Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis

Detecting remote homology proteins is a challenging problem for both basic research and drug development. Although there are a couple of methods to deal with this problem, the benchmark datasets based on which the existing methods were trained and tested contain many high homologous samples as reflected by the fact that the cutoff threshold was set at 95%. In this study, we reconstructed the benchmark dataset by setting the threshold at 40%, meaning none of the proteins included in the benchmark dataset has more than 40% pairwise sequence identity with any other in the same subset. Using the new benchmark dataset, we proposed a new predictor called “dRHP-GreyFun” based on the grey modeling and functional domain approach. Rigorous cross-validations have indicated that the new predictor is superior to its counterparts in both enhancing success rates and reducing computational cost. The predictor can be downloaded from https://github.com/jcilwz/dRHP-GreyFun.

The Significant and Profound Impacts of Chou’s Distorted Key Theory for Developing Peptide Drugs

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.

The Significant and Profound Impacts of Chou’s Invariance Theorem

In this short review paper, the significant and profound impacts of the Chou’s “invariance theorem” have been briefly presented with crystal clear convincingness.

The Significant and Profound Impacts of Chou’s Pseudo Amino Acid Composition or PseAAC

In this short review paper, the significant and profound impacts of the Pseudo Amino Acid Composition or PseAAC have been briefly presented with crystal clear convincingness.

The Significant and Profound Impacts of Chou’s 5-Steps Rule

In this short review paper, the significant and profound impacts of the 5-steps rule have been briefly recalled with crystal clear convincingness.

Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms

Cell-PLoc 2.0 is a package of web-servers evolved from Cell-PLoc (Chou, K.C. & Shen, H.B., Nature Protocols, 2008, 2:153-162) by a top-down approach to improve the power for predicting subcellular localization of proteins in various organisms. It contains six predictors: Euk-mPLoc 2.0, Hum-mPLoc 2.0, Plant-mPLoc, Gpos-mPLoc, Gneg-mPLoc, and Virus-mPLoc, specialized for eukaryotic, human, plant, Gram- positive bacterial, Gram-negative bacterial, and virus proteins, respectively. Compared with Cell-PLoc, the predictors in the Cell-PLoc 2.0 have the following advantageous features: (1) they all have the capacity to deal with the multiplex proteins that can simultaneiously exist, or move between, two or more subcellular location sites;(2) no accession number is needed for the input of a query protein even if using the “high- level” GO (gene ontology) prediction engine;(3) the functional domain information and sequential evolution information are fused into the “ab initio” sequence-based prediction engine to enhance its accuracy. In this protocol, a step- to-step guide is provided for how to use the web server predictors in the Cell-PLoc 2.0 package, which is freely accessible to the public at http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/.

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

Pse-in-One 2.0 is a package of web-servers evolved from Pse-in-One (Liu, B., Liu, F., Wang, X., Chen, J. Fang, L. & Chou, K.C. Nucleic Acids Research, 2015, 43:W65-W71). In order to make it more flexible and comprehensive as suggested by many users, the updated package has incorporated 23 new pseudo component modes as well as a series of new feature analysis approaches. It is available at http://bioinformatics.hitsz.edu.cn/Pse-in-One2.0/. Moreover, to maximize the convenience of users, provided is also the stand-alone version called “Pse-in-One-Analysis”, by which users can significantly speed up the analysis of massive sequences.

REVIEW : Recent advances in developing web-servers for predicting protein attributes

Recent advance in large-scale genome se-quencing has generated a huge volume of pro-tein sequences. In order to timely utilize the in-formation hidden in these newly discovered sequences, it is highly desired to develop com- putational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a sy- stematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

Prediction of protein folding rates from primary sequence by fusing multiple sequential features

We have developed a web-server for predicting the folding rate of a protein based on its amino acid sequence information alone. The web- server is called Pred-PFR (Predicting Protein Folding Rate). Pred-PFR is featured by fusing multiple individual predictors, each of which is established based on one special feature derived from the protein sequence. The ensemble pre-dictor thus formed is superior to the individual ones, as demonstrated by achieving higher correlation coefficient and lower root mean square deviation between the predicted and observed results when examined by the jack-knife cross-validation on a benchmark dataset constructed recently. As a user-friendly web- server, Pred-PFR is freely accessible to the public at www.csbio.sjtu.edu.cn/bioinf/Folding Rate/.

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

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 virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” 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 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. 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-mVirus/.

pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning

Recently, the life of worldwide human beings has been endangering by the spreading of pneu- monia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep- learning” technique and developed a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/, by which the majority of experimental scientists can easily get their desired data.

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

Gordon Life Science Institute is the first Internet Research Institute ever established in the world. It is a non-profit institute. Those scientists who really dedicate themselves to science and loving science more than anything else can become its member. In the friendly door-opened Institute, they can maximize their time and energy to engage in their scientific creativity. They have also believed that science would be more truthful and wonderful if scientists do not have to spend a lot of time on funding application, and that great scientific findings and creations in history were often made by those who were least supported or funded but driven by interesting imagination and curiosity. Recollected in this review article is its establishing and developing processes, as well as its philosophy and accomplishments. Particularly, its productive and by-productive outcomes have covered the following five very hot topics in bioinformatics and drug development: 1) PseAAC and PseKNC;2) Disported key theory;3) Wenxiang diagram;4) Multi-label system prediction;5) 5-steps rule. Their impacts on the proteomics and genomics as well as drug development are substantially and awesome.

pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

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.

pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of animal protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based animal protein subcellular localization predictor called “pLoc_Deep-mAnimal” 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 92% and its local accuracy is over 95%. Both have substantially exceeded the other existing state-of-the-art predictors. 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-mAnimal/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” 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 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. 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-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.

Wenxiang: a web-server for drawing wenxiang diagrams

The wenxiang diagram was proposed to represent α-helices in a 2D (two dimensional) space (Chou, K.C., Zhang, C.T., Maggiora, G.M. Proteins: Struct., Funct., Genet., 1997, 28, 99-108). It has the capacity to provide more information in a 2D plane about each of the constituent amino acid residues in an α-helix, and is particularly useful for studying and analyzing amphiphilic helices. To meet the increasing requests for getting the program of generating wenxiang diagrams, a user-friendly web-server called “Wenxiang” has been established. It is accessible to the public at the web-site http://www.jci-bioinfo.cn/wenxiang2 or http://icpr.jci.edu.cn/bioinfo/wenxiang2. Further- more, for the convenience of users, here we provide a step-to-step guide for how to use the Wenxiang web-server to generate the desired wenxiang diagrams.

The Implication of “I Am the Alpha and the Omega” to Internet Institutes

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

Showcase to Illustrate How the Web-Server pLoc_Deep-mHum Is Working

Recently, a very useful method called “pLoc_Deep-mHum” has been proposed for finding against the Pandemic COVID-19. Illustrated in this short report is a step-by-step guide for how to use its web-server.