Kaixian Chen

Bio: Kaixian Chen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Virtual screening & Docking (molecular). The author has an hindex of 47, co-authored 380 publications receiving 9209 citations. Previous affiliations of Kaixian Chen include Shanghai University & East China University of Science and Technology.

Exploring transition pathway and free-energy profile of large-scale protein conformational change by combining normal mode analysis and umbrella sampling molecular dynamics.

Abstract: Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔG ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions.

Residues on the dimer interface of SARS coronavirus 3C-like protease: dimer stability characterization and enzyme catalytic activity analysis.

Abstract: 3C-like protease (3CL pro) plays pivotal roles in the life cycle of severe acute respiratory syndrome coronavirus (SARS-CoV) and only the dimeric protease is proposed as the functional form. Guided by the crystal structure and molecular dynamics simulations, we performed systematic mutation analyses to identify residues critical for 3CL pro dimerization and activity in this study. Seven residues on the dimer interface were selected for evaluating their contributions to dimer stability and catalytic activity by biophysical and biochemical methods. These residues are involved in dimerization through hydrogen bonding and broadly located in the N-terminal finger, the alpha-helix A' of domain I, and the oxyanion loop near the S1 substrate-binding subsite in domain II. We revealed that all seven single mutated proteases still have the dimeric species but the monomer-dimer equilibria of these mutants vary from each other, implying that these residues might contribute differently to the dimer stability. Such a conclusion could be further verified by the results that the proteolytic activities of these mutants also decrease to varying degrees. The present study would help us better understand the dimerization-activity relationship of SARS-CoV 3CL pro and afford potential information for designing anti-viral compounds targeting the dimer interface of the protease.

Binding analyses between Human PPARgamma-LBD and ligands.

Abstract: The binding characteristics of a series of PPARgamma ligands (GW9662, GI 262570, cis-parinaric acid, 15-deoxy-Delta(12,14)-prostaglandin J(2), LY171883, indomethacin, linoleic acid, palmitic acid and troglitazone) to human PPARgamma ligand binding domain have been investigated for the first time by using surface plasmon resonance biosensor technology, CD spectroscopy and molecular docking simulation. The surface plasmon resonance biosensor determined equilibrium dissociation constants (KD values) are in agreement with the results reported in the literature measured by other methods, indicating that the surface plasmon resonance biosensor can assume a direct assay method in screening new PPARgamma agonists or antagonists. Conformational changes of PPARgamma caused by the ligand binding were detected by CD determination. It is interesting that the thermal stability of the receptor, reflected by the increase of the transition temperature (T(m)), was enhanced by the binding of the ligands. The increment of the transition temperature (DeltaT(m)) of PPARgamma owing to ligand binding correlated well with the binding affinity. This finding implies that CD could possibly be a complementary technology with which to determine the binding affinities of ligands to PPARgamma. Molecular docking simulation provided reasonable and reliable binding models of the ligands to PPARgamma at the atomic level, which gave a good explanation of the structure-binding affinity relationship for the ligands interacting with PPARgamma. Moreover, the predicted binding free energies for the ligands correlated well with the binding constants measured by the surface plasmon resonance biosensor, indicating that the docking paradigm used in this study could possibly be employed in virtual screening to discover new PPARgamma ligands, although the docking program cannot accurately predict the absolute ligand-PPARgamma binding affinity.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors

Abstract: A new coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the aetiological agent responsible for the 2019–2020 viral pneumonia outbreak of coronavirus disease 2019 (COVID-19)1–4. Currently, there are no targeted therapeutic agents for the treatment of this disease, and effective treatment options remain very limited. Here we describe the results of a programme that aimed to rapidly discover lead compounds for clinical use, by combining structure-assisted drug design, virtual drug screening and high-throughput screening. This programme focused on identifying drug leads that target main protease (Mpro) of SARS-CoV-2: Mpro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication and transcription, making it an attractive drug target for SARS-CoV-25,6. We identified a mechanism-based inhibitor (N3) by computer-aided drug design, and then determined the crystal structure of Mpro of SARS-CoV-2 in complex with this compound. Through a combination of structure-based virtual and high-throughput screening, we assayed more than 10,000 compounds—including approved drugs, drug candidates in clinical trials and other pharmacologically active compounds—as inhibitors of Mpro. Six of these compounds inhibited Mpro, showing half-maximal inhibitory concentration values that ranged from 0.67 to 21.4 μM. One of these compounds (ebselen) also exhibited promising antiviral activity in cell-based assays. Our results demonstrate the efficacy of our screening strategy, which can lead to the rapid discovery of drug leads with clinical potential in response to new infectious diseases for which no specific drugs or vaccines are available. A programme of structure-assisted drug design and high-throughput screening identifies six compounds that inhibit the main protease of SARS-CoV-2, demonstrating the ability of this strategy to isolate drug leads with clinical potential.

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays

Abstract: This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.