Xiaomin Luo

Predicting protein–protein interactions based only on sequences information

Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.

TarFisDock: a web server for identifying drug targets with docking approach

TarFisDock is a web-based tool for automating the procedure of searching for small molecule–protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand–protein docking program. In contrast to conventional ligand–protein docking, reverse ligand–protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand–protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at .

PDTD: a web-accessible protein database for drug target identification

BackgroundTarget identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a protein target database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.DescriptionPDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores.ConclusionPDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

Virtual Screening on Natural Products for Discovering Active Compounds and Target Information

Natural products, containing inherently large-scale structural diversity than synthetic compounds, have been the major resources of bioactive agents and will continually play as protagonists for discovering new drugs. However, how to access this diverse chemical space efficiently and effectively is an exciting challenge for medicinal chemists and pharmacologists. While virtual screening, which has shown a great promise in drug discovery, will play an important role in digging out lead (active) compounds from natural products. This review focuses on the strategy of virtual screening based on molecular docking and, with successful examples from our laboratory, illustrates the efficiency of virtual screening in discovering active compounds from natural products. On the other hand, the sequencing of the human genome and numerous pathogen genomes has resulted in an unprecedented opportunity for discovering potential new drug targets. Chemogenomics has appeared as a new technology to initiate target discovery by using active compounds as probes to characterize proteome functions. Natural products are the ideal probes for such research. Binding affinity fingerprint is a powerful chemogenomic descriptor to characterize both small molecules and pharmacologically relevant proteins. Therefore, this review also discusses binding affinity fingerprint strategy for identifying target information from the genomic data by using natural products as the probes.

Cinanserin Is an Inhibitor of the 3C-Like Proteinase of Severe Acute Respiratory Syndrome Coronavirus and Strongly Reduces Virus Replication In Vitro

ABSTRACT The 3C-like proteinase (3CLpro) of severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is one of the most promising targets for anti-SARS-CoV drugs due to its crucial role in the viral life cycle. In this study, a database containing structural information of more than 8,000 existing drugs was virtually screened by a docking approach to identify potential binding molecules of SARS-CoV 3CLpro. As a target for screening, both a homology model and the crystallographic structure of the binding pocket of the enzyme were used. Cinanserin (SQ 10,643), a well-characterized serotonin antagonist that has undergone preliminary clinical testing in humans in the 1960s, showed a high score in the screening and was chosen for further experimental evaluation. Binding of both cinanserin and its hydrochloride to bacterially expressed 3CLpro of SARS-CoV and the related human coronavirus 229E (HCoV-229E) was demonstrated by surface plasmon resonance technology. The catalytic activity of both enzymes was inhibited with 50% inhibitory concentration (IC50) values of 5 μM, as tested with a fluorogenic substrate. The antiviral activity of cinanserin was further evaluated in tissue culture assays, namely, a replicon system based on HCoV-229E and quantitative test assays with infectious SARS-CoV and HCoV-229E. All assays revealed a strong inhibition of coronavirus replication at nontoxic drug concentrations. The level of virus RNA and infectious particles was reduced by up to 4 log units, with IC50 values ranging from 19 to 34 μM. These findings demonstrate that the old drug cinanserin is an inhibitor of SARS-CoV replication, acting most likely via inhibition of the 3CL proteinase.

Progress in Clinical, Pharmacological, Chemical and Structural Biological Studies of Huperzine A: A Drug of Traditional Chinese Medicine Origin for the Treatment of Alzheimers Disease

HupA is a potent, reversible AChEI, which crosses the blood-brain barrier smoothly, and shows high specificity for AChE with a prolonged biological half-life. It has been approved as the drug for the treatment of AD in China, and marketed in USA as a dietary supplement. HupA has been the subject of investigations by an ever-increasing number of researchers since 1980s. In the last four years, HupA has been further studied in many aspects such as the chemical synthesis, structural modification, structure-activity relationship, various biological effects, and mechanisms of action. A number of papers dealing with the computational modeling and X-ray crystallographic studies of HupA-AChE complex have also been published. This review represents a comprehensive documentation of the progress in the studies on HupA during the period of 1999-2002.

Molecular Dynamics Simulations on SDF-1α: Binding with CXCR4 Receptor

Insights into the interacting mode of CXCR4 with SDF-1α are crucial in understanding the structural and functional characteristics of CXCR4 receptor. In this paper a computational pipeline, integrating protein structure prediction, molecular dynamics simulations, automated molecular docking, and Brownian dynamics simulations were employed to investigate the dynamic and energetic aspects of CXCR4 associating with SDF-1α. The entire simulation revealed the surface distribution feature of electrostatic potentials and conformational “open-close” process of the receptor. The possible binding conformation of CXCR4 was identified, and the CXCR4—SDF-1α binding complex was generated. Arg188-Glu277 salt bridge plays an important role for both the extracellular domain conformational change and SDF-1α binding. Two binding sites were mapped at the extracellular domain (Site 1) and inside the transmembrane domain (Site 2), which are composed of conserved residues. Sites 1 and 2 contribute ∼60% and 40% to the binding affinity with SDF-1α, respectively. The binding model is in agreement with most of the experimental data. Transmembrane VI has more significant motion in the harmonious conformational transition of CXCR4 during SDF-1α binding, which may be possibly associated with signal transduction. Based on the modeling and simulation, a binding mechanism hypothesis between CXCR4 and SDF-1α and its relationship to the signal transduction has been proposed.

Nucleocapsid protein of SARS coronavirus tightly binds to human cyclophilin A

Abstract Severe acute respiratory syndrome coronavirus (SARS-CoV) is responsible for SARS infection. Nucleocapsid protein (NP) of SARS-CoV (SARS_NP) functions in enveloping the entire genomic RNA and interacts with viron structural proteins, thus playing important roles in the process of virus particle assembly and release. Protein–protein interaction analysis using bioinformatics tools indicated that SARS_NP may bind to human cyclophilin A (hCypA), and surface plasmon resonance (SPR) technology revealed this binding with the equilibrium dissociation constant ranging from 6 to 160nM. The probable binding sites of these two proteins were detected by modeling the three-dimensional structure of the SARS_NP–hCypA complex, from which the important interaction residue pairs between the proteins were deduced. Mutagenesis experiments were carried out for validating the binding model, whose correctness was assessed by the observed effects on the binding affinities between the proteins. The reliability of the binding sites derived by the molecular modeling was confirmed by the fact that the computationally predicted values of the relative free energies of the binding for SARS_NP (or hCypA) mutants to the wild-type hCypA (or SARS_NP) are in good agreement with the data determined by SPR. Such presently observed SARS_NP–hCypA interaction model might provide a new hint for facilitating the understanding of another possible SARS-CoV infection pathway against human cell.

Large magnetic entropy change near room temperature in antiperovskite SnCMn3

We report the observation of large magnetocaloric effect near room temperature in antipervoskite SnCMn3. The maximal magnetic entropy change at the first-order ferrimagnetic-paramagnetic transition temperature (TC 279 K) is about 80.69mJ/cm3 K and 133mJ/cm3 K under the magnetic field of 20 kOe and 48 kOe, respectively. These values are close to those of typical magnetocaloric materials. The large magnetocaloric effect is associated with the sharp change of lattice, resistivity and magnetization in the vicinity of TC. Through the measurements of Seebeck coefficient and normal Hall effect, the title system is found to undergo a reconstruction of electronic structure at TC. Considering its low-cost and innocuous raw materials, Mn-based antiperovskite compounds are suggested to be appropriate for pursuing new materials with larger magnetocaloric effect.

Crystal growth and superconductivity of FeSex

In this work, crystals of FeSex have been grown by a flux approach. The crystallization process is divided into two stages. First, stoichiometric polycrystal FeSe0.82 was sintered in a solid state reaction. Then, FeSex crystals with a size about 500 µm were successfully grown in an evacuated sealed quartz tube using a NaCl/KCl flux. The products include two crystal structures: tetragonal and hexagonal. The electronic transport and magnetic property measurements show that FeSex crystals exhibit a superconducting transition at about 10 K.