核磁共振波谱在药物发现中的应用

核磁共振波谱通过检测组成有机化合物分子的原子核跃迁而得到反映核性质的参数以及周围化学环境对这些参数的影响规律. 这些参数的相关内容包含了极其详尽的有机化合物分子结构和分子间相互作用的信息,并构成了核磁共振结构解析和生物靶分子-配体相互作用研究的理论基础. 在生物医药研发领域内,科研院所和公司企业的研发工作者们一直在努力探索利用核磁共振波谱监测生物靶分子-配体相互作用作为药物发现工具的潜能. 本文旨在针对核磁共振波谱在药物发现过程中活性化合物筛选的最新研究进展进行综述.     

Cheminformatics analysis and learning in a data pipelining environment

Summary Workflow technology is being increasingly applied in discovery information to organize and analyze data. SciTegic's Pipeline Pilot is a chemically intelligent implementation of a workflow technology known as data pipelining. It allows scientists to construct and execute workflows using components that encapsulate many cheminformatics based algorithms. In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning. Case studies are described showing the application of these methods to the analysis of discovery data such as chemical series and high throughput screening results. The paper demonstrates that the methods are well suited to a wide variety of tasks such as building and applying predictive models of screening data, identifying molecules for lead optimization and the organization of molecules into families with structural commonality.     

Binding site characterization of G protein-coupled receptor by alanine-scanning mutagenesis using molecular dynamics and binding free energy approach: application to C-C chemokine receptor-2 (CCR2)

The C-C chemokine receptor 2 (CCR2) was proved as a multidrug target in many diseases like diabetes, inflammation and AIDS, but rational drug design on this target is still lagging behind as the information on the exact binding site and the crystal structure is not yet available. Therefore, for a successful structure-based drug design, an accurate receptor model in ligand-bound state is necessary. In this study, binding-site residues of CCR2 was determined using in silico alanine scanning mutagenesis and the interactions between TAK-779 and the developed homology model of CCR2. Molecular dynamic simulation and Molecular Mechanics-Generalized Born Solvent Area method was applied to calculate binding free energy difference between the template and mutated protein. Upon mutating 29 amino acids of template protein and comparison of binding free energy with wild type, six residues were identified as putative hot spots of CCR2.     

Towards Control of Aggregational Behaviour of α-Lactalbumin at Acidic pH

α-Lactalbumin (α-La) undergoes considerable structural changes upon loss of bound Ca²⁺ at acidic pH, leaving α-La in a molten globule structure. Using fluorescence the present work provides more insight into the structural transition of α-La at acidic pH leading to protein aggregation, most likely caused by a combination of hydrophobic and electrostatic interactions. The rate of aggregation is determined by the protein concentration and temperature applied. Availability of Ca²⁺ stabilises the protein, and thus prevent aggregation at pH values as low as pH 2.9. In contrast, presence of Cu²⁺ induces a destabilisation of the protein, which can be explained by a binding to the Zn²⁺ binding site in α-La, possibly resulting in structural alterations of the protein. In general, presence of anions destabilise α-La at pH values below pI, with SO₄ ²⁻ exhibiting the strongest effect on the protein stability, thus correlating well with the Hofmeister series. At more acidic pH values far from pI, α-La becomes more stable towards ion induced aggregation, since higher ion activity is required to efficiently screen the charges on the protein surface. The results presented in this paper provide detailed knowledge on the external parameters leading to aggregation of α-La at acidic pH, thus permitting rational design of the aggregation process.     

A study of the structural and thermodynamic properties of water by the molecular dynamics method

The thermodynamic and structural properties of four rigid water models were studied by the molecular dynamics method over a wide temperature range. Two three-center (SPC/E and TIP3P) and two five-center (ST4 and TIP5P) models were considered. The results discussed include the boiling and condensation temperatures, VT phase transition diagrams, three-dimensional spatial distributions of atoms, the temperature dependences of the total energy, density, heat capacity, the number of H-bonds per molecule, the distribution of H-bonds over the ∠HOO angle, the self-diffusion coefficient, and the radial distribution functions. The boiling points of all the models did not correspond to 100°C and were noticeably different from each other. The condensation points were also different. The data on the structural parameters led us to conclude that the TIP5P model reproduced the local structure of water most correctly. However, if the reproduction of the local structure is not a necessary condition, less resource consuming three-center models can be used.     

An integrated drug-likeness study for bicyclic privileged structures: from physicochemical properties to in vitro ADME properties

The concept of drug-likeness has been widely applied in combinatorial chemistry as an approach to reduce attrition in drug discovery and development. Meanwhile, bicyclic privileged structures with versatile binding properties have emerged as ideal source of core scaffolds for the design and synthesis of combinatorial libraries. For the purpose of better assisting the design of bicyclic privileged structure-based combinatorial libraries, we conducted an integrated drug-likeness study on compounds of these scaffolds. Distributions of physicochemical properties (PCPs) were analyzed and in silico prediction models were built. Our results showed that there exist much difference between the drug-like ranges (DLRs) of bicyclic privileged structures and that of others, which have significant impact on compound selection. The DLRs for bicyclic privileged structures were defined as 260 ≤ MW ≤ 524; 0.9 ≤ ALogP ≤ 5.4; 2 ≤ Hacc ≤ 8; Hdon ≤ 3; 21.0 ≤ PSA ≤ 128.6; 6.3 ≤ FPSA ≤ 34.2; 1 ≤ RotB ≤ 10; 2 ≤ Nr ≤ 5; 1 ≤ Nc ≤ 7; SA ≤ 4. Two accurate and easy to understand in silico prediction models, Caco-2 permeability model and metabolic stability classification model, had been built to guide drug candidate optimization. In these models, hydrogen-bond donor and rotatable bond showed major impact on the permeability of compounds, while lipophilicity, flexibility, degree of branching and the existence of some functional groups determined the fate of a drug in metabolic process. Suggestions on structural modification toward higher permeability and metabolic stability were given according to the in silico models.     

CORCEMA refinement of the bound ligand conformation within the protein binding pocket in reversibly forming weak complexes using STD-NMR intensities

We describe an intensity-restrained optimization procedure for refining approximate structures of ligands within the protein binding pockets using STD-NMR intensity data on reversibly forming weak complexes. In this approach, the global minimum for the bound-ligand conformation is obtained by a hybrid structure refinement method involving CORCEMA calculation of intensities and simulated annealing optimization of torsion angles of the bound ligand using STD-NMR intensities as experimental constraints and the NOE R-factor as the pseudo-energy function to be minimized. This method is illustrated using simulated STD data sets for typical carbohydrate and peptide ligands. Our procedure also allows for the optimization of side chain torsion angles of protein residues within the binding pocket. This procedure is useful in refining and improving initial models based on crystallography or computer docking or other algorithms to generate models for the bound ligand (e.g., a lead compound) within the protein binding pocket compatible with solution STD-NMR data. This method may facilitate structure-based drug design efforts.     

Combining 2D and 3D in silico methods for rapid selection of potential PDE5 inhibitors from multimillion compounds’ repositories: biological evaluation

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach when starting new drug discovery projects. If structures of active compounds are available rapid 2D similarity search can be performed on multimillion compounds’ databases. This in silico approach can be combined with physico-chemical parameter filtering based on the property space of the active compounds and 3D virtual screening if the structure of the target protein is available. A multi-step virtual screening procedure was developed and applied to select potential phosphodiesterase 5 (PDE5) inhibitors in real time. The combined 2D/3D in silico method resulted in the identification of 14 novel PDE5 inhibitors with <1 μMIC₅₀ values and the hit rate in the second in silico selection and in vitro screening round exceeded the 20%.     

On the industrial applications of MCRs: molecular diversity in drug discovery and generic drug synthesis

During the last decades, multicomponent chemistry has gained much attention in pharmaceutical research, especially in the context of lead finding and optimization. Here, in particular, the main advantages of multicomponent reactions (MCRs) like ease of automation and high diversity generation were utilized. In consequence of these beneficial properties, a plethora of new MCRs combined with appropriate classical reaction sequences have been published, the accessible chemical space was extended steadily. In the meantime, the desired high diversity became a challenge itself, because by now the systematic use of this huge and unmanageable space for drug discovery was limited by the lack of suitable computational tools. Therefore, this article provides an insight for the rational use of this enormous chemical space in drug discovery and generic drug synthesis. In this context, a short overview of the applied chemo informatics, necessary for the virtual screening of the biggest available chemical space, is given. Furthermore, some examples for recently developed multicomponent sequences are presented.     

Focused enumeration and assessing the structural diversity of scaffold libraries: conformationally restricted bicyclic secondary diamines

Comprehensive enumeration of conformationally restricted bicyclic secondary diamines (CRDA) was performed within defined structural limits, yielding a library of all theoretically possible compounds of this class, potentially useful as building blocks for drug design. In order to assess structural diversity of the generated library, molecular geometries of the library members were optimized using DFT calculations. It was shown that the distance between the amino groups and their relative orientation in space vary widely over the whole library, which might be beneficial for diversity-oriented conformational restriction approach in drug discovery. There are many representatives of "three-dimensional" scaffolds in the CRDA library. Selected literature data on biological activity of the known CRDA derivatives were discussed, demonstrating utility of the CRDA scaffold hopping in drug design.     

Discovery of enzyme inhibitors through combinatorial chemistry

Summary This review serves to highlight the recent examples of combinatoric methodology as applied to the discovery and optimization of enzyme inhibitors. Early research efforts focused on the identification of polypeptides from libraries as inhibitors of proteases. As solution- and solid-phase chemistries gain in sophistication, libraries containing less peptidic structural motifs have been created. A recurring design stratagem relies on the synthesis of libraries incorporating pharmacophores with known affinity for the target enzyme. Screening of these structure-based libraries has led to the discovery of small-molecule inhibitors of both proteolytic and non-proteolytic enzymes alike. Two tables are provided listing the enzyme targeted libraries through 1996. A name, generic structure and size is given for each library citation, accompanied by the enzyme screen and the structure and potency of the most active library member.     

Towards a high-accuracy kinetic database informed by theoretical and experimental data: CH3 + HO2 as a case study

The reliability of predictive simulations for advanced combustion engines depends on the availability of accurate data and models for thermochemistry, chemical kinetics, and transport. In that regard, accurate data are critically important for both their direct use in predictive simulations and for benchmarking improved theoretical methodologies that can similarly produce accurate data for predictive simulations. The use of informatics-based strategies for the determination of accurate thermochemical data with well-defined uncertainties, e.g. the Active Thermochemical Tables (ATcT), has revolutionized the field of thermochemistry–providing thermochemical data of unprecedented accuracy for predictive combustion simulations and has served as a key enabler of ab initio electronic structure methodologies of equally impressive accuracy. Clearly, pursuit of informatics-based analogs in chemical kinetics would be similarly worthwhile. Here, we present results and analyses for the kinetics of CH₃ + HO₂ reactions that demonstrate some key elements of any approach to developing analogs for kinetics, including: the importance of raw data for quantifying the information content of experimental data, the utility of theoretical kinetics calculations for constraining experimental interpretations and providing an independent data source, and the subtleties of target data selection for avoiding unphysical parameter adjustments to match data affected by structural uncertainties. Notably, we find the optimization performed here using the MultiScale Informatics (MSI) approach applied to carefully selected (mostly raw) experimental data yields an opposite temperature dependence for the channel-specific CH₃ + HO₂ rate constants as compared to a previous rate-parameter optimization. While both optimization studies use the same theoretical calculations to constrain model parameters, only the present optimization, which incorporates theory directly into the model structure, yields results that are consistent with theoretical calculations.     

Substructure methods for structural condition assessment

It is commonly known that an accurate analysis of a large structure requires an accurate analytical model. This is also true for the inverse analysis of a structural system where measured structural responses are used as input to assess the structural conditions. However, an accurate model of the structure is always not available in practice. Two substructural identification methods are presented in this paper with the structure divided into substructures and with one substructure assessed at one time. In the first method, an accurate finite element model of the whole structure is assumed known. A state space method is applied to identify the external forces acting on the structure, and a damage identification method is then applied to identify the local damages using time domain information. Iterative model updating method based on the measured acceleration in the selected substructure is employed for the assessment. The second identification method requires only the finite element model of the substructure. The interface forces and the external forces acting on the target substructure are all taken as excitations and they are identified in state space. The substructure is then assessed similar to the first method. Since the target substructure for updating consists of a much reduced number of components and the identification problem is more efficient. The validation of the proposed methods is demonstrated by a truss structure with polluted measured accelerations with promising results.     

Learning to design resistance proof drugs from folding

Learning how proteins fold will hardly have any impact on the way conventional — active site centered — drugs are designed. On the other hand, this knowledge is proving instrumental in defining a new paradigm for the identification of drugs against any target protein: folding inhibition. Targeting folding renders drugs less susceptible to spontaneous genetic mutations that in many cases, notably in connection with retroviruses like the Human Immunodeficiency Virus (HIV), can abrogate drug effect. The progress which has taken place during the last years to understand which are the sequences of amino acids which code for a protein, and how to read from these sequences the associated three-dimensional, biologically active, native structure, constitutes the main subject of the present paper. From this narrative the idea of folding inhibitors emerges both naturally and, to some extent, inescapably.     

γ-graphyne and its boron nitride analogue as nanocarriers for anti-cancer drug delivery

Presently, many studies are directed toward the design of new drug delivery systems. Inspired by a fascinating finding of a new carbon allotrope, namely graphyne (GY), we suggest the pristine and BN analogue of GY (BNY) nanosheets in the drug delivery applications. The purpose of the present study is to investigate the interaction of an anti-cancer drug (hydroxyurea (HU)) with GY and BNY nanosheets by means of the density functional theory (DFT). Results show that the GY nanosheet with B and N atoms could remarkably increase the tendency of nanosheet for adsorption of HU drug. Also, our ultraviolet-visible results show that the electronic spectra of the drug/nanosheet complexes exhibit a red shift toward higher wavelengths (lower energies). It was found that the HU/BNY had high chemical reactivity, which was important for binding of the drug onto the target site. In order to go further and gain insight into the binding features of considered systems with HU drug, the Atoms in Molecules (AIM) analysis was performed. Our results determine the strong interaction features of the HU/BNY bonding. Consequently, the present study demonstrated that the BNY could be used as potential carrier for delivery of HU drug.     

Aluminum-assisted crystallization and <Emphasis Type="Italic">p</Emphasis>-type doping of polycrystalline Si

Aluminum-doped p-type polycrystalline silicon thin films have been synthesized on glass substrates using an aluminum target in a reactive SiH₄+Ar+H₂ gas mixture at a low substrate temperature of 300 °C through inductively coupled plasma-assisted RF magnetron sputtering. In this process, it is possible to simultaneously co-deposit Si–Al in one layer for crystallization of amorphous silicon, in contrast to the conventional techniques where alternating metal and amorphous Si layers are deposited. The effect of aluminum target power on the structural and electrical properties of polycrystalline Si films is analyzed by X-ray diffraction, Raman spectroscopy, scanning electron microscopy and Hall-effect analysis. It is shown that at an aluminum target power of 100 W, the polycrystalline Si film features a high crystalline fraction of 91%, a vertically aligned columnar structure, a sheet resistance of 20.2 kΩ/□ and a hole concentration of 6.3×10¹⁸ cm⁻³. The underlying mechanism for achieving the semiconductor-quality polycrystalline silicon thin films at a low substrate temperature of 300 °C is proposed.     

Raman spectroscopy in investigations of secondary structure of human serum albumin at binding of nanomarkers of fluorescein family

The influence of binding of nanomarkers of fluorescein family to HSA on secondary structure of this protein at different values of pH was investigated by Raman spectroscopy method. The greatest changes in secondary structure of HSA, consisting in decreasing of α-helix sites, at binding of fluorescein to HSA occur at pH 5–6. The greatest changes in secondary structure of HSA, consisting in decreasing of α-helix sites, at binding of eosin or erythrosin to HSA take place at values of pH, smaller 5. The differences in changes in secondary structure of HSA at binding of these three nanomarkers are explained by dependences of binding of nanomarkers to HSA on pH which determined by value of electronegativity of atoms of lateral radicals in structural formulas of nanomarkers and, therefore, by value of pK of their ionized groups.     

Fibrinogen-β-Estradiol Binding Studied by Fluorescence Spectroscopy: Denaturation and pH Effects

Fibrinogen is a blood plasma protein that plays a crucial role in hemostasis. It is known that erythrocyte aggregation increases in the presence of fibrinogen, and that β-estradiol decreases erythrocyte aggregation with a constant fibrinogen concentration. In this work, we have used intrinsic tryptophan fluorescence to obtain information on the conformational changes of fibrinogen upon the recently proposed interaction with β-estradiol. To evaluate the effect on the conformational changes during fibrinogen-β-estradiol binding, fluorescence experiments were performed using guanidine hydrochloride (0–6 M) as denaturant, at different pH values. The results obtained for pH 6.5 and 8.0 showed no effect during the binding. The main differences were observed between pH 4.2 and 7.4, in the absence and in the presence of two different denaturant concentrations (1 and 5 M). A red shift of the fluorescence emission from 344 to 354 nm is observed when denaturant concentration is above 3 M for all studied pH values. This phenomenon may be explained by the loss of compact structure of the protein in the presence of denaturant, with tryptophan residues exposure to the aqueous environment and alteration of fibrinogen-β-estradiol binding. These results demonstrate that the binding sites of fibrinogen are strongly dependent on the conformational state of the protein.     

Nanostructure Empowers Active Tumor Targeting in Ligand‐Based Molecular Delivery

Cell‐selective targeting is expected to enhance effectiveness and minimize side effects of cytotoxic agents. Functionalization of drugs or drug nanoconjugates with specific cell ligands allows receptor‐mediated selective cell delivery. However, it is unclear whether the incorporation of an efficient ligand into a drug vehicle is sufficient to ensure proper biodistribution upon systemic administration, and also at which extent biophysical properties of the vehicle may contribute to the accumulation in target tissues during active targeting. To approach this issue, structural robustness of self‐assembling, protein‐only nanoparticles targeted to the tumoral marker CXCR4 is compromised by reducing the number of histidine residues (from six to five) in a histidine‐based architectonic tag. Thus, the structure of the resulting nanoparticles, but not of building blocks, is weakened. Upon intravenous injection in animal models of human CXCR4⁺ colorectal cancer, the administered material loses the ability to accumulate in tumor tissue, where it is only transiently found. It instead deposits in kidney and liver. Therefore, precise cell‐targeted delivery requires not only the incorporation of a proper ligand that promotes receptor‐mediated internalization, but also, unexpectedly, its maintenance of a stable multimeric nanostructure that ensures high ligand exposure and long residence time in tumor tissue.