Chou's 5-steps Rule Research Articles

Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule

Prostate cancer is one of the leading causes of death in men worldwide, revealing a substantial heterogeneity in terms of molecular and clinical behaviors. Tumor infiltrating immune cell is associated with prognosis and response to immunotherapy in several cancer types. However, until now, the immune infiltrate profile of distinct subtypes for prostate cancer remains poorly characterized. In this study, using immune infiltration profiles as well as transcriptomic datasets, we characterized this subtype of prostate tumors. We observed that the FLI1 subtype of prostate tumors was highly enriched in immune system processes, immune related KEGG pathways and biological processes. We also expanded this approach to explore the immune infiltration profile of the high FLI1 expression subtype for skin cutaneous melanoma, similar results were found. Investigation of the association of immune infiltration features with the FLI1 expression demonstrated that many important features were associated with the FLI1 expression.

Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule

DNase I hypersensitive sites (DHSs) are regarded as those regions of chromatin that are sensitive to cleavage by the DNase I enzyme. Identification of DNase I hypersensitive sites will provide useful insights for discovering DNA's functional elements from the non-coding sequences in the biomedical research. Because of the significance for DNase I hypersensitive sites, it is indispensable to develop an accurate, fast, robust, and high-throughput automated computational model. In this paper, we develop a model named iDHSs-MFF by combining multiple fusion features and F-score features selection approach. The multiple fusion features include three auto-correlation descriptors based on the dinucleotide property matrix and the trinucleotide property matrix (TPM), Pseudo-DPM and Pseudo-TPM. Evaluation by the jackknife cross-validation indicates that the selected features by F-score are effective in the identification of DNase I hypersensitive sites. Experimental results on two benchmark datasets demonstrate that the proposed model outperforms some highly related models. Systematic application of this computational approach will greatly facilitate the analysis of transcriptional regulatory elements. The datasets and Matlab source codes are freely available at: https://github.com/shengli0201/Datasets.

Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components

Lysine formylation is a newly discovered post-translational modification in histones, which plays a crucial role in epigenetics of chromatin function and DNA binding. In this study, a novel bioinformatics tool named CKSAAP_FormSite is proposed to predict lysine formylation sites. An effective feature extraction method, the composition of k-spaced amino acid pairs, is employed to encode formylation sites. Moreover, a biased support vector machine algorithm is proposed to solve the class imbalance problem in the prediction of formylation sites. As illustrated by 10-fold cross-validation, CKSAAP_FormSite achieves an satisfactory performance with an AUC of 0.8234. Therefore, CKSAAP_FormSite can be a useful bioinformatics tool for the prediction of formylation sites. Feature analysis shows that some amino acid pairs, such as ‘KA’, ‘SxxxxK’ and ‘SxxxA’ around formylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of formylation.

IRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components

Meiotic recombination plays an important role in the process of genetic diversity generation. “Hotspots” are regions that show a higher rate of meiotic recombination, while the regions with a lower rate are called “cold spots”. It has a great effect on the genome evolution via gene conversion or mutagenesis. According to recent research, recombination is present in uneven distribution across the genome. Many computational methods have been developed using secondary sequence information or physiochemical properties of nucleotide descriptor for the prediction of hotspots and cold spots, which are computationally cheap and fast in performance rather than web-lab experiments, but the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which conceal a very important predictive information. In this study, we have proposed a deep neural network to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features. Our deep learning algorithm leverage's deep dense architecture by showing its effectiveness over the state-of-the-art methods with a classification accuracy of 90.04%, sensitivity of 92.21%, specificity of 92.11% and area under the curve of 0.9801. Moreover, it is anticipated, that our model will provide novel insight into basic research, drug designing, academic research and recombination spots studies particularly. All the methodology and python-based source code is publicly available for the users at https://github.com/zaheerkhancs/irSpot_SPI along with publicly accessible web server using the proposed predictor.

IMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule.

Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming.Objective: Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites.Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing.Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing.Conclusion: It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.

ISulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components.

Background:The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.Methodology:In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing.Results:Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing.Conclusion:The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.

IDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule

DNA methylation is an elementary epigenetic process. The N6-methyladenine is related to a large kind of biological processes i.e., transcription, DNA replication, and repair. In genome, the N6-methyladenine (6 mA) site distribution is non-random; therefore, precise discrimination of 6 mA is necessary to understand its biological functions. Through biochemical experiments, the N6-methyladenine produced a positive outcome, still, these wet lab processes are very time consuming and high pricy. In view of this, it is of high priority to introduce a powerful, accurate, and fast computational model to identify N6-methyladenine sites. In this connection, we propose an intelligent computational model called iDNA6mA (5-step rule) using deep learning approach to identify N6-methyladenine sites from DNA sequences in the rice genome. Existing methods used handcrafted features to identify N6-methyladenine sites; however, the proposed computational model automatically extracts the key features from DNA input sequences via the proposed convolution neural network (CNN) model. The intelligent computational model iDNA6mA (5-step rule) obtained 86.64% of accuracy, 86.70% of sensitivity, 86.59% of specificity, 0.732 of MCC, and 0.931 of auROC. The results demonstrate that the proposed intelligent computational model achieved better performance in terms of all evaluation parameters than existing techniques. It is observed that iDNA6mA (5-step rule) model will become a useful tool in the fields of computational biology, bioinformatics, and for the academic research on N6-methyladenine sites prediction. A user-friendly webserver has been established and freely accessible at https://home.jbnu.ac.kr/NSCL/iDNA6mA.htm.

IMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule

Motor proteins are the driving force behind muscle contraction and are responsible for the active transportation of most proteins and vesicles in the cytoplasm. There are three superfamilies of cytoskeletal motor proteins with various molecular functions and structures: dynein, kinesin, and myosin. The functional loss of a specific motor protein molecular function has linked to a variety of human diseases, e.g., Charcot-Marie-Tooth disease, kidney disease. Therefore, creating a precise model to classify motor proteins is essential for helping biologists understand their molecular functions and design drug targets according to their impact on human diseases. Here we attempt to classify cytoskeleton motor proteins using deep learning, which has been increasingly and widely used to address numerous problems in a variety of fields resulting in state-of-the-art results. Our effective deep convolutional neural network is able to achieve an independent test accuracy of 97.5%, 96.4%, and 96.1% for each superfamily, respectively. Compared to other state-of-the-art methods, our approach showed a significant improvement in performance across a range of evaluation metrics. Through the proposed study, we provide an effective model for classifying motor proteins and a basis for further research that can enhance the performance of protein function classification using deep learning.

DForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

Formylation is a type of post-translational modification that can occur on lysine sites, which plays an irreplaceable role in organism. To better understand the mechanism, it is necessary to identify formylation sites in proteins accurately. Computational method is popular because of its more convenience and higher speed than traditional experimental methods. However, no computational method has been proposed for prediction of lysine formylation. In this study, we developed a predictor named LFPred to identify lysine formylation sites using sequence features (including amino acid composition (AAC), binary profile features (BPF), and amino acid index (AAI)) combined K-nearest neighbor algorithm as classifier. We chose discrete window instead of continuous window according to information entropy. Besides, we took measure to select more reliable negative samples and address the severe imbalance between positive samples and negative samples. Finally, the performance of LFPred is measured with a specificity of 79.9% and a sensibility of 81.4% using jackknife test method, which indicated that our method can be a useful tool for prediction of lysine formylation sites.

IEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

An enhancer is a short (50–1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory bowel disease. Due to the importance of enhancers in genomics, the classification of enhancers has become a popular area of research in computational biology. Despite the few computational tools employed to address this problem, their resulting performance still requires improvements. In this study, we treat enhancers by the word embeddings, including sub-word information of its biological words, which then serve as features to be fed into a support vector machine algorithm to classify them. We present iEnhancer-5Step, a web server containing two-layer classifiers to identify enhancers and their strength. We are able to attain an independent test accuracy of 79% and 63.5% in the two layers, respectively. Compared to current predictors on the same dataset, our proposed method is able to yield superior performance as compared to the other methods. Moreover, this study provides a basis for further research that can enrich the field of applying natural language processing techniques in biological sequences. iEnhancer-5Step is freely accessible via http://biologydeep.com/fastenc/.

INR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families

Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model “iNR-2 L” is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.

SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

The protein prenylation (or S-prenylation) is one of the most essential modifications, required for the association of membrane of a plethora of signalling proteins with the key biological process such as protein trafficking, cell growth, proliferation and differentiation. Due to the ubiquitous nature of S-prenylation and its role in cellular functions, any defect in the biosynthesis or regulation of the isoprenoid leads to the occurrence of a variety of diseases including neurodegenerative disorders, metabolic issues, cardiovascular diseases and one of the most fatal diseases, cancer. This depicts the strong biological significance of S-prenylation, thus, the timely and accurate identification of S-prenylation sites is crucial and may provide with possible ways to understand the mechanism of this modification in proteins. To avoid laborious, resource demanding and expensive experimental techniques of identifying S-prenylation sites, here, we propose a novel predictor namely SPrenylC-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. A 2-tier classification was performed i.e., at first level, identification of prenylation and non-prenylation sites is performed, while at the second level, identification of S-farnesylation and S-geranylgeranylation sites is performed. Using jackknife, perdition model validation gave 95.31% accuracy for tier-1 classification and 91.42% for tier 2 classification, while for 10-fold cross-validation, it gave 93.68% accuracy for tier-1 classification and 89.70% for tier 2 classification. Thus the proposed predictor can help in predicting the Prenylation sites in an efficient and accurate way. The SPrenylC-PseAAC is available at (biopred.org/prenyl).

SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins

S-Palmitoylation is a uniquely reversible and biologically important post-translational modification as it plays an essential role in a variety of cellular processes including signal transduction, protein-membrane interactions, neuronal development, lipid raft targeting, subcellular localization and apoptosis. Due to its association with the neuronal development, it plays a pivotal role in a variety of neurodegenerative diseases, mainly Alzheimer's, Schizophrenia and Huntington's disease. It is also essential for developmental life cycles and pathogenesis of Toxoplasma gondii and Plasmodium falciparum, known to cause toxoplasmosis and malaria, respectively. This depicts the strong biological significance of S-Palmitoylation, thus, the timely and accurate identification of S-palmitoylation sites is crucial. Herein, we propose a predictor for S-Palmitoylation sites in proteins namely SPalmitoylC-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. Self-consistency testing and 10-fold cross-validation are performed to evaluate the performance of SPalmitoylC-PseAAC, using accuracy metrics. For self-consistency testing, 99.79% Acc, 99.77% Sp, 99.80% Sn and 1.00 MCC was observed, whereas, for 10-fold cross validation 97.22% Acc, 98.85% Sp, 95.80% Sn and 0.94 MCC was observed. Thus the proposed predictor can help in predicting the palmitoylation sites in an efficient and accurate way. The SPalmitoylC-PseAAC is available at (biopred.org/palm).

IPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Protein phosphorylation is one of the most fundamental types of post-translational modifications and it plays a vital role in various cellular processes of eukaryotes. Among three types of phosphorylation i.e. serine, threonine and tyrosine phosphorylation, tyrosine phosphorylation is one of the most frequent and it is important for mediation of signal transduction in eukaryotic cells. Site-directed mutagenesis and mass spectrometry help in the experimental determination of cellular signalling networks, however, these techniques are costly, time taking and labour associated. Thus, efficient and accurate prediction of these sites through computational approaches can be beneficial to reduce cost and time. Here, we present a more accurate and efficient sequence-based computational method for prediction of phosphotyrosine (PhosY) sites by incorporation of statistical moments into PseAAC. The study is carried out based on Chou's 5-step rule, and various position-composition relative features are used to train a neural network for the prediction purpose. Validation of results through Jackknife testing is performed to validate the results of the proposed prediction method. Overall accuracy validated through Jackknife testing was calculated 93.9%. These results suggest that the proposed prediction model can play a fundamental role in the prediction of PhosY sites in an accurate and efficient way.

2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.

Involved with important cellular or gene functions and implicated with many kinds of cancers, piRNAs, or piwi-interacting RNAs, are of small non-coding RNA with around 19–33 nt in length. Given a small non-coding RNA molecule, can we predict whether it is of piRNA according to its sequence information alone? Furthermore, there are two types of piRNA: one has the function of instructing target mRNA deadenylation, and the other does not. Can we discriminate one from the other? With the avalanche of RNA sequences emerging in the postgenomic age, it is urgent to address the two problems for both basic research and drug development. Unfortunately, to the best of our knowledge, so far no computational methods whatsoever could be used to deal with the second problem, let alone deal with the two problems together. Here, by incorporating the physicochemical properties of nucleotides into the pseudo K-tuple nucleotide composition (PseKNC), we proposed a powerful predictor called 2L-piRNA. It is a two-layer ensemble classifier, in which the first layer is for identifying whether a query RNA molecule is piRNA or non-piRNA, and the second layer for identifying whether a piRNA is with or without the function of instructing target mRNA deadenylation. Rigorous cross-validations have indicated that the success rates achieved by the proposed predictor are quite high. For the convenience of most biologists and drug development scientists, the web server for 2L-piRNA has been established at http://bioinformatics.hitsz.edu.cn/2L-piRNA/, by which users can easily get their desired results without the need to go through the mathematical details.

Recent Progresses in Studying Helix-Helix Interactions in Proteins by Incorporating the Wenxiang Diagram into the NMR Spectroscopy.

All residues in an alpha helix can be characterized and dispositioned on a 2D the wenxiang diagram, which possesses the following features: (1) the relative locations of the amino acids in the α-helix can be clearly displayed regardless how long it is; (2) direction of an alphahelix can be indicated; and (3) more information regarding each of the constituent amino acid residues in an alpha helix. Owing to its intuitionism and easy visibility, wenxiang diagrams have had an immense influence on our understanding of protein structure, protein-protein interactions, and the effect of helical structural stability on protein conformational transitions. In this review, we summarize two recent applications of wenxiang diagrams incorporating NMR spectroscopy in the researches of the coiled-coil protein interactions related to the regulation of contraction or relaxation states of vascular smooth muscle cells, and the effects of α-helical stability on the protein misfolding in prion disease, in hopes that the gained valuable information through these studies can stimulate more and more widely applications of wenxiang diagrams in structural biology.

Recent development of peptide drugs and advance on theory and methodology of peptide inhibitor design.

Due to the low toxicity, easy synthesis, rapid elimination, and less side effect, more and more peptide inhibitors are emerging as the effective drugs that are clinically used in therapies of a number of diseases. At the same time the computer-aided drug design (CADD) methods have remarkably developed. In this mini review the newly developed peptide inhibitors and drugs are introduced, including peptide vaccines for cancers, peptide inhibitors for HIV, Alzheimer's disease and related diseases, and the peptides as the leading compounds of drugs. The recent progress in the theory and methodology of peptide inhibitor design is reviewed. (1) The flexible protein-peptide docking model is introduced, in which the peptide structures are treated as segment-flexible chains using genetic algorithm and special force field parameters. (2) The "Wenxiang diagram" is illustrated for protein-peptide interaction analysis that has been successfully used in the coiled-coil interaction analysis. (3) The "Distorted key" theory is reviewed, which is an effective method to convert the peptide inhibitors to the small chemical drugs. (4) The amino acid property-based peptide prediction method (AABPP) is described that is a twolevel QSAR prediction network for the bioactivity prediction of peptide inhibitors. (5) Finally, several types of molecular interactions between protein and peptide ligands are summarized, including cation-π interactions; polar hydrogen-π interactions; and π-π stocking interactions.

Impacts of bioinformatics to medicinal chemistry.

Facing the explosive growth of biological sequence data, such as those of protein/peptide and DNA/RNA, generated in the post-genomic age, many bioinformatical and mathematical approaches as well as physicochemical concepts have been introduced to timely derive useful informations from these biological sequences, in order to stimulate the development of medical science and drug design. Meanwhile, because of the rapid penetrations from these disciplines, medicinal chemistry is currently undergoing an unprecedented revolution. In this minireview, we are to summarize the progresses by focusing on the following six aspects. (1) Use the pseudo amino acid composition or PseAAC to predict various attributes of protein/peptide sequences that are useful for drug development. (2) Use pseudo oligonucleotide composition or PseKNC to do the same for DNA/RNA sequences. (3) Introduce the multi-label approach to study those systems where the constituent elements bear multiple characters and functions. (4) Utilize the graphical rules and "wenxiang" diagrams to analyze complicated biomedical systems. (5) Recent development in identifying the interactions of drugs with its various types of target proteins in cellular networking. (6) Distorted key theory and its application in developing peptide drugs.

The pH-Triggered Conversion of the PrP<sup>c</sup> to PrP<sup>sc</sup>

Transmissible spongiform encephalopathies (TSEs) are prion protein misfolding diseases that involve the accumulation of an abnormal β-sheet-rich prion protein aggregated form (PrP(sc)) of the normal α- helix-rich prion protein (PrP(c)) within the central nervous system (CNS) and other organs. On account of its large size and insolubility properties, characterization of PrP(c) is quite difficult. A soluble intermediate, called PrP(β) or β(o), exhibiting many of the same features as PrP(sc), can be generated using a combination of low pH and/or mild denaturing conditions. Here, we review the current knowledge on the following five issues relevant to the conversion mechanisms of PrP(c) to PrP(sc) : (1) How is the Stability of the Helical Structures in the Native PrP(c) Related to the Primary Structure of the PrP(c) (2) Why the Low pH Solution System is a Ideal Trigger of PrP(c) to PrP(sc) Conversion (3) How are the Structural and Dynamical Characteristics of the α-helixrich Intermediates Determined using NMR Data (4) How are the Premolten (PrP(α4) and PrP(αβ)) and β-Oligomer (PrP(β)) Intermediates Detected and Assayed, and (5) Can the Disordered N-terminal Domain be folded into the Structural Segment? Particularly, Chou's wenxiang diagram (http://en.wikipedia.org/wiki/Wenxiang_diagram) was introduced for providing an intuitive picture. This review may help to further understand the prion protein misfolding mechanism.

The Structural Determinations of the Leucine Zipper Coiled-Coil Domains of the cGMP-Dependent Protein Kinase Iα and Its Interaction with the Myosin Binding Subunit of the Myosin Light Chains Phosphase

Physiologic relaxation of vascular smooth muscle is induced by the cyclic guanosine monophosphate (cGMP)- dependent protein kinase Iα enzyme (cGKIα), which activates myosin phosphatase (MLCP). This activation process is thought to occur through the interaction involving both N- and C-terminal leucine zipper coiled-coil (LZCC) domains of the kinase enzyme (cGKIα) with the myosin binding subunit (MBS) of MLCP. In this review, I summarize how to define the LZCC domains in both N-terminal cGKIα(1-59) and C-terminal MBS proteins using predictive and experimental methods, how to make a rapid and accurate structure determination of a cGKIα(1-59) molecule using NMR's residual dipolar coupling (RDC) measurements, and how to indentify the existence of a weak protein interaction between N-terminal LZCC domain (cGKIα(1-59)) and a LZCC domain (MBSCT42) within the C-terminal MBS. In addition, the location and orientation of the residues in LZCC proteins can be readily visualized using a novel diagram, the so-called "wenxiang diagram", which is more advantageous than traditional helical wheel diagrams in analyzing LZCC protein structures and their action mechanisms. Using the composed wenxiang diagrams, we have characterized the interaction between cGKIα(1- 59) and another LZCC molecule (MBSCT42), and deduced that the most affected residues of these two LZCC molecules might be at the positions d, a, e and g. These studies and findings are also covered in this review. It is intriguing to see that the successful incorporation of wenxiang diagrams and NMR spectroscopy in the LZCC structural and functional studies may provide some insights into protein-protein interaction mechanisms.