Fang Bai

Optimization of Culture Conditions for Lipid Production by Rhodosporidium toruloides

Abstract The effects of culture conditions on the lipid production by Rhodosporidium toruloides Y4 were investigated using uniform design principles and single-factor experiments. The optimal medium was obtained as follows: 70 g/L glucose, 0.1 g/L (NH4)2SO4, 0.75 g/L yeast powder, 1.5 g/L MgSO4·7H2O, 0.4 g/L KH2PO4, sterilized at 121 °C for 15 min, and then supplemented with 1.91×10−6 mmol/L ZnSO4, 1.50 mmol/L CaCl2, 1.22×10−4 mmol/L MnCl2, and 1.00×10−4 mmol/L CuSO4. The optimal fermentation conditions were also developed as: 50 mL of medium (pH 6.0) in a 250-mL Erlenmeyer flask with 10% inoculum (28-h-old) under orbital shaking at 200 r/min for 120 h at 30 °C. Under the optimized conditions, yeast accumulated lipids up to 76% on a cellular biomass basis with biomass yield of 18.2 g/L.

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development.

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

BackgroundConformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.ResultsThe conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1.ConclusionOn the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of reproducing the bioactive conformations against 329 structures. The speed advantage indicates Cyndi is a powerful alternative method for extensive conformational sampling and large-scale conformer database preparation.

The Fe-S cluster-containing NEET proteins mitoNEET and NAF-1 as chemotherapeutic targets in breast cancer

Significance Cancer is a leading cause of mortality worldwide, with the identification of novel drug targets and chemotherapeutic agents being a high priority in the fight against it. The NEET proteins mitoNEET (mNT) and nutrient-deprivation autophagy factor-1 (NAF-1) were recently shown to be required for cancer cell proliferation. Utilizing a combination of experimental and computational techniques, we identified a derivative of the mitocan cluvenone that binds to NEET proteins at the vicinity of their 2Fe-2S clusters and facilitates their destabilization. The new drug displays a high specificity in the selective killing of human epithelial breast cancer cells, without any apparent effects on normal breast cells. Our results identify the 2Fe-2S clusters of NEET proteins as a novel target in the chemotherapeutic treatment of breast cancer. Identification of novel drug targets and chemotherapeutic agents is a high priority in the fight against cancer. Here, we report that MAD-28, a designed cluvenone (CLV) derivative, binds to and destabilizes two members of a unique class of mitochondrial and endoplasmic reticulum (ER) 2Fe-2S proteins, mitoNEET (mNT) and nutrient-deprivation autophagy factor-1 (NAF-1), recently implicated in cancer cell proliferation. Docking analysis of MAD-28 to mNT/NAF-1 revealed that in contrast to CLV, which formed a hydrogen bond network that stabilized the 2Fe-2S clusters of these proteins, MAD-28 broke the coordinative bond between the His ligand and the cluster’s Fe of mNT/NAF-1. Analysis of MAD-28 performed with control (Michigan Cancer Foundation; MCF-10A) and malignant (M.D. Anderson–metastatic breast; MDA-MB-231 or MCF-7) human epithelial breast cells revealed that MAD-28 had a high specificity in the selective killing of cancer cells, without any apparent effects on normal breast cells. MAD-28 was found to target the mitochondria of cancer cells and displayed a surprising similarity in its effects to the effects of mNT/NAF-1 shRNA suppression in cancer cells, causing a decrease in respiration and mitochondrial membrane potential, as well as an increase in mitochondrial iron content and glycolysis. As expected, if the NEET proteins are targets of MAD-28, cancer cells with suppressed levels of NAF-1 or mNT were less susceptible to the drug. Taken together, our results suggest that NEET proteins are a novel class of drug targets in the chemotherapeutic treatment of breast cancer, and that MAD-28 can now be used as a template for rational drug design for NEET Fe-S cluster-destabilizing anticancer drugs.

Breast cancer tumorigenicity is dependent on high expression levels of NAF-1 and the lability of its Fe-S clusters.

Significance Elevated expression of the iron–sulfur (Fe-S) protein nutrient-deprivation autophagy factor-1 (NAF-1) is associated with the progression of multiple cancer types. Here we demonstrate that the lability of the Fe-S cluster of NAF-1 plays a key role in promoting breast cancer cell proliferation, tumor growth, and resistance of cancer cells to oxidative stress. Our study establishes an important role for the unique 3Cys-1His Fe-S cluster coordination structure of NAF-1 in promoting the development of breast cancer tumors and suggests the potential use of drugs that suppress NAF-1 accumulation or stabilize its cluster in the treatment of cancers that display high expression levels of NAF-1. Iron–sulfur (Fe-S) proteins are thought to play an important role in cancer cells mediating redox reactions, DNA replication, and telomere maintenance. Nutrient-deprivation autophagy factor-1 (NAF-1) is a 2Fe-2S protein associated with the progression of multiple cancer types. It is unique among Fe-S proteins because of its 3Cys-1His cluster coordination structure that allows it to be relatively stable, as well as to transfer its clusters to apo-acceptor proteins. Here, we report that overexpression of NAF-1 in xenograft breast cancer tumors results in a dramatic augmentation in tumor size and aggressiveness and that NAF-1 overexpression enhances the tolerance of cancer cells to oxidative stress. Remarkably, overexpression of a NAF-1 mutant with a single point mutation that stabilizes the NAF-1 cluster, NAF-1(H114C), in xenograft breast cancer tumors results in a dramatic decrease in tumor size that is accompanied by enhanced mitochondrial iron and reactive oxygen accumulation and reduced cellular tolerance to oxidative stress. Furthermore, treating breast cancer cells with pioglitazone that stabilizes the 3Cys-1His cluster of NAF-1 results in a similar effect on mitochondrial iron and reactive oxygen species accumulation. Taken together, our findings point to a key role for the unique 3Cys-1His cluster of NAF-1 in promoting rapid tumor growth through cellular resistance to oxidative stress. Cluster transfer reactions mediated by the overexpressed NAF-1 protein are therefore critical for inducing oxidative stress tolerance in cancer cells, leading to rapid tumor growth, and drugs that stabilize the NAF-1 cluster could be used as part of a treatment strategy for cancers that display high NAF-1 expression.

A Generic Force Field for Protein Coarse-Grained Molecular Dynamics Simulation

Coarse-grained (CG) force fields have become promising tools for studies of protein behavior, but the balance of speed and accuracy is still a challenge in the research of protein coarse graining methodology. In this work, 20 CG beads have been designed based on the structures of amino acid residues, with which an amino acid can be represented by one or two beads, and a CG solvent model with five water molecules was adopted to ensure the consistence with the protein CG beads. The internal interactions in protein were classified according to the types of the interacting CG beads, and adequate potential functions were chosen and systematically parameterized to fit the energy distributions. The proposed CG force field has been tested on eight proteins, and each protein was simulated for 1000 ns. Even without any extra structure knowledge of the simulated proteins, the Cα root mean square deviations (RMSDs) with respect to their experimental structures are close to those of relatively short time all atom molecular dynamics simulations. However, our coarse grained force field will require further refinement to improve agreement with and persistence of native-like structures. In addition, the root mean square fluctuations (RMSFs) relative to the average structures derived from the simulations show that the conformational fluctuations of the proteins can be sampled.

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Significance Protein−protein interfaces have become an emerging class of molecular targets for the design of therapeutic drugs. However, major challenges exist for the correct identification of binding sites on the protein surface as well as drug-like modulators of protein−protein interaction. An integrated approach using molecular fragment docking and coevolutionary analysis is presented to face these challenges. This approach can accurately predict and characterize the binding sites for protein−protein interactions as well as provide clusters of bound, fragment-sized molecules on the druggable regions of the predicted binding site. These bound, molecular fragments can be chemically combined to create candidate drugs. Protein−protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein−protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

BackgroundConformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.ResultsOur analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.ConclusionsBy incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.

Gambogic acid identifies an isoform-specific druggable pocket in the middle domain of Hsp90β

Significance The molecular chaperone heat-shock protein 90 (Hsp90) is a key member of the cellular proteostasis network, and as such helps to protect cells against proteotoxic stress. Cancer cells have up-regulated members of this network, including Hsp90, to promote their survival and growth. Several Hsp90 inhibitors have undergone clinical trials, but these drugs, which bind to a shared nucleotide pocket in the N-terminal domain, do not differentiate between the four Hsp90 family members [Hsp90α, Hsp90β, GRP94 (glucose-regulated protein 94 kDa), and TRAP1 (tumor necrosis receptor-associated protein 1)]. In this report, we identify a pharmacophore contained within the natural product gambogic acid that binds uniquely to a site in Hsp90β, thus identifying this compound as a prototype of a new class of isoform-specific Hsp90 inhibitors. Because of their importance in maintaining protein homeostasis, molecular chaperones, including heat-shock protein 90 (Hsp90), represent attractive drug targets. Although a number of Hsp90 inhibitors are in preclinical/clinical development, none strongly differentiate between constitutively expressed Hsp90β and stress-induced Hsp90α, the two cytosolic paralogs of this molecular chaperone. Thus, the importance of inhibiting one or the other paralog in different disease states remains unknown. We show that the natural product, gambogic acid (GBA), binds selectively to a site in the middle domain of Hsp90β, identifying GBA as an Hsp90β-specific Hsp90 inhibitor. Furthermore, using computational and medicinal chemistry, we identified a GBA analog, referred to as DAP-19, which binds potently and selectively to Hsp90β. Because of its unprecedented selectivity for Hsp90β among all Hsp90 paralogs, GBA thus provides a new chemical tool to study the unique biological role of this abundantly expressed molecular chaperone in health and disease.

Interactions between mitoNEET and NAF-1 in cells

The NEET proteins mitoNEET (mNT) and nutrient-deprivation autophagy factor-1 (NAF-1) are required for cancer cell proliferation and resistance to oxidative stress. NAF-1 and mNT are also implicated in a number of other human pathologies including diabetes, neurodegeneration and cardiovascular disease, as well as in development, differentiation and aging. Previous studies suggested that mNT and NAF-1 could function in the same pathway in mammalian cells, preventing the over-accumulation of iron and reactive oxygen species (ROS) in mitochondria. Nevertheless, it is unknown whether these two proteins directly interact in cells, and how they mediate their function. Here we demonstrate, using yeast two-hybrid, in vivo bimolecular fluorescence complementation (BiFC), direct coupling analysis (DCA), RNA-sequencing, ROS and iron imaging, and single and double shRNA lines with suppressed mNT, NAF-1 and mNT/NAF-1 expression, that mNT and NAF-1 directly interact in mammalian cells and could function in the same cellular pathway. We further show using an in vitro cluster transfer assay that mNT can transfer its clusters to NAF-1. Our study highlights the possibility that mNT and NAF-1 function as part of an iron-sulfur (2Fe-2S) cluster relay to maintain the levels of iron and Fe-S clusters under control in the mitochondria of mammalian cells, thereby preventing the activation of apoptosis and/or autophagy and supporting cellular proliferation.