Optimization of Hybrid Hub-and-Spoke Network Operation for Less-Than-Truckload Freight Transportation considering Incremental Quantity Discount

This paper presents a mixed integer linear programming model (MILP) for optimizing the hybrid hub-and-spoke network operation for a less-than-truckload transportation service. The model aims to minimize the total operation costs (transportation cost and transfer cost), given the determined demand matrix, truck load capacity, and uncapacitated road transportation. The model also incorporates an incremental quantity discount function to solve the reversal of the total cost and the total demand. The model is applied to a real case of a Chinese transportation company engaged in nationwide freight transportation. The numerical example shows that, with uncapacitated road transportation, the total costs and the total vehicle trips of the hybrid hub-and-spoke network operation are, respectively, 8.0% and 15.3% less than those of the pure hub-and-spoke network operation, and the assumed capacity constraints in an extension model result in more target costs on the hybrid hub-and-spoke network. The two models can be used to support the decision making in network operations by transportation and logistics companies

Application of Nickel(II) Complexes to the Efficient Synthesis of α- or β-Amino Acids

Nonproteinogenic α- or β-amino acids have attracted tremendous attention, as they are widely utilized for biological, biochemical, pharmaceutical, and asymmetric chemical investigations. Recently, we developed a series of new strategies for preparing achiral and chiral nickel(ii) complexes for the synthesis of amino acids. We applied these new methods utilizing chiral nickel(ii) complexes for the asymmetric Mannich reaction to synthesize enantiopure α,β-diamino acids, the enantioselective tandem conjugate addition–elimination reaction to prepare glutamic acid derivatives, the Suzuki coupling reaction to yield β2-amino acid derivatives, the asymmetric Mannich reaction to synthesize 3-aminoaspartate, the asymmetric Michael addition reaction to give β-substituted-α,γ-diaminobutyric acid derivatives, the asymmetric alkylation reaction to prepare linear ω-trifluoromethyl containing amino acids, and the asymmetric Michael addition reaction to synthesize syn-β-substituted tryptophans

Lys169 of Human Glucokinase Is a Determinant for Glucose Phosphorylation: Implication for the Atomic Mechanism of Glucokinase Catalysis

Glucokinase (GK), a glucose sensor, maintains plasma glucose homeostasis via phosphorylation of glucose and is a potential therapeutic target for treating maturity-onset diabetes of the young (MODY) and persistent hyperinsulinemic hypoglycemia of infancy (PHHI). To characterize the catalytic mechanism of glucose phosphorylation by GK, we combined molecular modeling, molecular dynamics (MD) simulations, quantum mechanics/molecular mechanics (QM/MM) calculations, experimental mutagenesis and enzymatic kinetic analysis on both wild-type and mutated GK. Our three-dimensional (3D) model of the GK-Mg2+-ATP-glucose (GMAG) complex, is in agreement with a large number of mutagenesis data, and elucidates atomic information of the catalytic site in GK for glucose phosphorylation. A 10-ns MD simulation of the GMAG complex revealed that Lys169 plays a dominant role in glucose phosphorylation. This prediction was verified by experimental mutagenesis of GK (K169A) and enzymatic kinetic analyses of glucose phosphorylation. QM/MM calculations were further used to study the role of Lys169 in the catalytic mechanism of the glucose phosphorylation and we found that Lys169 enhances the binding of GK with both ATP and glucose by serving as a bridge between ATP and glucose. More importantly, Lys169 directly participates in the glucose phosphorylation as a general acid catalyst. Our findings provide mechanistic details of glucose phorphorylation catalyzed by GK, and are important for understanding the pathogenic mechanism of MODY

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

<p>Abstract</p> <p>Background</p> <p>Target 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 (<it>Tar</it>get <it>Fis</it>hing <it>Dock</it>ing) <url>http://www.dddc.ac.cn/tarfisdock</url>, which has been used widely by others. Recently, we have constructed a protein target database, <it>P</it>otential <it>D</it>rug <it>T</it>arget <it>D</it>atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.</p> <p>Description</p> <p>PDTD is a web-accessible protein database for <it>in silico </it>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.</p> <p>Conclusion</p> <p>PDTD 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 <it>in silico </it>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 <url>http://www.dddc.ac.cn/pdtd/</url>.</p

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

Molecular Basis of NDM-1, a New Antibiotic Resistance Determinant

The New Delhi Metallo-β-lactamase (NDM-1) was first reported in 2009 in a Swedish patient. A recent study reported that Klebsiella pneumonia NDM-1 positive strain or Escherichia coli NDM-1 positive strain was highly resistant to all antibiotics tested except tigecycline and colistin. These can no longer be relied on to treat infections and therefore, NDM-1 now becomes potentially a major global health threat

The dynamic conformational landscape of the protein methyltransferase SETD8

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape