Simulations of Enzyme Catalysis and Protein Action
Our early works paved the way for quantitative theoretical studies of enzymatic reactions. These works introduced the Hybrid - Quantum Mechanical / Molecular Mechanics (QM/MM) method and a microscopic approach for studies of electrostatic effects in proteins. The QM/MM and related approaches allow other scientists to study the energetics and dynamics of enzymatic reactions. Our group continues to push the frontiers of the field developing new approaches, studying complex effects such as quantum tunneling and entropic effects in enzymes and exploring the action of enzymes of special biological importance.
Simulating the Dynamics of Photobiological Processes
The first molecular dynamics simulation of a biological process was reported by Warshel in a 1976 study of the primary event of the vision process. Warshel's group continues to be very active in this field, studying ultrafast reactions such as the primary processes in the photosynthetic reaction center (where their theoretical study was the first to elucidate the correct electron transfer mechanisms) and the photoisomerization reaction in bacteriorhodopsin.
Simulation of Chemical Reactions in Solution
In order to understand enzymatic reactions it is crucial to have a quantitative picture of the corresponding reference reactions in solutions. The realization of this fact led Warshel's group to spend a major effort on studying the energetics and dynamics of chemical processes in solution. These studies include the use and development of various QM/MM approaches and related models for quantitative simulations of chemical reactions in solutions.
Electrostatic Energies in Macromolecules
Our early works involved the development of the first physically consistent models for studies of electrostatic energies in proteins. The use of these models led to the current realization that electrostatic energies provide the best way of correlating structure and function of biological molecules. The use of calculations of electrostatic energies in analyzing a wide range of biological problems is a major part of the research effort of Warshel's group. This includes evaluation of pKa's and redox potentials of proteins, drug designs, and studies of protein-protein interactions.
The simplified model for protein folding introduced by Levitt and Warshel is now the method of choice in most studies of protein folding. Our recent effort in this direction has focused on developing innovative ways of using the results of the simplified model in the evaluation of the corrresponding free energies of more detailed all-atom models
On the fundamental principles relating protein geometrical shapes to protein dynamics and function
Protein average thermal fluctuations are shown be derived directly from protein structures without assuming any mechanical models. A number of protein structural profiles such as residue packing, i.e. the weighted contact number (WCN), protein centroid distance, thermal fluctuation profile or smoothed solvent accessible surface profiles are similar to a smoothed version of sequence conservation profile in varying degrees. This is surprising, since the WCN profile is derived from a single protein structure, while the sequence conservation profile is derived from multiple sequence alignment of a group of homologous sequences. This suggests that a single protein structure contains evolutionary information that was previously derived only from multiple sequences. We are also interested in the following topics such as knotted proteins, protein subcellular localization, protein structure prediction, disulfide proteins, molecular simulation and any topics that are intellectually interesting.
- Liu JW, Cheng CW, Lin YF, Chen SY, Hwang JK, SC Yen, Relationships between residue Voronoi volume and sequence conservation in proteins BBA – Proteins and Proteomics (2017) (accepted)
- Huang TT, del Valle Marcos ML, Hwang JK, Echave J. A mechanistic stress model of protein evolution accounts for site-specific evolutionary rates and their relationship with packing density and flexibility. J. BMC Evol Biol. 2014 Apr 9;14:78. doi: 10.1186/1471-2148-14-78.
- Yeh SW, Liu JW, Yu SH, Shih CH, Hwang JK, Echave J. Site-specific structural constraints on protein sequence evolutionary divergence: local packing density versus solvent exposure. Mol Biol Evol. 2014 Jan;31(1):135-9. doi: 10.1093/molbev/mst178.
- Yeh SW, Huang TT, Liu JW, Yu SH, Shih CH, Hwang JK, Echave J. Local packing density is the main structural determinant of the rate if protein sequence sequence at the site level. Biomed Res Int. 2014;2014:572409. doi: 10.1155/2014/572409.
- Shih CH, Chang CM, Lin YS, Lo WC, Hwang JK. Evolutionary information hidden in a single protein structure. Proteins (2012) 80, 16472.
- Lin CP, Huang SW, Lai YL, Yen SC, Shih CH, Lu CH, Huang CC, Hwang JK. Deriving protein dynamical properties from weighted protein contact number, Proteins: Structure, Function and Bioinformatics (2008) 72, 929-935.
- Shih CH, Huang SW, Yen SC, Lai YL, Yu SH, Hwang JK. Computation of protein dynamics without a mechanical model, Proteins: Structure, Function and Bioinformatics (2007) 68, 34-38
Computational Health Informatics and Omics
Recently, high-throughput sequencing and Brain-Computer interface technology become our major focus both in scientific research and in clinical applications. The ultimate goal in our lab is to get a comprehensive view of complex disease-gene-drug relationships - from the molecular level to biological functions of drug targets and response. Reveal previously unseen patterns across heterogeneous and big datasets to predict targets and biomarkers. Our team will also define a rigorous data-mining program and integrate the data using machine learning or other deep analytical approaches.
A resource for experimentally validated microRNA-target interactions
Research / Development Strategy and Direction
- Keep outstanding bioinformatics in microRNA and small RNA research areas.
- Application of High-throughput technology and cross-domain research in biomedical research.
- High throughput multilevel body technology (Multi-Omics) and big data analysis.
- Research on Genome Medicine, microbiology, epigenetics, and noncoding RNA.
- Development of Next-Generation Sequencing (NGS) and Third-Generation sequencing (TGS) technology and integration analysis methods.
- Development of new cancer diagnosis related to miRNA Biomarker and target.
- Development of biological databases and bioinformatics tools.
- Development of non-invasive detection and precision medical technology.
- Development of new database and computing system for transitional Chinese herbal medicine.
- Clinical Applications of Brain-Computer Interfaces
Computational identification of protein PTM sites
dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications
dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data
Prof. HIRAO's research group is interested in computationally deriving general principles of chemical reactions and molecular interactions occurring in nature and labs, and also in rationally designing therapeutic and other types of functional molecules with experimentalists.
Specific research themes of the group include the following:
1. Development of intuitively appealing methods for analyzing chemical reactivities and molecular interactions: reactive hybrid orbital (RHO); reactive bond orbital (RBO); energy decomposition analysis; reconstruction of molecular wave functions for chemistry purposes.
2. Development of unique molecular mechanics (MM) force-field parametrization schemes: partial hessian fitting (PHF); full hessian fitting (FHF); internal hessian fitting (IHF); geometry amendment (Katachi); genetic and other optimization algorithms; machine learning; Python, etc.
3. Enzyme catalysis: metalloenzymes, proteases, etc.; density functional theory (DFT); ab initio quantum chemistry; quantum mechanics and molecular mechanics (QM/MM); ONIOM; molecular dynamics (MD); catalytic cycles; QM/MM energy decomposition; free energy calculations.
4. Computational drug design: discovery of potent and selective enzyme inhibitors; docking simulation; analysis of protein-ligand interactions; drug metabolism; mechanism-based inactivation (MBI); central nervous system (CNS); β-lactam antibiotics; DNA-targeting anti-cancer drugs.
5. Bioinorganic chemistry: heme enzymes (cytochrome P450, etc.); nonheme enzymes (myo-inositol oxygenase (MIOX), hydroxyethylphosphonate dioxygenase (HEPD), etc.); high-spin reactivity of iron(IV)-oxo intermediates; metal-oxo complexes bearing synthetic ligands; C–H bond activation.
6. Homogeneous and heterogeneous catalysis: transition-metal catalysis; photocatalytic reactions; main group chemistry; organocatalysis; reactions catalyzed by heterogeneous systems such as nanoparticles and metal–organic frameworks (MOFs); machine learning; QM/QM hybridization.
7. Porous materials: MOFs; pillararenes; zeolites; molecular organic cages; porous organic frameworks (POFs); adsorption, separation, purification, catalytic activation, and sensing of guest molecules; QM/MM, MM, and plane-wave density functional theory (DFT) treatments of porous systems.
Computational and experimental study of biological & chemical catalysis
Enzymes play important roles in many key biological processes and therefore are major targets for biomedical research. Better understanding of enzymes is essential for analyzing the enzyme activities, and for the design of inhibitors as pharmaceutical lead compounds. Our particular interest is the epigenetic enzymes, metalloenzymes, biosynthesis, and inhibitor design. The main computational methods used are combined quantum mechanical/molecular mechanical modelling (QM/MM), molecular dynamics simulation and molecular docking.
Organic synthesis greatly facilitates the development of modern medicine, but there are many challenges to overcome, such as creating new reaction types, increasing reaction efficiency, and decreasing the cost and waste. Mechanism understanding of organic reactions provides vital information to optimize reactions and design new reactions. We use quantum mechanics (QM) method and experimental methods (MS, EPR, electrochemical methods, etc.) to understand, predict, optimize, and design organic reactions. The current primary area of interest includes organic synthesis of drug molecules, bio-active compounds and natural products, transition-metal catalyzed reactions, radical reactions and electrochemical reactions.
Other research interests include computer-aided drug design and fundamental principles of enzymatic and chemical catalysis
Dr. Zhu’s current research focuses on RNA-protein interactions, mechanism of RNA/DNA interference and the development of automated path searching methods and other enhanced sampling techniques.
Development of enhanced sampling techniques
such as automated path searching methods and investigation of functional dynamics for biological systems
The group's research currently focus on the development of enhanced sampling techniques such as automated path searching methods and the biological applications.
- To develop the method to locate multiple transition pathway (High-energy parallel cascade selection, HePaCS) and to optimize the multiple transition paths (multiple Traveling-salesman based Automated Path Searching, mTAPS);
- To investigate the molecular mechanism of RNA/DNA interference with argonaute.
- To investigate the molecular mechanism of small-molecule induced homo/hetero-dimerization effects of MEK1 kinase.
Artificial intelligence (AI) has profoundly changed the mode of drug research and development (R&D) industry. In contrast to traditional methods, AADD has the ability to explore much larger chemical space by computer, thus improving the structural novelty of drug molecules and reduce the time of the cycle. Our group aims to develop an interdisciplinary AADD platform for novel drug discovery and modification. The platform combines the world-leading computational biology technologies, ultra-large scale high-throughput virtual screening technologies, and the AI algorithms based on original molecular features from mechanism studies.