Progresses in Predicting Post-translational Modification

Physicochemical and Functional Properties of Moringa Seed Protein Treated with Ultrasound

Functional and structural properties of Moringa protein concentrate (MPC), obtained from defatted Moringa oleifera seed, were investigated after treating it with an ultrasonic technique. For this purpose, dried M. oleifera seed powder was defatted and subjected to a simple protein precipitation method to generate a MPC with 73.2% protein contents. Then, a Box-Behnken design was applied to optimize the sonication treatment of MPC where ultrasound amplitude (20-80%), treatment time (5-25 min), and solute-to-solvent ratio (0.1-0.3 g/mL) were studied as factors that influence the protein solubility (PS), emulsion capacity (EC), and foaming capacity (FC) of MPC. The optimal conditions were amplitude of 58%, time of 18 min, and solute to solvent ratio of 0.18 g/mL. At these conditions, PS, EC, and FC were increased to 42, 33, and 73%, respectively, in comparison to untreated one. The structural modification by ultrasound was further confirmed by using Fourier transform infrared spectroscopy which illustrated the MPC modification through the changes in the peak width of amide-I band. Similarly, the intrinsic fluorescence spectral signature also showed a significant increase in the amino residues of MPC. In conclusion, the exposure of hydrophilic groups and the alteration of secondary and tertiary structures induced by ultrasonic treatment improved the functional characteristics of MPC.

Comparative analysis of adipokinetic hormones and their receptors in Blattodea reveals novel patterns of gene evolution

Adipokinetic hormone (AKH) is a neuropeptide produced in the insect corpora cardiaca that plays an essential role in mobilising carbohydrates and lipids from the fat body to the haemolymph. AKH acts by binding to a rhodopsin-like G protein-coupled receptor (GPCR), the adipokinetic hormone receptor (AKHR). In this study, we tackle AKH ligand and receptor gene evolution as well as the evolutionary origins of AKH gene paralogues from the order Blattodea (termites and cockroaches). Phylogenetic analyses of AKH precursor sequences point to an ancient AKH gene duplication event in the common ancestor of Blaberoidea, yielding a new group of putative decapeptides. In total, 16 different AKH peptides from 90 species were obtained. Two octapeptides and seven putatively novel decapeptides are predicted for the first time. AKH receptor sequences from 18 species, spanning solitary cockroaches and subsocial wood roaches as well as lower and higher termites, were subsequently acquired using classical molecular methods and in silico approaches employing transcriptomic data. Aligned AKHR open reading frames revealed 7 highly conserved transmembrane regions, a typical arrangement for GPCRs. Phylogenetic analyses based on AKHR sequences support accepted relationships among termite, subsocial (Cryptocercus spp.) and solitary cockroach lineages to a large extent, while putative post-translational modification sites do not greatly differ between solitary and subsocial roaches and social termites. Our study provides important information not only for AKH and AKHR functional research but also for further analyses interested in their development as potential candidates for biorational pest control agents against invasive termites and cockroaches.

Understanding the impact of structural modifications at the NNAT gene's post-translational acetylation site: in silico approach for predicting its drug-interaction role in anorexia nervosa

PURPOSE

Anorexia nervosa (AN) is a neuropsychological public health concern with a socially disabling routine and affects a person's healthy relationship with food. The role of the NNAT (Neuronatin) gene in AN is well established. The impact of mutation at the protein's post-translational modification (PTM) site has been exclusively associated with the worsening of the protein's biochemical dynamics.

METHODS

To understand the relationship between genotype and phenotype, it is essential to investigate the appropriate molecular stability of protein required for proper biological functioning. In this regard, we investigated the PTM-acetylation site of the NNAT gene in terms of 19 other specific amino acid probabilities in place of wild type (WT) through various in silico algorithms. Based on the highest pathogenic impact computed through the consensus classifier tool, we generated 3 residue-specific (K59D, P, W) structurally modified 3D models of NNAT. These models were further tested through the AutoDock Vina tool to compute the molecular drug binding affinities and inhibition constant (Ki) of structural variants and WT 3D models.

RESULTS

With trained in silico machine learning algorithms and consensus classifier; the three structural modifications (K59D, P, W), which were also the most deleterious substitution at the acetylation site of the NNAT gene, showed the highest structural destabilization and decreased molecular flexibility. The validation and quality assessment of the 3D model of these structural modifications and WT were performed. They were further docked with drugs used to manage AN, it was found that the ΔGbind (kcal/mol) values and the inhibition constants (Ki) were relatively lower in structurally modified models as compared to WT.

CONCLUSION

We concluded that any future structural variation(s) at the PTM-acetylation site of the NNAT gene due to possible mutational consequences, will serve as a basis to explore its relationship with the propensity of developing AN.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles

The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence representation that is more memory and time-efficient than well-known k-mer sequence representation. Combining protein-protein interaction data, which is essential for numerous biological processes, with our novel distance-based subsequence profiles of mRNA sequences produces more accurate features. On two benchmark datasets, CeFra-Seq and RNALocate, the performance of NN-RNALoc is compared to powerful predictive models proposed in previous works (mRNALoc, RNATracker, mLoc-mRNA, DM3Loc, iLoc-mRNA, and EL-RMLocNet), and a ground neural (DNN5-mer) network. Compared to the previous methods, NN-RNALoc significantly reduces computation time and also outperforms them in terms of accuracy. This study's source code and datasets are freely accessible at https://github.com/NeginBabaiha/NN-RNALoc.

Transformer-based deep learning for predicting protein properties in the life sciences

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.

Comparing proteome changes involved in biofilm formation by Streptococcus mutans after exposure to sucrose and starch

Streptococcus mutans is a main organism of tooth infections including tooth decay and periodontitis. The aim of this study was to assess the influence of sucrose and starch on biofilm formation and proteome profile of S. mutans ATCC 35668 strain. The biofilm formation was assessed by microtiter plating method. Changes in bacterial proteins after exposure to sucrose and starch carbohydrates were analyzed using matrix-assisted laser desorption/ionization mass spectrometry. The biofilm formation of S. mutans was increased to 391.76% in 1% sucrose concentration, 165.76% in 1% starch, and 264.27% in the 0.5% sucrose plus 0.5% starch in comparison to biofilm formation in the media without sugars. The abundance of glutamines, adenylate kinase, and 50S ribosomal protein L29 was increased under exposure to sucrose. Upregulation of lactate utilization protein C, 5-hydroxybenzimidazole synthase BzaA, and 50S ribosomal protein L16 was formed under starch exposure. Ribosome-recycling factor, peptide chain release factor 1, and peptide methionine sulfoxide reductase MsrB were upregulated under exposure to sucrose in combination with starch. The results demonstrated that the carbohydrates increase microbial pathogenicity. In addition, sucrose and starch carbohydrates can induce biofilm formation of S. mutans via various mechanisms such as changes in the expression of special proteins.

Phosphoproteome Analysis Using Two-Dimensional Electrophoresis Coupled with Chemical Dephosphorylation

Protein phosphorylation is a reversible post-translational modification (PTM) with major regulatory roles in many cellular processes. However, the analysis of phosphoproteins remains the most challenging barrier in the prevailing proteome research. Recent technological advances in two-dimensional electrophoresis (2-DE) coupled to mass spectrometry (MS) have enabled the identification, characterization, and quantification of protein phosphorylation on a global scale. Most research on phosphoproteins with 2-DE has been conducted using phosphostains. Nevertheless, low-abundant and low-phosphorylated phosphoproteins are not necessarily detected using phosphostains and/or MS. In this study, we report a comparative analysis of 2-DE phosphoproteome profiles using Pro-Q Diamond phosphoprotein stain (Pro-Q DPS) and chemical dephosphorylation of proteins with HF-P from longissimus thoracis (LT) muscle samples of the Rubia Gallega cattle breed. We found statistically significant differences in the number of identified phosphoproteins between methods. More specifically, we found a three-fold increase in phosphoprotein detection with the HF-P method. Unlike Pro-Q DPS, phosphoprotein spots with low volume and phosphorylation rate were identified by HF-P technique. This is the first approach to assess meat phosphoproteome maps using HF-P at a global scale. The results open a new window for 2-DE gel-based phosphoproteome analysis.

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites

Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.

Naringenin as a potential inhibitor of human cyclin-dependent kinase 6: Molecular and structural insights into anti-cancer therapeutics

Cancer is one of the major causes of global deaths and needs immediate therapeutic development. So far, several strategies have been undertaken to prevent cancer, including kinase targeting by small-molecule inhibitors. Cyclin dependent kinase 6 (CDK6) plays an essential role in cancer progression and development as its overexpression is associated with tumor development and progression. The present study demonstrated that Naringenin (NAG) binds strongly to CDK6 with a binding affinity of -7.51 kcal/mol. ATPase assay of CDK6 in the presence of NAG shows that it inhibits CDK6 with an IC₅₀ = 3.13 μM. Fluorescence and isothermal titration calorimetry studies demonstrated that NAG binds to CDK6 with the binding constant (K) values of 3.55 × 10⁶ M^-1 and 7.06 ± 2.70 × 10⁶ M^-1, respectively. The cell-based functional studies showed that NAG decreases the cell viability of human cancer cell lines, induces apoptosis, and reduces their colonization ability. Outcomes of the present in silico and in vitro studies highlighted the significance of NAG for the development of anti-cancer leads in terms of CDK6 inhibitors and provided future implications for combinatorial anti-cancer therapies.

Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

Using Chou's 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods

N⁶-methyladenine (m6A), a type of modification mostly affecting the downstream biological functions and determining the levels of gene expression, is mediated by the methylation of adenine in nucleic acids. It is also a key factor for influencing biological processes and has attracted attention as a target for treating diseases. Here, an ensemble predictor named as TL-Methy, was developed to identify m6A sites across the genome. TL-Methy is a 2-level machine learning method developed by combining the support vector machine model and multiple features extraction methods, including nucleic acid composition, di-nucleotide composition, tri-nucleotide composition, position-specific trinucleotide propensity, Bi-profile Bayes, binary encoding, and accumulated nucleotide frequency. For Homo sapiens, TL-Methy method reached the accuracy of 91.68% on jackknife test and of 92.23% on 10-fold cross validation test; For Mus musculus, TL-Methy method achieved the accuracy of 93.66% on jackknife test and of 97.07% on 10-fold cross validation test; For Saccharomyces cerevisiae, TL-Methy method obtained the accuracy of 81.57% on jackknife test and of 82.54% on 10-fold cross validation test; For rice genome, TL-Methy method achieved the accuracy of 91.87% on jackknife test and of 93.04% on 10-fold cross validation test. The results via these two test approaches demonstrated the robustness and practicality of our TL-Methy model. The TL-Methy model may be as a potential method for m6A site identification.Communicated by Ramaswamy H. Sarma.

Designed protein multimerization and polymerization for functionalization of proteins

Abstract

Multimeric and polymeric proteins are large biomacromolecules consisting of multiple protein molecules as their monomeric units, connected through covalent or non-covalent bonds. Genetic modification and post-translational modifications (PTMs) of proteins offer alternative strategies for designing and creating multimeric and polymeric proteins. Multimeric proteins are commonly prepared by genetic modification, whereas polymeric proteins are usually created through PTMs. There are two methods that can be applied to create polymeric proteins: self-assembly and crosslinking. Self-assembly offers a spontaneous reaction without a catalyst, while the crosslinking reaction offers some catalyst options, such as chemicals and enzymes. In addition, enzymes are excellent catalysts because they provide site-specificity, rapid reaction, mild reaction conditions, and activity and functionality maintenance of protein polymers. However, only a few enzymes are applicable for the preparation of protein polymers. Most of the other enzymes are effective only for protein conjugation or labeling. Here, we review novel and applicable strategies for the preparation of multimeric proteins through genetic modification and self-assembly. We then describe the formation of protein polymers through site-selective crosslinking reactions catalyzed by enzymes, crosslinking reactions of non-natural amino acids, and protein-peptide (SpyCatcher/SpyTag) interactions. Finally, we discuss the potential applications of these protein polymers.

Graphical abstract

Use Chou's 5-steps rule to identify protein post-translational modification and its linkage to secondary metabolism during the floral development of Lonicera japonica Thunb

Lonicera japonica Thunb. is widely used in traditional medicine systems of East Asian and attracts a large amount of studies on the biosynthesis of its active components. Currently, there is little understanding regarding the regulatory mechanisms behind the accumulation of secondary metabolites during its developmental stages. In this study, published transcriptomic and proteomic data were mined to evaluate potential linkage between protein modification and secondary metabolism during the floral development. Stronger correlations were observed between differentially expressed genes (DEGs) and their corresponding differentially abundant proteins (DAPs) in the comparison of juvenile bud stage (JBS)/third green stage (TGS) vs. silver flowering stage (SFS). Seventy-five and 76 cor-rDEGs and cor-rDAPs (CDDs) showed opposite trends at both transcriptional and translational levels when comparing their levels at JBS and TGS relative to those at SFS. CDDs were mainly involved in elements belonging to the protein metabolism and the TCA cycle. Protein-protein interaction analysis indicated that the interacting proteins in the major cluster were primarily involved in TCA cycle and protein metabolism. In the simple phenylpropanoids biosynthetic pathway of SFS, both phospho-2-dehydro-3-deoxyheptonate aldolase (PDA) and glutamate/aspartate-prephenate aminotransferase (AAT) were decreased at the protein level, but increased at the gene level. A confirmatory experiment indicated that protein ubiquitination and succinylation were more prominent during the early floral developmental stages, in correlation with simple phenylpropanoids accumulation. Taken together, those data indicates that phenylpropanoids metabolism and floral development are putatively regulated through the ubiquitination and succinylation modifications of PDA, AAT, and TCA cycle proteins in L. japonica.

Molecular Docking and Dynamics Simulation Analysis of Thymoquinone and Thymol Compounds from Nigella sativa L. that Inhibit Cag A and Vac A Oncoprotein of Helicobacter pylori: Probable Treatment of H. pylori Infections

BACKGROUND

Helicobacter pylori infection is accountable for most of the peptic ulcer and intestinal cancers. Due to the uprising resistance towards H. pylori infection through the present and common proton pump inhibitors regimens, the investigation of novel candidates is the inevitable issue. Medicinal plants have always been a source of lead compounds for drug discovery. The research of the related effective enzymes linked with this gram-negative bacterium is critical for the discovery of novel drug targets.

OBJECTIVE

The aim of the study is to identify the best candidate to evaluate the inhibitory effect of thymoquinone and thymol against H. pylori oncoproteins, Cag A and Vac A in comparison to the standard drug, metronidazole by using a computational approach.

MATERIALS AND METHODS

The targeted oncoproteins, Cag A and Vac A were retrieved from RCSB PDB. Lipinski's rule and ADMET toxicity profiling were carried out on the phytoconstituents of the N. sativa. The two compounds of N. sativa were further analyzed by molecular docking and MD simulation studies. The reported phytoconstituents, thymoquinone and thymol present in N. sativa were docked with H. pylori Cag A and Vac A oncoproteins. Structures of ligands were prepared using ChemDraw Ultra 10 software and then changed into their 3D PDB structures using Molinspiration followed by energy minimization by using software Discovery Studio client 2.5.

RESULTS

The docking results revealed the promising inhibitory potential of thymoquinone against Cag A and Vac A with docking energy of -5.81 kcal/mole and -3.61kcal/mole, respectively. On the contrary, the inhibitory potential of thymol against Cag A and Vac A in terms of docking energy was -5.37 kcal/mole and -3.94kcal/mole as compared to the standard drug, metronidazole having docking energy of -4.87 kcal/mole and -3.20 kcal/mole, respectively. Further, molecular dynamic simulations were conducted for 5ns for optimization, flexibility prediction, and determination of folded Cag A and Vac A oncoproteins stability. The Cag A and Vac A oncoproteins-TQ complexes were found to be quite stable with the root mean square deviation value of 0.2nm.

CONCLUSION

The computational approaches suggested that thymoquinone and thymol may play an effective pharmacological role to treat H. pylori infection. Hence, it could be summarized that the ligands thymoquinone and thymol bound and interacted well with the proteins Cag A and Vac A as compared to the ligand MTZ. Our study showed that all lead compounds had good interaction with Cag A and Vac A proteins and suggested them to be a useful target to inhibit H. pylori infection.

Protein analysis and stability: Overcoming trial-and-error by grouping according to physicochemical properties

Today proteins are possibly the most important class of substances. Yet new tasks for proteins are still often solved by trial-and-error approaches. However, in some areas these euphemistically called "screening approaches" are not suitable. E.g. stability tests just take too long and therefore require a more strategic, target-orientated concept. This concept is available by grouping proteins according to their physicochemical properties and then pulling out the right drawer for new tasks. These properties include size, then charge and hydrophobicity as well as their patchinesses, and the degree of order. In addition, solubility, the content of (free) enthalpy, aromatic-amino-acid- and α/β-frequency as well as helix capping, and corresponding patchiness, the number of specific motifs and domains as well as the typical concentration range can be helpful to discriminate between different groups of proteins. Analyzing correlations will reduce the necessary amount of parameters and additional ones, which may be still undiscovered at the present time, can be identified looking at protein subgroups with similar physicochemical properties which still behave heterogeneously. Step-by-step the methodology will be improved. Possibly protein stability will be the driver of this process, but all other areas such as production, purification and analytics including sample pre-treatment and the choice of appropriate separation conditions for e.g. chromatography and electrophoresis will profit from a rational strategy.

A comprehensive review of the imbalance classification of protein post-translational modifications

Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.

Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature

It is well known that DNA-protein binding (DPB) prediction is not only beneficial to understand the regulation mechanism of gene expression but also a challenging task in the field of computational biology. Traditional methods for DPB prediction that depend on manually extracted features may lead to classification errors. Recently, deep learning such as convolutional neural network (CNN) has been successfully applied to classification tasks and improved DPB prediction performance significantly. Yet, these methods are based on the original DNA sequence modeling, ignoring the hidden complex dependency and complementarity between multiple sequence features. In consideration of this problem, we propose a method to fuse different sequence features and analyze them systematically through multi-scale CNN. First, sliding windows of specified lengths are set on distinct DNA sequences to generate multiple sequence features with unequal lengths. Second, multiple feature sequences are fused and encoded for feature representation. Third, multi-scale CNN with different binding motif lengths is used to automatically learn and mine the influence of internal attributes and hidden complex relations between the fusion sequence features and make full use of the complementary advantages of extracted CNN features to predict DPB. When our model is applied to 690 ChIP-seq datasets, it achieves an average AUC of 0.9112, which is significantly better than the latest methods. The results show that our method is effective for DPB prediction and is freely available at http://121.5.71.120/mscDPB/.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

OBJECTIVE

One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

METHODS

To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

RESULTS AND CONCLUSION

It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

The Cooperative Effect between Polybasic Region (PBR) and Polysialyltransferase Domain (PSTD) within Tumor-Target Polysialyltranseferase ST8Sia II

ST8Sia II (STX) is a highly homologous mammalian polysialyltransferase (polyST), which is a validated tumor-target in the treatment of cancer metastasis reliant on tumor cell polysialylation. PolyST catalyzes the synthesis of α2,8-polysialic acid (polySia) glycans by carrying out the activated CMP-Neu5Ac (Sia) to N- and O-linked oligosaccharide chains on acceptor glycoproteins. In this review article, we summarized the recent studies about intrinsic correlation of two polybasic domains, Polysialyltransferase domain (PSTD) and Polybasic region (PBR) within ST8Sia II molecule, and suggested that the critical amino acid residues within the PSTD and PBR motifs of ST8Sia II for polysialylation of Neural cell adhesion molecules (NCAM) are related to ST8Sia II activity. In addition, the conformational changes of the PSTD domain due to point mutations in the PBR or PSTD domain verified an intramolecular interaction between the PBR and the PSTD. These findings have been incorporated into Zhou's NCAM polysialylation/cell migration model, which will provide new perspectives on drug research and development related to the tumor-target ST8Sia II.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

Using Chou’s 5-Step Rule to Evaluate the Stability of Tautomers: Susceptibility of 2-[(Phenylimino)-methyl]-cyclohexane-1,3-diones to Tautomerization Based on the Calculated Gibbs Free Energies

Gibbs free energies, based on DFT (Density Functional Theory) calculations, prove that enaminone (2-(anilinemethylidene)cyclohexane-1,3-dione) and ketamine (2-[(phenylimino)-methyl]cyclohexane-1,3-dione) are the most and least stable tautomeric forms of the studied systems, respectively. 1H and 13C NMR spectra prove that 2-(anilinemethylidene)cyclohexane-1,3-diones are the only tautomeric species present in dimethylsulfoxide solution (a very weak signal can be seen only for the p-methoxy derivatives). The zwitterionic character of these enaminones is strengthened by naphthoannulation and by the insertion of the electron-withdrawing substituent into the benzene ring (the latter weakens the intramolecular hydrogen bond in the compound). Substituent and naphtoannulation have no effect on the stability of the studied tautomers. Slight twisting of the benzene ring, with respect to the CArNC plane (seen in the crystalline state), was proven to also take place in vacuum and in solution.

Using Chou's General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou's Five-Step Rule

Laccases are a group of enzymes with a critical activity in the degradation process of both phenolic and non-phenolic compounds. These enzymes present in a diverse array of species, including fungi and bacteria. Since this enzyme is in the market for different usages from industry to medicine, having a better knowledge of its structures and properties from diverse sources will be useful to select the most appropriate candidate for different purposes. In the current study, sequence- and structure-based characteristics of these enzymes from fungi and bacteria, including pseudo amino acid composition (PseAAC), physicochemical characteristics, and their secondary structures, are being compared and classified. Autodock 4 software was used for docking analysis between these laccases and some phenolic and non-phenolic compounds. The results indicated that features including molecular weight, aliphatic, extinction coefficient, and random coil percentage of these protein groups present high degrees of diversity in most cases. Categorization of these enzymes by the notion of PseAAC, showed over 96% accuracy. The binding free energy between fungal laccases and their substrates showed to be considerably higher than those of bacterial ones. According to the outcomes of the current study, data mining methods by using different machine learning algorithms, especially neural networks, could provide valuable information for a fair comparison between fungal and bacterial laccases. These results also suggested an association between efficacy and physicochemical features of laccase enzymes from different sources.

Insights into Lignan Composition and Biosynthesis in Stinging Nettle (Urtica dioica L.)

Stinging nettle (Urtica dioica L.) has been used as herbal medicine to treat various ailments since ancient times. The biological activity of nettle is chiefly attributed to a large group of phenylpropanoid dimers, namely lignans. Despite the pharmacological importance of nettle lignans, there are no studies addressing lignan biosynthesis in this plant. We herein identified 14 genes encoding dirigent proteins (UdDIRs) and 3 pinoresinol-lariciresinol reductase genes (UdPLRs) in nettle, which are two gene families known to be associated with lignan biosynthesis. Expression profiling of these genes on different organs/tissues revealed a specific expression pattern. Particularly, UdDIR7, 12 and 13 displayed a remarkable high expression in the top internode, fibre tissues of bottom internodes and roots, respectively. The relatively high expression of UdPLR1 and UdPLR2 in the young internodes, core tissue of bottom internode and roots is consistent with the high accumulation of lariciresinol and secoisolariciresinol in these tissues. Lignan quantification showed a high abundance of pinoresinol in roots and pinoresinol diglucosides in young internodes and leaves. This study sheds light on lignan composition and biosynthesis in nettle, providing a good basis for further functional analysis of DIRs and PLRs and, ultimately, engineering lignan metabolism in planta and in cell cultures.

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively.