Amino Acid Composition Features Research Articles

Comparison of gel and functional properties of gelatin derived from two jellyfish Stomolophus meleagris and Rhopilema esculentum kishinouye

The gel and functional properties of jellyfish gelatin from Stomolophus meleagris (SG) and Rhopilema esculentum kishinouye (RG) species were investigated, and commercial fish gelatin (FG) was used as a control. In comparison to FG, SG and RG had similar X-ray diffraction (XRD) and fourier transform infrared (FTIR) pattern with shorter amide A band position, representing the formation of triple helices and hydrogen bonds due to polar groups in gelatin, and indicating a more ordered structure. Moreover, FG and RG presented elastic dominance while SG exhibited viscous dominance in a dose-dependent behavior from 10%-20%, with G′ increasing approximately 785-1374 times when comparing SG and RG at 1 Hz due to their different characteristic amino acid composition. RG showed the least water mobility and most abundant hydrogen proton density, thus supporting greater gel strength than SG. Furthermore, SG showed a sparse and loose network with large voids, while FG and RG showed dense and homogeneous gel networks with smaller holes, which was conductive to the resistance of external forces. In addition, SG showed superior foam stability (20%-200% increase) and emulsifying stability index (ESI) (12%-86% increase) than those of FG and RG in a dose-dependent manner due to larger pores and more hydrophobic amino acid contents. The comparable gelation behaviors of RG to FG and remarkable interfacial properties of SG to FG supported the possible application of SG and RG as marine-based gelling, foaming and emulsifying agents in the food sector.

FSP4HSP: Frequent sequential patterns for the improved classification of heat shock proteins, their families, and sub-types

Heat shock proteins (HSPs) from different families and sub-types play a vital role in the folding and unfolding of proteins, in maintaining cellular health, and in preventing serious disorders. Previous computational methods for HSP classification have yielded promising performance. However, most of the existing methods rely heavily on amino acid composition features and still face challenges related to interpretability and accuracy. To overcome these issues, we introduce a novel frequent sequential pattern (FSP)-based analysis and classification method for the classification of HSPs, their families, and sub-types. The proposed method is called FSP4HSP, which stands for “FSP for HSP”. It identifies FSPs of amino acids (FSPAAs) and utilizes them for analysis and classification. Besides FSPAAs, sequential rules among amino acids are also discovered. Both binary and multi-class classification scenarios are considered, with the utilization of eight integer-based and four string-based classifiers. The incorporation of FSPAAs in the classification/prediction task enhances the interpretability of FSP4HSP and a comprehensive performance comparison using various evaluation measures demonstrates that it surpasses existing methods for the classification/recognition of HSPs.

Prediction of Plant Ubiquitylation Proteins and Sites by Fusing Multiple Features

Introduction: Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have developed some large-scale computational methods to predict ubiquitylation sites, but there is still much room for improvement. Much of the research related to ubiquitylation is cross-species while the life pattern is diversified, and the prediction method always shows its specificity in practical application. This study just aims at the issue of plants and has constructed computational methods for identifying ubiquitylation protein and ubiquitylation sites. Method: In this work, we constructed two predictive models to identify plant ubiquitylation proteins and sites. First, in the ubiquitylation proteins prediction model, in order to better reflect protein sequence information and obtain better prediction results, the KNN scoring matrix model based on functional domain Gene Ontology (GO) annotation and word embedding model, i.e. Skip-Gram and Continuous Bag of Words (CBOW), are used to extract the features, and the light gradient boosting machine (LGBM) is selected as the ubiquitylation proteins prediction engine. Results: As a result, accuracy (ACC), Precision, recall rate (Recall), F1_score and AUC are respectively 85.12%, 80.96%, 72.80%, 76.37% and 0.9193 in the 10-fold cross-validations on independent dataset. In the ubiquitylation sites prediction model, Skip-Gram, CBOW and enhanced amino acid composition (EAAC) feature extraction codes were used to extract protein sequence fragment features, and the predicted results on training and independent test data have also achieved good performance. Conclusion: In a word, the comparison results demonstrate that our models have a decided advantage in predicting ubiquitylation proteins and sites, and it may provide useful insights for studying the mechanisms and modulation of ubiquitination pathways

Sequence-Structure Based Comparison of Structurally Homologous Thermophilic and Mesophilic Polyethylene Terephthalate (PET) Hydrolases

Protein structure has a direct impact on thermostability. Deviations in the primary sequence can affect structural changes, leading to alterations in thermostability properties. However, the molecular basis of protein thermostability is unspecified; thus, elucidation of key factors that role particular protein thermostability is required when engineering proteins to be thermostable. To address this challenge, the amino acid composition, hydrophobicity/hydrophilicity ratio, cysteine bridges, and intrinsic features of two structurally homologous but different thermostability, poly(ethylene terephthalate) hydrolase (PETase) were compared. According to the findings, thermostable and thermolabile PETases have similar folds, compactness, and disulfide bridges. Interestingly, an abundance gap of aromaticity, hydrophobic cluster area, polar amino acid and hydrogen bond network compositions demonstrated dominant trends of variations for both PET hydrolases, indicating a pivotal role of these features in the thermostability of PET hydrolase. Furthermore, increased hydrophobic amino acid frequency in the inner surface of thermostable proteins contributed significantly to thermostability by forming more internal hydrophobic interactions and a less hydrophobic patch. There are no consistent trends in insertions and deletions between both PETases. Taken together, these observations demonstrate that hydrophobicity and hydrogen bond networks are essential factors in thermostability of thermostable PETase.

Differences in amino acid composition of carrot α-tubulin potentially confer the resistance to dinitroaniline herbicides

Aim. To reveal the features of amino acid composition of carrot α-tubulin isotypes that potentially determine natural tolerance to dinitroaniline herbicides. Methods. Literature and database search. Comparison of protein sequences and structures: multiple sequence alignment, phylogenetic profiling, protein and ligand structure modeling, etc. Results. Genomic and proteomic analysis of Daucus carota has revealed at least eight unique isotypes of α-tubulin that differ in amino acid sequences and gene loci. Remarkable differences in amino acid composition of the dinitroanilinebinding-like (DBL) region of analyzed α-tubulin have been revealed, which may be the reason of its natural resistance to these compounds. Сonclusions. Differences in amino acids at positions of canonical mutations – Cys4 (TBA1, 2, 3, 6, 7 and 8), Thr53 (TBA6), Ile202 (TBA1 and 7) and Met202 (TBA5), as well as previously undescribed non-canonical substitutions – Ile4 (TBA4 and 5), Cys52 (TBA6), Ser201 (TBA1, 2, 3 and 8) and Val194 (TBA4 and 5), were noted as potentially associated with natural tolerance of the carrot to dinitroaniline herbicides.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea.

Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease. In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale. A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

Disordered-ordered protein binary classification by circular dichroism spectroscopy.

Intrinsically disordered proteins are abundant in nature and responsible for a plethora of cellular functions. They lack a stable tertiary structure and form dynamic conformational ensembles due to their characteristic physicochemical properties and amino acid composition. While numerous bioinformatics tools have been developed for in silico disorder prediction in the last decades, there is a need for experimental methods to verify the disordered state. CD spectroscopy is widely used for protein secondary structure analysis. Even without providing high-resolution information, it is especially useful when NMR, X-ray, or other techniques are problematic or one simply needs a fast technique to verify the structure of proteins. Here, we proposed an automatized binary disorder-order classification method by analyzing far-UV CD spectroscopy data. The method needs CD data at only three wavelength points, making high-throughput data collection possible. The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. To reach the best classification accuracy, CD of the protein should be measurable down to 197 nm in good quality. However, the method can be used even if the spectrum can be recorded down to the wavelength of 212 nm due to the strong absorbance of the samples. The method is implemented on a webserver (https://bestsel.elte.hu/idp_classification.php) and freely available for academic users to help the experimental identification of intrinsically disordered proteins (IDPs) and the verification of the results of bioinformatics tools. This study was supported by the National Research, Development and Innovation Office of Hungary (grants 2017-1.2.1-NKP-2017-00002, PD135510, K120391, K125340, K131702, K138937 and 2019-2.1.11-TÉT-2020-00101). SRCD measurements were supported by SOLEIL (Proposals 20181890, 20191810, and 20200751).

Biomorphological features of plants and amino acid composition of <i>Agastache anisatus</i> and <i>Agastache rugosa</i> (Lamiaceae) proteins under culture conditions in the North

This paper summarizes the results of a long-term collection study of the growth and development of plants of two species of polygrate Agastache anisatus and A. rugosa (Lamiaceae) in the Botanical Garden of the Institute of Biology, Komi Scientific Center, Ural Branch, Russian Academy of Sciences. The material for the study was attracted according to the exchange lists of seeds from other botanical gardens and abroad. Under the conditions of the culture of the middle taiga subzone of the Komi Republic, both types of multi-grate behave like juvenile herbaceous plants. Seeds of these species do not need pre-sowing preparation. When sown in open ground at the end of May the first ten days of June, mass seedlings were noted on the 1220th day, depending on the weather conditions of the season. During the first year of life, the plants successively passed through all phases of the pregenerative period, and only 510% of plants passed into the generative period in September, forming one shoot up to 4045 cm high with 13 inflorescences. Winter hardiness of plants of the pregenerative period is quite high, 7085%. The following year, plant regrowth was observed in mid may. They entered the flowering phase at the end of July August, forming 35 generative shoots per individual 5080 cm high, branched in the upper part of the stem up to the second or third order. At the same time, under the conditions of a short northern summer, for 120130 days of vegetation, plants of the second year of life are able to form abundant self-sowing, but as a result, winter hardiness of plants fell to 10%. Overwintering was tolerated by single individuals, 24 specimens from 2030 generative plants. A more optimal option for growing multi-grate plants in the north is the seedling method. When plants were planted at the age of 3040 days in early June in open ground with an optimal feeding area of 40 40 cm, large branched shoots 5065 cm high were formed, which until mid-September went through the phases of flowering and fruiting, forming mature seeds. Winter hardiness of seedlings of plants of the first year of life is low. It has been established that under the culture conditions of the middle taiga subzone of the Komi Republic, plants of the multi-grate are able to accumulate a large amount of nitrogenous substances in the aboveground phytomass. Thus, a high content of crude protein was found in the aboveground phytomass of plants of the first and second years of life 26,228,8% in Agastache anisatus and 21,922,5% A. rugosa. 17 amino acids, including 7 essential amino acids, were found and determined in the above-ground phytomass of plants of polygonium. The proportion of essential amino acids varied from 34 to 39%. The highest indicators in the raw plant phytomass were noted for amino acids: glutamic, aspartic, leucine, proline, lysine and arginine 13,0, 10,7, 9,0, 8,6, 7,5 and 6,4% respectively.

PredAoDP: Accurate identification of antioxidant proteins by fusing different descriptors based on evolutionary information with support vector machine

Antioxidant proteins play a vital role in diseases prevention caused by free radical intermediates. Accurate identification of antioxidant proteins may provide significant clues to improve their fundamental understanding in the field of bioinformatics and pharmacology. Prediction of antioxidant protein is meaningful and a challenging task. In this study, we develop a novel predictor called PredAoDP based on two different descriptors, which incorporates salient evolutionary profile features with support vector machine (SVM), to predict antioxidant proteins. The evolutionary information in the PSI-BLAST profile is encoded and transformed into a series of fixed-length feature vectors by extracting 20-D, amino acid composition and 400-D bigram features from position-specific scoring matrix (PSSM). As a result, a feature vector of 420-dimensional (420-D) feature vector via serial combination was constructed from two training datasets of antioxidant protein Z1 and Z2. The descriptors are then fed to the SVM for classification along with jack-knife cross-validation test method for evaluating the prediction performance. Our proposed intelligent predictor achieved ACC, 93.18% and MCC, 0.712 for Z1 similarly for Z2 datasets ACC, 97.89% and MCC, 0.949 by jack-knife cross-validation test. To evaluate the generalization abilities of the developed method, we performed an independent test and achieved superior performance compared the other published approaches. The experimental results revealed the effectiveness of the proposed approach and can be utilized as a reliable tool for predicting large-scale antioxidant proteins in particular and other proteins in general. The source code and benchmark datasets are available at https://github.com/saeed344/PreAoDp.

Disordered-Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.

Intrinsically disordered proteins lack a stable tertiary structure and form dynamic conformational ensembles due to their characteristic physicochemical properties and amino acid composition. They are abundant in nature and responsible for a large variety of cellular functions. While numerous bioinformatics tools have been developed for in silico disorder prediction in the last decades, there is a need for experimental methods to verify the disordered state. CD spectroscopy is widely used for protein secondary structure analysis. It is usable in a wide concentration range under various buffer conditions. Even without providing high-resolution information, it is especially useful when NMR, X-ray, or other techniques are problematic or one simply needs a fast technique to verify the structure of proteins. Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. The method needs CD data at only three wavelength points, making high-throughput data collection possible. The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. Moreover, the method can be used even for strong absorbing samples, such as the case of crowded environmental conditions, if the spectrum can be recorded down to the wavelength of 212 nm. We believe the classification method will be useful in identifying disorder and will also facilitate the growth of experimental data in IDP databases. The method is implemented on a webserver and freely available for academic users.

Mutational Analysis of Mycobacterial F-ATP Synthase Subunit δ Leads to a Potent δ Enzyme Inhibitor.

While many bacteria are able to bypass the requirement for oxidative phosphorylation when grown on carbohydrates, Mycobacterium tuberculosis is unable to do so. Differences of amino acid composition and structural features of the mycobacterial F-ATP synthase (α3:β3:γ:δ:ε:a:b:b':c9) compared to its prokaryotic or human counterparts were recently elucidated and paved avenues for the discovery of molecules interfering with various regulative mechanisms of this essential energy converter. In this context, the mycobacterial peripheral stalk subunit δ came into focus, which displays a unique N-terminal 111-amino acid extension. Here, mutants of recombinant mycobacterial subunit δ were characterized, revealing significant reduction in ATP synthesis and demonstrating essentiality of this subunit for effective catalysis. These results provided the basis for the generation of a four-feature model forming a δ receptor-based pharmacophore and to identify a potent subunit δ inhibitor DeMF1 via in silico screening. The successful targeting of the δ subunit demonstrates the potential to advance δ's flexible coupling as a new area for the development of F-ATP synthase inhibitors.

ISuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier

BackgroundLysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular mechanism of succinylation in depth, succinylation sites need to be accurately identified, and because experimental approaches are costly and time-consuming, there is a great demand for reliable computational methods. Feature extraction is a key step in building succinylation site prediction models, and the development of effective new features improves predictive accuracy. Because the number of false succinylation sites far exceeds that of true sites, traditional classifiers perform poorly, and designing a classifier to effectively handle highly imbalanced datasets has always been a challenge.ResultsA new computational method, iSuc-ChiDT, is proposed to identify succinylation sites in proteins. In iSuc-ChiDT, chi-square statistical difference table encoding is developed to extract positional features, and has a higher predictive accuracy and fewer features compared to common position-based encoding schemes such as binary encoding and physicochemical property encoding. Single amino acid and undirected pair-coupled amino acid composition features are supplemented to improve the fault tolerance for residue insertions and deletions. After feature selection by Chi-MIC-share algorithm, the chi-square decision table (ChiDT) classifier is constructed for imbalanced classification. With a training set of 4748:50,551(true: false sites), ChiDT clearly outperforms traditional classifiers in predictive accuracy, and runs fast. Using an independent testing set of experimentally identified succinylation sites, iSuc-ChiDT achieves a sensitivity of 70.47%, a specificity of 66.27%, a Matthews correlation coefficient of 0.205, and a global accuracy index Q9 of 0.683, showing a significant improvement in sensitivity and overall accuracy compared to PSuccE, Success, SuccinSite, and other existing succinylation site predictors.ConclusionsiSuc-ChiDT shows great promise in predicting succinylation sites and is expected to facilitate further experimental investigation of protein succinylation.

Research on protein solubility prediction based on ensemble learning and feature fusion

Protein solubility is one of the momentous properties of a protein that can effectively participate in and inhibit the physiological and biochemical processes of cancer cells in the human body. Therefore, understanding the solubility of proteins may be significant to find the mechanism of diseases caused by the solubility of proteins. In this paper, to improve the protein solubility prediction performance and address the inadequacy of existing protein solubility prediction methods that more feature information about protein sequences is difficult to be obtained. A protein solubility prediction model named EL-FFsol is proposed, which is based on the CatBoost ensemble learning framework and multiple feature fusion of protein sequences. First of all, protein sequence features were introduced to build fusion representation, including the Physicochemical Properties, One-hot Feature Encoding, Amino Acid Composition and Statistical Features. Additionally, the CatBoost was employed to construct an ensemble learning model to predict protein solubility. Finally, EL-FFsol was tested on the benchmark dataset to predict the solubility of proteins. In terms of accuracy, matthews correlation coefficient, sensitivity, specificity, area under ROC curve and area under P-R curve, EL-FFsol achieved 0.7679, 0.5480, 0.6630, 0.8729, 0.8540 and 0.8440 performances. Compared with the DeepSOL and DDcCNN, the matthews correlation coefficient was increased by 1.68% and 0.79%, the area under ROC curve was increased by 1.60% and 2.20% and the area under P-R curve was increased by 1.70% and 2.40%, respectively.

Multi-descriptor approaches to oxygen binding proteins prediction and classification using deep learning

Oxygen binding proteins play a key role in the transport and storage of oxygen through the body's cells. However, costly and time consuming biological tests can only determine a very small portion of all proteins available. This has made computational approaches increasingly essential to help biologists. The key idea behind this work is to investigate the effect of using several descriptors and deep learning to achieve better prediction. Three kinds of descriptors to represent protein sequences are considered namely amino acid composition (AAC), dipeptide composition (DC) and conjoint triad feature (CTF). Firstly, we applied deep neural networks (DNN) to single descriptor and then to multi-descriptor by investigation of the combination of descriptors. Secondly, DNN was developed to classify the oxygen binding proteins into six different classes. The experimental results show that the proposed prediction models outperform the existing methods where we obtained a Matthews correlation coefficient (MCC) of 0.9677.

Antibacterial Gel Coatings Inspired by the Cryptic Function of a Mussel Byssal Peptide

Although the adhesive and cohesive nature of mussel byssal proteins have long served to inspire the design of materials embodying these properties, their characteristic amino acid compositions suggest that they might also serve to inspire an unrelated material function not yet associated with this class of protein. Herein, it is demonstrated that a peptide derived from mussel foot protein-5, a key protein in mussel adhesion, displays antibacterial properties, a yet unreported activity. This cryptic function serves as inspiration for the design of a new class of peptide-based antibacterial adhesive hydrogels prepared via self-assembly, which are active against drug-resistant Gram-positive bacteria. The gels exert two mechanisms of action, surface-contact membrane disruption and oxidative killing affected by material-produced H2 O2 . Detailed studies relating amino acid composition and sequence to material mechanical adhesion/cohesion and antibacterial activity affords the MIKA2 adhesive gel, a material with a superior activity that is shown to inhibit colonization of titanium implants in mice.

Protein transfer learning improves identification of heat shock protein families

Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitations. First, they relied heavily on amino acid composition features, which inevitably limited their prediction performance. Second, their prediction performance was overestimated because of the independent two-stage evaluations and train-test data redundancy. To overcome these limitations, we introduce two novel deep learning algorithms: (1) time-efficient DeepHSP and (2) high-performance DeeperHSP. We propose a convolutional neural network (CNN)-based DeepHSP that classifies both non-HSPs and six HSP families simultaneously. It outperforms state-of-the-art algorithms, despite taking 14–15 times less time for both training and inference. We further improve the performance of DeepHSP by taking advantage of protein transfer learning. While DeepHSP is trained on raw protein sequences, DeeperHSP is trained on top of pre-trained protein representations. Therefore, DeeperHSP remarkably outperforms state-of-the-art algorithms increasing F1 scores in both cross-validation and independent test experiments by 20% and 10%, respectively. We envision that the proposed algorithms can provide a proteome-wide prediction of HSPs and help in various downstream analyses for pathology and clinical research.

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

IPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.

Physicochemical, functional and structural properties of the major protein fractions extracted from Cordyceps militaris fruit body

The physicochemical and functional as well as structural properties of major protein fractions (albumin, globulin, glutelin) sequentially extracted in water, salt, alkaline solution respectively from Cordyceps militaris Minfu20 fruit body were investigated. The glutelin (43.11%, w/w) was the predominant protein component of C. militaris fruit body followed by albumin (36.47%) and globulin (17.94%). The three proteins extracted from different solvents showed different characteristics, which were related to the alternation of amino acid composition, surface hydrophobicity, and structural feature. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) showed that the albumin and globulin mainly consisted of polypeptides with size < 20 kDa. The glutelin showed serious staining on the lane which may have a relatively bigger molecular weight. Intrinsic fluorescence intensity (FI) suggested glutelin contained more unfolding conformations (highest FI) which were probably resulted in a better foaming capacity of 151% and emulsion formation with the smallest size oil droplets (10.410 µm). The protein fractions showed great nutritional quality since they satisfied all recommended essential amino acid allowances for adults of Food & Agriculture Organization (FAO)/World Health Organization (WHO). Therefore, Cordyceps militaris Minfu20 fruit body proteins have potential alternative renewable edible fungi (mushroom) protein and could be used effectively as a food ingredient to improve food nutrition and product diversification compared with plant proteins.

IPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC

Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspartate sites is critical, and its experimental identification can be expensive, time-consuming, and tedious. For this purpose, we propose iPhosD-PseAAC, a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues: (1) Benchmark dataset. (2) The feature extraction techniques such as pseudo amino acid composition (PseAAC), statistical moments, and position relative features. (3) For the classification, artificial neural network AAN will be used. (4) In this step, 10-fold cross-validation and self-consistency testing will be used for validation. For self-consistency testing, 100% Acc is achieved, whereas, for 10-fold crossvalidation 95.14% Acc, 95.58% Sn, 94.70% Sp and 0.95 MCC are observed. (5). The final step is the development of a user-friendly web server for the ease of users. Thus, the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.