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 p...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.展开更多
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, kno...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.展开更多
<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span>...<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">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 <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>展开更多
<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human bei...<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human beings has been endangering by the spreading of </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">pneu</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">monia</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">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 need展开更多
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 ...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.展开更多
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...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.展开更多
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 proc...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.展开更多
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.
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.
A systematic introduction has been presented for the recent advances in predicting protein subcellular localization in the multi-label systems, where the constituent proteins may simultaneously occur or move between t...A systematic introduction has been presented for the recent advances in predicting protein subcellular localization in the multi-label systems, where the constituent proteins may simultaneously occur or move between two or more location sites and hence have exceptional biological functions worthy of our special notice. All the predictors included in this review each have a user-friendly web-server, by which the majority of experimental scientists can very easily acquire their desired data without the need to go through the complicated mathematics involved.展开更多
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 proc...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 positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” 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 99% and its local accuracy is around 92% - 99%. 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-mGpos/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.展开更多
Established in 2004, Gordon Life Science Institute is the first Internet Research Institute in the world. It is a non-profit institute, a gift to science. Those scientists, who are really loving science more than anyt...Established in 2004, Gordon Life Science Institute is the first Internet Research Institute in the world. It is a non-profit institute, a gift to science. Those scientists, who are really loving science more than anything else and have shown fantastic creativity in science, can become the membership of such Institute. Their driving force is not funding but firmly belief that scientists will do much better science if they do not have to spend a lot of time for funding application, and that great scientific findings in history were often discovered by those who were without funding at all but driven by profound imagination and curiosity. Summarized in this review are also the accomplishments of the Gordon Life Science Institute and its future perspective.展开更多
Using similarity software to examine the quality of scientific papers is a nuisance. The significance of a scientific paper should be decided by the acknowledged experts. The practice of using the computer program to ...Using similarity software to examine the quality of scientific papers is a nuisance. The significance of a scientific paper should be decided by the acknowledged experts. The practice of using the computer program to decide scientific papers must be rescinded or voided.展开更多
Recently, a very useful method called “pLoc_Deep-mEuk” 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.
Current coronavirus pandemic has endangered the entire mankind life. The reported cas-es are increasing exponentially. Information of protein post-translational modification (PTM) can provide useful clues to develop a...Current coronavirus pandemic has endangered the entire mankind life. The reported cas-es are increasing exponentially. Information of protein post-translational modification (PTM) can provide useful clues to develop antiviral drugs. According to our recent works, the PTM prediction can be significantly improved by widening the samples of training da-taset. Based on such an idea, a new predictor called “iSulf_Wide-PseAAC” has been de-veloped. Its accuracy is overwhelmingly higher than its counterparts. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new pre-dictor has been established at http://121.36.221.79/Isulf_Pse/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.展开更多
Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspe...Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.展开更多
Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of ...Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.展开更多
Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as...Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.展开更多
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the D...Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the DNA alongside transcription features and initiates the transcription of correct response genes.Thus,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and tedious.For this purpose,we propose iPhosD-PseAAC,a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues:(1)Benchmark dataset.(2)The feature extraction techniques such as pseudo amino acid composition(PseAAC),statistical moments,and position relative features.(3)For the classification,artificial neural network AAN will be used.(4)In this step,10-fold cross-validation and self-consistency testing will be used for validation.For self-consistency testing,100%Acc is achieved,whereas,for 10-fold crossvalidation 95.14%Acc,95.58%Sn,94.70%Sp and 0.95 MCC are observed.(5).The final step is the development of a user-friendly web server for the ease of users.Thus,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.展开更多
文摘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.
文摘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.
文摘<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">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 <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>
文摘<span style="font-family:Verdana;"> <p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">Recently, the life of worldwide human beings has been endangering by the spreading of </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">pneu</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">- </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">monia</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">-</span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective </span><span style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">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 need
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘A systematic introduction has been presented for the recent advances in predicting protein subcellular localization in the multi-label systems, where the constituent proteins may simultaneously occur or move between two or more location sites and hence have exceptional biological functions worthy of our special notice. All the predictors included in this review each have a user-friendly web-server, by which the majority of experimental scientists can very easily acquire their desired data without the need to go through the complicated mathematics involved.
文摘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 positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” 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 99% and its local accuracy is around 92% - 99%. 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-mGpos/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.
文摘Established in 2004, Gordon Life Science Institute is the first Internet Research Institute in the world. It is a non-profit institute, a gift to science. Those scientists, who are really loving science more than anything else and have shown fantastic creativity in science, can become the membership of such Institute. Their driving force is not funding but firmly belief that scientists will do much better science if they do not have to spend a lot of time for funding application, and that great scientific findings in history were often discovered by those who were without funding at all but driven by profound imagination and curiosity. Summarized in this review are also the accomplishments of the Gordon Life Science Institute and its future perspective.
文摘Using similarity software to examine the quality of scientific papers is a nuisance. The significance of a scientific paper should be decided by the acknowledged experts. The practice of using the computer program to decide scientific papers must be rescinded or voided.
文摘Recently, a very useful method called “pLoc_Deep-mEuk” 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.
文摘Current coronavirus pandemic has endangered the entire mankind life. The reported cas-es are increasing exponentially. Information of protein post-translational modification (PTM) can provide useful clues to develop antiviral drugs. According to our recent works, the PTM prediction can be significantly improved by widening the samples of training da-taset. Based on such an idea, a new predictor called “iSulf_Wide-PseAAC” has been de-veloped. Its accuracy is overwhelmingly higher than its counterparts. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new pre-dictor has been established at http://121.36.221.79/Isulf_Pse/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.
基金National Natural Science Foundations of China(Nos.61503116,61402007)Foundation for Young Talents in the Colleges of Anhui Province Committee,China(No.2013SQRL097ZD)+1 种基金Natural Science Foundation of Anhui Educational Committee,China(No.KJ2014A198)Natural Science Foundation of Anhui Province,China(No.1408085QF108)
文摘Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.
文摘Post-translational modification (PTM) increases the functional diversity of proteins by introducing new functional groups to the side chain of amino acid of a protein. Among all amino acid residues, the side chain of lysine (K) can undergo many types of PTM, called K-PTM, such as “acetylation”, “crotonylation”, “methylation” and “succinylation” and also responsible for occurring multiple PTM in the same lysine of a protein which leads to the requirement of multi-label PTM site identification. However, most of the existing computational methods have been established to predict various single-label PTM sites and a very few have been developed to solve multi-label issue which needs further improvement. Here, we have developed a computational tool termed mLysPTMpred to predict multi-label lysine PTM sites by 1) incorporating the sequence-coupled information into the general pseudo amino acid composition, 2) balancing the effect of skewed training dataset by Different Error Cost method, and 3) constructing a multi-label predictor using a combination of support vector machine (SVM). This predictor achieved 83.73% accuracy in predicting the multi-label PTM site of K-PTM types. Moreover, all the experimental results along with accuracy outperformed than the existing predictor iPTM-mLys. A user-friendly web server of mLysPTMpred is available at http://research.ru.ac.bd/mLysPTMpred/.
基金the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No.XMUMRF/2019-C4/IECE/0012).
文摘Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.
基金the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,https://dsr.kau.edu.sa/Default-305-EN under Grant No.G:136-611-1441.
文摘Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the DNA alongside transcription features and initiates the transcription of correct response genes.Thus,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and tedious.For this purpose,we propose iPhosD-PseAAC,a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues:(1)Benchmark dataset.(2)The feature extraction techniques such as pseudo amino acid composition(PseAAC),statistical moments,and position relative features.(3)For the classification,artificial neural network AAN will be used.(4)In this step,10-fold cross-validation and self-consistency testing will be used for validation.For self-consistency testing,100%Acc is achieved,whereas,for 10-fold crossvalidation 95.14%Acc,95.58%Sn,94.70%Sp and 0.95 MCC are observed.(5).The final step is the development of a user-friendly web server for the ease of users.Thus,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.
