NIR-laser-triggered gadolinium-doped carbon dots for magnetic resonance imaging, drug delivery and combined photothermal chemotherapy for triple negative breast cancer.

BACKGROUND: Owing to high genetic diversities of tumor cells and low response rate of standard chemotherapy, patients with triple negative breast cancer (TNBC) have short progression-free survivals and poor outcomes, which need to explore an effective approach to improve therapeutic efficacy. METHODS: Novel gadolinium doped carbon dots (Gd@CDs) have been designed and prepared through hydrothermal method with 3,4-dihydroxyhydrocinnamic acid, 2,2'-(ethylenedioxy)bis(ethylamine) and gadolinium chloride. The synthesized nanostructures were characterized. Taking advantage of good biocompatibility of Gd@CDs, a nanoplatform based on Gd@CDs has been developed to co-deliver chemotherapy drug doxorubicin hydrochloride (Dox) and a near-infrared (NIR) photothermal agent, IR825 for magnetic resonance imaging (MRI) guided photothermal chemotherapy for TNBC. RESULTS: The as-synthesized Dox@IR825@Gd@CDs displayed favorable MRI ability in vivo. Upon NIR laser irradiation, Dox@IR825@Gd@CDs could convert the NIR light to heat and efficiently inhibit tumor growth through photothermal chemotherapy in vitro and in vivo. Additionally, the impact of photothermal chemotherapy on the murine motor coordination was assessed by rotarod test. Dox@IR825@Gd@CDs presented low toxicity and high photothermal chemotherapy efficiency. CONCLUSION: A noble theranostic nanoplatform (Dox@IR825@Gd@CDs) was developed that could be tailored to achieve loading of Dox and IR825, intracellular delivery, favorable MRI, excellent combination therapy with photothermal therapy and chemotherapy to enhance therapeutic effect against TNBC cells. This study will provide a promising strategy for the development of Gd-based nanomaterials for MRI and combinational therapy for TNBC.

Enhanced production of optical (S)-acetoin by a recombinant Escherichia coli whole-cell biocatalyst with NADH regeneration.

Acetoin is an important platform chemical with a variety of applications in foods, cosmetics, chemical synthesis, and especially in the asymmetric synthesis of optically active pharmaceuticals. It is also a useful breath biomarker for early lung cancer diagnosis. In order to enhance production of optical (S)-acetoin and facilitate this building block for a series of chiral pharmaceuticals derivatives, we have developed a systematic approach using in situ-NADH regeneration systems and promising diacetyl reductase. Under optimal conditions, we have obtained 52.9 g L-1 of (S)-acetoin with an enantiomeric purity of 99.5% and a productivity of 6.2 g (L h)-1. The results reported in this study demonstrated that the production of (S)-acetoin could be effectively improved through the engineering of cofactor regeneration with promising diacetyl reductase. The systematic approach developed in this study could also be applied to synthesize other optically active α-hydroxy ketones, which may provide valuable benefits for the study of drug development.

Recent development of peptide drugs and advance on theory and methodology of peptide inhibitor design.

Due to the low toxicity, easy synthesis, rapid elimination, and less side effect, more and more peptide inhibitors are emerging as the effective drugs that are clinically used in therapies of a number of diseases. At the same time the computer-aided drug design (CADD) methods have remarkably developed. In this mini review the newly developed peptide inhibitors and drugs are introduced, including peptide vaccines for cancers, peptide inhibitors for HIV, Alzheimer's disease and related diseases, and the peptides as the leading compounds of drugs. The recent progress in the theory and methodology of peptide inhibitor design is reviewed. (1) The flexible protein-peptide docking model is introduced, in which the peptide structures are treated as segment-flexible chains using genetic algorithm and special force field parameters. (2) The "Wenxiang diagram" is illustrated for protein-peptide interaction analysis that has been successfully used in the coiled-coil interaction analysis. (3) The "Distorted key" theory is reviewed, which is an effective method to convert the peptide inhibitors to the small chemical drugs. (4) The amino acid property-based peptide prediction method (AABPP) is described that is a twolevel QSAR prediction network for the bioactivity prediction of peptide inhibitors. (5) Finally, several types of molecular interactions between protein and peptide ligands are summarized, including cation-π interactions; polar hydrogen-π interactions; and π-π stocking interactions.

Insight into a molecular interaction force supporting peptide backbones and its implication to protein loops and folding.

Although not being classified as the most fundamental protein structural elements like α-helices and ß-strands, the loop segment may play considerable roles for protein stability, flexibility, and dynamic activity. Meanwhile, the protein loop is also quite elusive; i.e. its interactions with the other parts of protein as well as its own shape-maintaining forces have still remained as a puzzle or at least not quite clear yet. Here, we report a molecular force, the so-called polar hydrogen-π interaction (Hp-π), which may play an important role in supporting the backbones of protein loops. By conducting the potential energy surface scanning calculations on the quasi π-plane of peptide bond unit, we have observed the following intriguing phenomena: (1) when the polar hydrogen atom of a peptide unit is perpendicularly pointing to the π-plane of other peptide bond units, a remarkable Hp-π interaction occurs; (2) the interaction is distance and orientation dependent, acting in a broad space, and belonging to the 'point-to-plane' one. The molecular force reported here may provide useful interaction concepts and insights into better understanding the loop's unique stability and flexibility feature, as well as the driving force of the protein global folding.

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.

Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design.

This review is to summarize three new QSAR (quantitative structure-activity relationship) methods recently developed in our group and their applications for drug design. Based on more solid theoretical models and advanced mathematical techniques, the conventional QSAR technique has been recast in the following three aspects. (1) In the fragment-based two dimensional QSAR, or abbreviated as FB-QSAR, the molecular structures in a family of drug candidates are divided into several fragments according to the substitutes being investigated. The bioactivities of drug candidates are correlated with physicochemical properties of the molecular fragments through two sets of coefficients: one is for the physicochemical properties and the other for the molecular fragments. (2) In the multiple field three dimensional QSAR, or MF-3D-QSAR, more molecular potential fields are integrated into the comparative molecular field analysis (CoMFA) through two sets of coefficients: one is for the potential fields and the other for the Cartesian three dimensional grid points. (3) In the AABPP (amino acid-based peptide prediction), the bioactivities of peptides or proteins are correlated with the physicochemical properties of all or partial residues of the sequence through two sets of coefficients: one is for the physicochemical properties of amino acids and the other for the weight factors of the residues. Meanwhile, an iterative double least square (IDLS) technique is developed for solving the two sets of coefficients in a training dataset alternately and iteratively. Using the two sets of coefficients, one can predict the bioactivity of a query peptide, protein, or drug candidate. Compared with the old methods, the new QSAR approaches as summarized in this review possess machine learning ability, can remarkably enhance the prediction power, and provide more structural information. Meanwhile, the future challenge and possible development in this area have been briefly addressed as well.

Predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201: an application of amino acid-based peptide prediction.

A new peptide design strategy, the amino acid-based peptide prediction (AABPP) approach, is applied for predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201. The AABPP approach consists of two sets of predictive coefficients. The former is the coefficients for the physicochemical properties of amino acids and the latter is the weight factors for the residue positions in a peptide sequence. An iterative double least square technique is introduced to determine the two sets of coefficients alternately through a benchmark dataset. The coefficients converged through such an iterative process are further used to predict the bioactivities of query peptides. In the AABPP algorithm, the following eight physicochemical properties are used as the descriptors of amino acids: (i) lipophilic indices, (ii) hydrophilic indices, (iii) lipophilic surface area, (iv) hydrophilic surface area, (v) alpha-potency indices, (vi) beta-potency indices, (vii) coil-potency indices and (viii) volume of amino acid side chains. In comparison with the existing methods in this area, a remakable advantage of the current approach is that there is no need to know the exact conformation of a query peptide and its alignment with a template. The two steps are indispensable but cannot always be successfully realized otherwise. It is anticipated that the AABPP approach will become a powerful tool for peptide drug design, or at least play a complemetary role to the existing methods.

Peptide reagent design based on physical and chemical properties of amino acid residues.

It has tremendous values for both drug discovery and basic research to develop a solid bioinformatical tool for guiding peptide reagent design. Based on the physical and chemical properties of amino acids, a new strategy for peptide reagent design, the so-called AABPD (amino acid based-peptide design), is proposed. The peptide samples in a training dataset are described by a series of HMLP (heuristic molecular lipophilicity potential) parameters and other physicochemical properties of amino acid residues that form a three-dimensional data matrix where each component is defined by three indexes: the first index refers to the peptide samples, the second to the amino acid positions, and the third to the amino acid parameters. The binding free energy between a peptide ligand and its protein receptor is calculated by a linear free energy equation through the physicochemical parameters, resulting in a set of simultaneous linear equations between the bioactivity of the peptides and the physicochemical properties of amino acids. An iterative double least square technique is developed for the solution of the three-dimensional simultaneous linear equation set to determine the amino acid position coefficients of peptide sequence and the physicochemical parameter coefficients of amino acid residues alternately. The two sets of coefficients thus obtained are used for predicting the bioactivity of other query peptide reagents. Two calculation examples, the peptide substrate specificity of the SARS coronavirus 3C-like proteinase and the affinity prediction for epitope-peptides with Class I MHC molecules are studied by using the peptide reagent design strategy.

Inhibitor design for SARS coronavirus main protease based on "distorted key theory".

In order to find effective peptide inhibitors against SARS CoV M(pro), an analysis was performed for 11 oligo-peptides that can be cleaved by the SARS coronavirus main protease (CoV M(pro), or 3CL(pro)). Flexible molecular alignments of the 11 cleavable peptides have provided useful insights into the chemical properties of their amino acid residues close to the cleavage site. Moreover, it was found through the ligand-receptor docking studies that of the 11 cleavable peptides, NH2-ATLQ / AIAS-COOH and NH2-ATLQ / AENV-COOH had the highest affinity with SARS CoV M(pro). The two octapeptides were selected as initial templates for further chemical modification to make them become effective inhibitors against the SARS enzyme according to the "distorted key" theory [K. C. Chou, Analytical Biochemistry 233 (1996) 1-14]. The possible chemical modification methods are proposed and examined. The approach developed in this study and the findings thus obtained might stimulate new strategies and provide useful information for drug design against SARS.

Progress in computational approach to drug development against SARS.

Since the outbreak of SARS (severe acute respiratory syndrome) in November 2002 in Southern China's Guangdong Province, considerable progress has been made in the development of drugs for SARS therapy. The present mini review is focused on the area of computer-aided drug discovery, i.e., the advances achieved mainly from the approaches of structural bioinformatics, pharmacophore modeling, molecular docking, peptide-cleavage site prediction, and other computational means. It is highlighted that the compounds C(28)H(34)O(4)N(7)Cl, C(21)H(36(O)5)N(6) and C(21)H(36)O(5)N(6) (Wei et al., Amino Acids, 2006, 31: 73-80), as well as KZ7088 (Chou et al. Biochem. Biophys. Res. Commun., 2003, 308: 148-151), a derivative of AG7088, might be the promising candidates for further investigation, and that the octapeptides ATLQAIAS and ATLQAENV, as well as AVLQSGFR, might be converted to effective inhibitors against the SARS enzyme. Meanwhile, how to modify these octapeptides based on the "distorted key" theory to make them become potent inhibitors is explicitly elucidated. Finally, a brief introduction is given for how to use computer-generated graphs to rapidly diagnose SARS coronavirus.

Molecular modeling studies of peptide drug candidates against SARS.

Flexible alignment and docking studies were conducted for the three octapeptides, ATLQANEV, AVLQSGFR, and ATLQAIAS, that were cleavable by SARS-CoV Mpro. It has been observed that all pharmacophores of the three peptides overlap very well, and that ATLQANEV binds best with the receptor, followed by AVLQSGFR, and then ATLQAIAS. During the process of docking the octapeptides to the SARS enzyme, the residues of the catalytic dyad, i.e., His-41 and Cys-145 are actively involved in forming the hydrogen bonds, so is the center residue (Gln) of all the three octapeptides. The findings are fully consistent with experimental observations. The present studies suggest that the octapeptides ATLQANEV and ATLQAIAS, like AVLQSGFR, might also be the good starting points for designing potential drugs against SARS.

Application of bioinformatics in search for cleavable peptides of SARS-CoV M(pro) and chemical modification of octapeptides.

According to the "distorted key" theory as elaborated in a review article years ago (Chou, K.C.: Analytical Biochemistry, 1996, 233, 1-14), the knowledge of the cleavable peptides by SARS-CoV M(pro) (severe acute respiratory syndrome coronavirus main proteinase) can provide very useful insights on developing drugs against SARS. In view of this, the softwares, ZCURVE_CoV 1.0 and ZCURVE_CoV 2.0 (http://tubic.tju.edu.cn/sars/), developed recently for SARS-Coronavirus are used to analyze the 36 complete SARS-Coronavirus RNA sequences in the gene bank NCBI (http://www.ncbi.nlm.nih.gov/) from different sources for protein coding genes, and to search for the cleavage sites of SARS-CoV M(pro) in polyproteins pp1a and pp1ab. A total of 396 cleavage points are found in the 36 SARS-Coronavirus and 11 cleavable octapeptides abstracted from the 396 cleavage sites. The statistical distributions of amino acids for the cleavable octapeptides at the subsites R4, R3, R2, R1, R1', R2', R3' and R4' are calculated. The cleavage-specific positions are on R2, R1 and R1', and the positions R3 and R4 are featured by some certain specificity for SARS-CoV M(pro). The structural characters of amino acid residues around the cleavage-specific positions are discussed. Two most promising octapeptides, i.e., NH(2)-ATLQ downward arrowAIAS-COOH and NH(2)-ATLQ downward arrowAENV-COOH, are selected to be the candidates for chemical modification, converting into the inhibitors of SARS-CoV M(pro). A possible strategy to convert a cleavable octapeptide by SARS enzyme into a drug candidate against SARS is elucidated.

Polyprotein cleavage mechanism of SARS CoV Mpro and chemical modification of the octapeptide.

The cleavage mechanism of severe acute respiratory syndrome (SARS) coronavirus main proteinase (M(pro) or 3CL(pro)) for the octapeptide AVLQSGFR is studied using molecular mechanics (MM) and quantum mechanics (QM). The catalytic dyad His-41 and Cys-145 in the active pocket between domain I and II seem to polarize the pi-electron density of the peptide bond between Gln and Ser in the octapeptide, leading to an increase of positive charge on C(CO) of Gln and negative charge on N(NH) of Ser. The possibility of enhancing the chemical bond between Gln and Ser based on the "distorted key" theory [Anal. Biochem. 233 (1996) 1] is examined. The scissile peptide bond between Gln and Ser is found to be solidified through "hybrid peptide bond" by changing the carbonyl group CO of Gln to CH(2) or CF(2). This leads to a break of the pi-bond system for the peptide bond, making the octapeptide (AVLQSGFR) a "distorted key" and a potential starting system for the design of anti SARS drugs.

Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points.

Pharmacophore modeling can provide valuable insight into ligand-receptor interactions. It can also be used in 3D (dimensional) database searching for potentially finding biologically active compounds and providing new research ideas and directions for drug-discovery projects. To stimulate the structure-based drug design against SARS (severe acute respiratory syndrome), a pharmacophore search was conducted over 3.6 millions of compounds based on the atomic coordinates of the complex obtained by docking KZ7088 (a derivative of AG7088) to SARS CoV M(pro) (coronavirus main proteinase), as reportedly recently (Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Biochem. Biophys. Res. Commun. 2003, 308, 148-151). It has been found that, of the 3.6 millions of compounds screened, 0.07% are with the score satisfying five of the six pharmacophore points. Moreover, each of the hit compounds has been evaluated for druggability according to 13 metrics based on physical, chemical, and structural properties. Of the 0.07% compounds thus retrieved, 17% have a perfect score of 1.0; while 23% with one druggable rule violation, 13% two violations, and 47% more than two violations. If the criterion for druggability is set at a maximum allowance of two rule violations, we obtain that only about 0.03% of the compounds screened are worthy of further tests by experiments. These findings will significantly narrow down the search scope for potential compounds, saving substantial time and money. Finally, the featured templates derived from the current study will also be very useful for guiding the design and synthesis of effective drugs for SARS therapy.