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Use chou’s 5-steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain similarity Analysis

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Natural science 2020年第3期12卷 181-198页

作者： Weizhong Lin Xuan Xiao Wangren Qiu Kuo-Chen chou Computer Department Jing-De-Zhen Ceramic Institute Jing-De-Zhen China Gordon Life science Institute Boston MA 02478 USA

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://***/jcilwz/dRHP-GreyFun.

关键词： Remote Homology Proteins Grey Model Domain similarity chou’s 5-steps Rules

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Theoretical and experimental biology in one<br>—A symposium in honour of Professor Kuo-Chen chou’s 50th anniversary and Professor Richard Giegé’s 40th anniversary of their scientific careers

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Journal of Biomedical science and Engineering 2013年第4期6卷 435-442页

作者： sheng-Xiang Lin Jacques Lapointe Department of Biochemistry Microbiology and Bioinformatics and PROTEO Laval University Quebec Canada Laboratory of Molecular Endocrinology and Oncology Research Center of Laval University Hospital Center (CHUQ-CHUL) and PROTEO Laval University Quebec Canada

It has been a dream that theoretical biology can be extensively applied in experimental biology to accelerate the understanding of the sophiscated movements in living organisms. A brave assay and an excellent example were represented by enzymology, in which the well-established physico-chemistry is used to describe, to fit, to predict and to improve enzyme reactions. Before the modern bioinformatics, the developments of the combination of theoretical biology and experimental biology have been mainly limited to various classic formulations. The systematic use of graphic rules by Prof. Kuo-Chen chou and his co-workers has significantly facilitated to deal with complicated enzyme systems. With the recent fast progress of bioinformatics, prediction of protein structures and various protein attributes have been well established by chou and co-workers, stimulating the experimental biology. For example, their recent method for predicting protein subcellular localization (one of the important attributes of proteins) has been extensively applied by scientific colleagues, yielding many new results with thousands of citations. The research by Prof. chou is characterized by introducing novel physical concepts as well as powerful and elegant mathematical methods into important biomedical problems, a focus throughout his career, even when facing enormous difficulties. His efforts in 50 years have greatly helped us to realize the dream to make “theoretical and experimental biology in one”. Prof. Richard Giege is well known for his multi-disciplinary research combining physics, chemistry, enzymology and molecular biology. His major focus of study is on the identity of tRNAs and their interactions with aminoacyl-tRNA synthetases (aaRs), which are of critical importance to the fidelity of protein biosynthesis. He and his colleagues have carried out the first crystallization of a tRNA/aaRs complex, that between tRNAAsp and AspRs from yeast. The determination of the complex structure c

关键词： Theoretical Biology Experimental Biology chou’s Graphic Rules in Enzymology Prediction of Protein Attributes chou’s Invariance Theorem Macromolecular Crystallogenesis tRNA Identity on Aminoacylation specificity Physico-Biochemistry

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

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Natural science 2017年第9期9卷 330-349页

作者： Xuan Xiao Xiang Cheng shengchao su Qi Mao Kuo-Chen chou Computer Department Jingdezhen Ceramic Institute Jingdezhen China The Gordon Life science Institute Boston USA College of Information science and Technology Donghua University Shanghai China Center for Informational Biology University of Electronic Science and Technology of China Chengdu China Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Jeddah Saudi Arabia

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://***/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

关键词： Multi-Target Drugs Gene Ontology chou’s General PseAAC ML-GKR chou’s Metrics

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