Theoretical and experimental biology in one —

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 coworkers 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 tRNA and AspRS from yeast. The determination of the complex structure contributed significantly to understand the interaction of protein and RNA. From his fine research, they have also found other biological function of these small RNAs. He has developed in parallel appropriate methods for his research, of which the protein crystallogenesis, a name he has coined, is an excellent example. Now macromolecular crystallogenesis has become a developed science. In fact, such contribution has accelerated the development of protein crystallography, stimulating the study of macromolecular structure and function.

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[41]  Zachariah Sinkala,et al.  Soliton/exciton transport in proteins. , 2006, Journal of theoretical biology.

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[44]  Glen A. Gordon,et al.  Designed electromagnetic pulsed therapy: Clinical applications , 2007, Journal of cellular physiology.

[45]  Kuo-Chen Chou,et al.  MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. , 2007, Biochemical and biophysical research communications.

[46]  Yanzhi Guo,et al.  Predicting DNA-binding proteins: approached from Chou’s pseudo amino acid composition and other specific sequence features , 2007, Amino Acids.

[47]  Kuo-Chen Chou,et al.  Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. , 2007, Biochemical and biophysical research communications.

[48]  K. Chou,et al.  EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. , 2007, Biochemical and biophysical research communications.

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[50]  K. Chou,et al.  Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms , 2008, Nature Protocols.

[51]  Guangya Zhang,et al.  Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo-amino acid composition. , 2008, Journal of theoretical biology.

[52]  Ozlem Keskin,et al.  Principles of Protein Recognition and Properties of Protein-protein Interfaces , 2008, Protein-protein Interactions and Networks.

[53]  Glen A. Gordon Extrinsic electromagnetic fields, low frequency (phonon) vibrations, and control of cell function: a non-linear resonance system , 2008 .

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[55]  Hao Lin,et al.  Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. , 2008, Protein and peptide letters.

[56]  Xiaoying Jiang,et al.  Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. , 2008, Protein and peptide letters.

[57]  Hao Lin The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. , 2008, Journal of theoretical biology.

[58]  Fengmin Li,et al.  Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. , 2008, Protein and peptide letters.

[59]  Shao-Wu Zhang,et al.  Using Chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach , 2008, Amino Acids.

[60]  Zong Dai,et al.  Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis , 2009, Amino Acids.

[61]  Yongsheng Ding,et al.  Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier , 2008, Pattern Recognit. Lett..

[62]  K. Chou,et al.  ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. , 2008, Biochemical and biophysical research communications.

[63]  Tongliang Zhang,et al.  Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location , 2008, Amino Acids.

[64]  J. Andraos Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws — New methods based on directed graphs , 2008 .

[65]  Kuo-Chen Chou,et al.  Steps to the clinic with ELF EMF , 2009 .

[66]  Hao Lin,et al.  Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. , 2009, Protein and peptide letters.

[67]  Yanzhi Guo,et al.  Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. , 2009, Journal of theoretical biology.

[68]  J. Nieto,et al.  Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. , 2009, Journal of theoretical biology.

[69]  Hao Lin,et al.  Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition , 2009, Acta biotheoretica.

[70]  Jianding Qiu,et al.  Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. , 2009, Analytical biochemistry.

[71]  Xiaoyong Zou,et al.  Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. , 2009, Protein and peptide letters.

[72]  K. Chou,et al.  REVIEW : Recent advances in developing web-servers for predicting protein attributes , 2009 .

[73]  Jiangning Song,et al.  Prediction of protein folding rates from primary sequence by fusing multiple sequential features , 2009 .

[74]  Q Gu,et al.  Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns. , 2010, Protein and peptide letters.

[75]  M. Esmaeili,et al.  Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. , 2010, Journal of theoretical biology.

[76]  K. Chou,et al.  Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization , 2010, PloS one.

[77]  Shao-Ping Shi,et al.  Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform. , 2010, Protein and peptide letters.

[78]  Menglong Li,et al.  SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition. , 2010, Journal of theoretical biology.

[79]  D. Poirier,et al.  17β-Hydroxysteroid Dehydrogenase Type 1 Stimulates Breast Cancer by Dihydrotestosterone Inactivation in addition to Estradiol Production , 2010 .

[80]  K. Chou Graphic rule for drug metabolism systems. , 2010, Current drug metabolism.

[81]  Ganapati Panda,et al.  A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction , 2010, Comput. Biol. Chem..

[82]  Hassan Mohabatkar,et al.  Prediction of cyclin proteins using Chou's pseudo amino acid composition. , 2010, Protein and peptide letters.

[83]  K. Chou,et al.  iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins , 2011, PloS one.

[84]  K. Chou,et al.  iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model , 2011, PloS one.

[85]  Kuo-Chen Chou,et al.  NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features , 2011, PloS one.

[86]  H. Mohabatkar,et al.  Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach , 2011, Journal of Structural and Functional Genomics.

[87]  Kuo-Chen Chou,et al.  GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. , 2011, Molecular bioSystems.

[88]  K. Chou Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.

[89]  Jianxiu Guo,et al.  Predicting protein folding rates using the concept of Chou's pseudo amino acid composition , 2011, Journal of computational chemistry.

[90]  Dongsheng Zou,et al.  Supersecondary structure prediction using Chou's pseudo amino acid composition , 2011, J. Comput. Chem..

[91]  A. Esmaeili,et al.  Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. , 2011, Journal of theoretical biology.

[92]  K. Chou,et al.  iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. , 2012, Molecular bioSystems.