In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named "DNAPred_Prot", which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

In this review paper, the remarkable impacts of the first Internet Institute, the Gordon Life Science Institute, as well as its profound and far-reaching influence have been systematically and comprehensively presented.

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

In this short review paper, the significant and profound impacts of the protein subcellular prediction have been briefly presented with crystal clear convincingness.

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

It is\r\nextremely fearful for the pestilences covering our Earth. Does that mean the\r\n“World End” is around the corner? For the so-called “Atheists” originally\r\nproposed by Karl Max and Friedrich Engels, “there is a Beginning, there must be\r\nan End”, meaning our Earth will finally no longer exist in the entire Universe\r\nby colliding with the other planet. According to Holly Bible, however, Jesus,\r\nwill send out his angels to separate the wicked from the righteous and throw\r\nthe former into the fiery furnace. For such a special time-period, many useful\r\nideas or outcomes can be acquired by the Internet Institutes.

In this short review paper, the significant and profound impacts of the distorted key theory for developing peptide drugs have been briefly recalled with crystal clear convincingness.As a culprit of AIDS [1], HIV protease has been a target for developing drugs against AIDS [2].Functioning as a dimmer of two identical subunits, HIV protease has a crab-like shape.Its catalytic cleft is gated by a pair of flaps (or pincers if viewed as a crab).When the enzyme is in an inhibitor-free state, the pincer-gate is open, allowing substrates to enter the catalytic cleft; when in an inhibitor-binding state, the pincer-gate is closed, blocking the entrance [3].As a member of the aspartyl proteases, HIV protease is highly substrate-selective and cleavage-specific.Its susceptible sites in a protein extend to an octapeptide region [4].Knowledge of the protein cleavage sites by HIV protease can provide very useful information for finding effective inhibitors against the culprit enzyme, as elaborated by Kuo-Chen Chou in [5].According to Fisher's lock-and-key model proposed by Hermann Emil Fischer in 1984 and Koshland's induced fit theory by Daniel E. Koshland, Jr. in 1958, given a peptide, the prerequisite condition for it to be cleaved by HIV-protease is a good fit and binding between the substrate and the enzyme's active site.However, such a peptide, after a modification on its scissile bond with some simple chemical procedure, will completely lose its cleavability but it can still tightly bind to the enzyme's active site.According to Kuo-Chen Chou [3], the molecule thus modified can be likened to a "distorted key", which can be inserted into a lock but can neither open the lock nor automatically get out from it.That is why a molecule modified from a cleavable peptide can spontaneously become a competitive inhibitor against the enzyme [6].Even for non-peptide inhibitors, the information derived from the cleavable peptides can also provide useful insights about the key binding groups and fitting conformation, among many other detailed requirements in microenvironment.Many efforts have been made to predict the protein cleavage sites by HIV-protease (see, e.g., [6][7][8]).Also, a webserver named HIVcleave was established [9] for predicting HIV protease cleavage sites in proteins.

Kuo Chen Chou* Author Affiliations Gordon Life Science Institute, United States of America Received: December 20, 2019 | Published: January 06, 2020 Corresponding author: Kuo Chen Chou, Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America DOI: 10.26717/BJSTR.2020.24.004016

About 38 years ago a very important paper with the title “Origin of the right-handed twist of beta-sheets of poly-L-valine chains...

The most unethical behaviors in science are of falsifying data and stealing ideas from previous investigators. But for publishing papers with high similarity and editing papers with coercion, it is necessary to carry out a concrete analysis case by case.

With the avalanche of biological sequences discovered in the postgenomic era, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, yet still keep its considerable sequence-order information or special pattern. To deal with this problem, the idea of "pseudo amino acid components" or "PseAAC" was proposed in 2001. In this paper, the author has recalled the proposal of "pseudo amino acid components" and its significant and substantial impacts on proteome and genome analyses as well as developing novel and effective drugs, particularly peptide drugs.

Introduction Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. Methods The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called “iHyd-LysSite (EPSV)” (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. Results The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. Conclusion The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

In this short review paper, the significant and profound impacts of the enzyme diffusion-controlled reactions have been briefly presented with crystal clear convincingness...

In this current minireview, the cradle of the “5-steps rule” or “5-step rules”, along with its essence and advances, has been recalled. Born in 2011, its impacts on molecular biology are both substantial and rapid, fully indicating the “5-steps rule” is no double a remarkable and profound milestone in molecular biology.

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

In 2016 a very powerful AI (artificial intelligence) tool has been established for predicting lysine succinylation sites in proteins, one of the most important post modifications in proteins.

About 10 years ago a very important paper on “Some remarks on protein attribute prediction and pseudo amino acid composition...