistant enzymes.
Graphical abstract
... The G-gap dipeptide encoding scheme incorporates the amino acid frequency information of the peptide sequence, where g' is a parameter that represents a dipeptide with a gap of 'g' amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y, B) (Lin et al., 2013;Lin et al., 2015;Xu et al., 2018). In this study, we tried 0, 1, 2,3, and 4-gap dipeptides to encode each protein peptide. ...
Neuropeptides contain more chemical information than other classical neurotransmitters and have multiple receptor recognition sites. These characteristics allow neuropeptides to have a correspondingly higher selectivity for nerve receptors and fewer side effects. Traditional experimental methods, such as mass spectrometry and liquid chromatography technology, still need the support of a complete neuropeptide precursor database and the basic characteristics of neuropeptides. Incomplete neuropeptide precursor and information databases will lead to false-positives or reduce the sensitivity of recognition. In recent years, studies have proven that machine learning methods can rapidly and effectively predict neuropeptides. In this work, we have made a systematic attempt to create an ensemble tool based on four convolution neural network models. These baseline models were separately trained on one-hot encoding, AAIndex, G-gap dipeptide encoding and word2vec and integrated using Gaussian Naive Bayes (NB) to construct our predictor designated NeuroCNN_GNB. Both 5-fold cross-validation tests using benchmark datasets and independent tests showed that NeuroCNN_GNB outperformed other state-of-the-art methods. Furthermore, this novel framework provides essential interpretations that aid the understanding of model success by leveraging the powerful Shapley Additive exPlanation (SHAP) algorithm, thereby highlighting the most important features relevant for predicting neuropeptides.
... Researchers have employed computational approaches to investigate protein pH relationships with biophysical methods, [30][31][32] and to predict enzyme pHopt using traditional machine learning models with limited datasets (fewer than 500 proteins). 7,[33][34][35][36][37][38][39] Despite these efforts, the adoption of Enzyme optimum pH (pHopt) and organism environment pH (pHenv) are used to train various machine learning methods with different sequence representations. For each method, multiple models are trained with varying hyperparameters, and the optimal model, selected by performance on the validation set, is assessed on a held-out testing set. ...
The relationship between pH and enzyme catalytic activity, as well as the optimal pH (pH opt ) at which enzymes function, is crucial for biotechnological applications. Consequently, computational methods that predict pH opt would significantly benefit enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at a specific pH, and by promoting a better understanding of how sequence affects enzyme function in relation to pH. In this study, we present EpHod (Enzyme pH optimum prediction with deep learning), which is a deep semi-supervised language model for predicting enzyme pH opt directly from the protein sequence. By evaluating various machine learning methods with extensive hyperparameter optimization (training over 4,000 models in total), we find that semi-supervised methods that utilize language model embeddings, including EpHod, achieve the lowest error in predicting pH opt . From sequence data alone, EpHod learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic center and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and could potentially speed up the development of improved enzyme technologies.
... Longest sequence was selected starting from methionine and ending at stop codon. It is important to analyze the nature of protein as the activity of enzyme is largely affected by pH value (Lin et al., 2013). The type of enzyme either it is alkaline or acidic, was predicted through AcalPred (http://lin-group.cn/server/AcalPred) which is a sequence-based tool for discriminating between alkaline and acidic enzyme (Lin et al., 2013). ...
... It is important to analyze the nature of protein as the activity of enzyme is largely affected by pH value (Lin et al., 2013). The type of enzyme either it is alkaline or acidic, was predicted through AcalPred (http://lin-group.cn/server/AcalPred) which is a sequence-based tool for discriminating between alkaline and acidic enzyme (Lin et al., 2013). Nature of protein, either it was acidic or alkaline, was detected by giving FASTA sequence of protein to AcalPred. ...
The degradation of plastic waste has become a critical environmental issue, and microbial degradation is an effective method to address this problem. Cytochrome P450 is a highly efficient enzyme for degrading various pollutants, including plastic, due to its broad substrate specificity. This study aimed to predict the degradation potential of cytochrome P450 for two types of plastic, namely polycarbonate and phenol formaldehyde. Additionally, the study aimed to identify newly emerging harmful plastics and assess their toxicity levels. Several essential tools were used to design the study, including retrieving the sequence of the targeted strain from NCBI, multiple sequence alignment, and phylogenetic analysis. The gene was translated into protein, and the protein model was generated and purified. The AcalPred computational tool was used to predict the enzyme's acidic and alkaline nature. Active sites were detected, and the protein was docked with polycarbonate and phenol formaldehyde separately. PyMol was used to visualize the interaction between the receptor protein cytochrome P450 and the ligands polycarbonate and phenol formaldehyde. Effective energies were observed towards the degradation of polycarbonate and phenol formaldehyde. Dietza maris was more effective at degrading polycarbonate than phenol formaldehyde. Both types of plastic were found to be harmful based on toxicity analysis using ECOSAR. A computational tool called Discovery Studio was used for the detailed interaction studies. The gene encoding cytochrome P450 was cloned into the pUC-19 vector for the purpose of predicting its integration into other organisms and potential downstream applications. This suggests that the use of in-silico degradation methods can be employed to predict the degradation potential of any enzyme, while in-vitro studies can be conducted to determine the actual degradation ability of the enzyme.
... However, due to a pK a of 6.5, chitosan is sufficiently protonated and readily soluble only at pH values below 5 [15]. At these pH values, the formation of protein-loaded NPs with highly preserved protein bioactivity may be limited to the proteins (e.g., enzymes) that are not susceptible to denaturation at pH values below physiological conditions [16]. Synthetic polycations such as polyethylenimine (PEI), poly(L-lysine) (PLL), or poly(ethylene argininylaspartate diglyceride) (PEAD) have also been intensively studied [17]. ...
