蛋白骨架在随机肽库构建及靶分子筛选中的应用*

自然界有着丰富的蛋白骨架来源。选择适宜的蛋白骨架和展示、筛选方法,可构建基于合理蛋白骨架结构的优化限制性随机肽库。与非限制性随机肽库相比,可望获得针对靶分子具有新功能的蛋白结构或更高亲和力的配体分子。目前,骨架蛋白限制的随机肽库已在高效靶分子筛选、基础研究、临床诊断和医学治疗等方面显示出巨大的潜在应用价值。以S-S限制性骨架、抗体分子、锌指蛋白、Z结构域、FN3结构域等为主要代表,综合介绍了蛋白骨架的结构基础、分类、基于蛋白骨架的限制性随机肽库构建及近年来在靶分子筛选等方面的应用最新进展。     

FISH and immunocytochemistry: towards visualising single target molecules in living cells

Knowledge of how molecules interact in space and time is crucial for understanding cellular processes. A host of novel techniques have been developed for the visualisation of single target molecules in living cells, many based on fluorescence in situ hybridisation (FISH) or immunocytochemistry (IC). To extend the applicability of FISH to living cells, special backbone-modified probes and specific conformations (molecular beacons) have been designed. In the case of IC, conventional immunoreagents have been fine-tuned with respect to size and affinity or replaced with new protein scaffolds based on ankyrin repeat proteins. Other key advances include the use of proximity ligation to confirm vicinity binding and the use of quantum dots, which have proven potential for cellular labelling.     

Ligand Discovery for the Alanine-Serine-Cysteine Transporter (ASCT2, SLC1A5) from Homology Modeling and Virtual Screening

The Alanine-Serine-Cysteine transporter ASCT2 (SLC1A5) is a membrane protein that transports neutral amino acids into cells in exchange for outward movement of intracellular amino acids. ASCT2 is highly expressed in peripheral tissues such as the lung and intestines where it contributes to the homeostasis of intracellular concentrations of neutral amino acids. ASCT2 also plays an important role in the development of a variety of cancers such as melanoma by transporting amino acid nutrients such as glutamine into the proliferating tumors. Therefore, ASCT2 is a key drug target with potentially great pharmacological importance. Here, we identify seven ASCT2 ligands by computational modeling and experimental testing. In particular, we construct homology models based on crystallographic structures of the aspartate transporter Glt_Ph in two different conformations. Optimization of the models’ binding sites for protein-ligand complementarity reveals new putative pockets that can be targeted via structure-based drug design. Virtual screening of drugs, metabolites, fragments-like, and lead-like molecules from the ZINC database, followed by experimental testing of 14 top hits with functional measurements using electrophysiological methods reveals seven ligands, including five activators and two inhibitors. For example, aminooxetane-3-carboxylate is a more efficient activator than any other known ASCT2 natural or unnatural substrate. Furthermore, two of the hits inhibited ASCT2 mediated glutamine uptake and proliferation of a melanoma cancer cell line. Our results improve our understanding of how substrate specificity is determined in amino acid transporters, as well as provide novel scaffolds for developing chemical tools targeting ASCT2, an emerging therapeutic target for cancer and neurological disorders.     

Conformational control in structure-based drug design

In structure-based drug design, the basic goal is to design molecules that fit complementarily to a given binding pocket. Since such computationally modeled molecules may not adopt the intended bound conformation outside the binding pocket, one challenge is to ensure that the designed ligands adopt similar low energy conformations both inside and outside of the binding pocket. Computational chemistry methods and conformational preferences of small molecules from PDB and Cambridge Structural Database (CSD) can be used to predict the bound structures of the designed molecules. Herein, we review applications of conformational control in structure-based drug design using selected examples from the recent medicinal chemistry literature. The main purpose is to highlight some intriguing conformational features that can be applied to other drug discovery programs.     

Target flexibility in molecular recognition

Induced-fit effects are well known in the binding of small molecules to proteins and other macromolecular targets. Among other targets, protein kinases are particularly flexible proteins, so that such effects should be considered in attempts at structure-based inhibitor design for kinase targets. This paper outlines some recent progress in methods for including target flexibility in computational studies of molecular recognition. A focus is the "relaxed complex method," in which ligands are docked to an ensemble of conformations of the target, and the best complexes are re-scored to provide predictions of optimal binding geometries. Early applications of this method have suggested a new approach to the development of inhibitors of HIV-1 Integrase.     

Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity

Background  

Virtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.     

Identification of type I and type II inhibitors of c-Yes kinase using in silico and experimental techniques

c-Yes kinase is considered as one of the attractive targets for anti-cancer drug design. The DFG (Asp-Phe-Gly) motif present in most of the kinases will adopt active and inactive conformations, known as DFG-in and DFG-out and their inhibitors are classified into type I and type II, respectively. In the present study, two screening protocols were followed for identification of c-Yes kinase inhibitors. (i) Structure-based virtual screening (SBVS) and (ii) Structure-based (SB) and Pharmacophore-based (PB) tandem screening. In SBVS, the c-Yes kinase structure was obtained from homology modeling and seven ensembles with different active site scaffolds through molecular dynamics (MD) simulations. For SB-PB tandem screening, we modeled ligand bound active and inactive conformations. Physicochemical properties of inhibitors of Src kinase family and c-Yes kinase were used to prepare target focused libraries for screenings. Our screening procedure along with docking showed 520 probable hits in SBVS and tandem screening (120 and 400, respectively). Out of 5000 compounds identified from different computational methods, 2410 were examined using kinase inhibition assays. It includes 266 compounds (5.32%) identified from our method. We observed that 14 compounds (12%) are identified by the present method out of 168 that showed > 30% inhibition. Among them, three compounds are novel, unique, and showed good inhibition. Further, we have studied the binding of these compounds at the DFG-in and DFG-out conformations and reported the probable class (type I or type II). Hence, we suggest that these compounds could be novel drug leads for regulation of colorectal cancer.     

Gaussian-weighted RMSD superposition of proteins: a structural comparison for flexible proteins and predicted protein structures

Many proteins contain flexible structures such as loops and hinged domains. A simple root mean square deviation (RMSD) alignment of two different conformations of the same protein can be skewed by the difference between the mobile regions. To overcome this problem, we have developed a novel method to overlay two protein conformations by their atomic coordinates using a Gaussian-weighted RMSD (wRMSD) fit. The algorithm is based on the Kabsch least-squares method and determines an optimal transformation between two molecules by calculating the minimal weighted deviation between the two coordinate sets. Unlike other techniques that choose subsets of residues to overlay, all atoms are included in the wRMSD overlay. Atoms that barely move between the two conformations will have a greater weighting than those that have a large displacement. Our superposition tool has produced successful alignments when applied to proteins for which two conformations are known. The transformation calculation is heavily weighted by the coordinates of the static region of the two conformations, highlighting the range of flexibility in the overlaid structures. Lastly, we show how wRMSD fits can be used to evaluate predicted protein structures. Comparing a predicted fold to its experimentally determined target structure is another case of comparing two protein conformations of the same sequence, and the degree of alignment directly reflects the quality of the prediction.     

Surface grafting onto template-assembled synthetic protein scaffolds in molecular recognition

Creating functional biological molecules de novo requires a detailed understanding of the intimate relationship between primary sequence, folding mechanism, and packing topology, and remains up to now a most challenging goal in protein design and mimicry. As a consequence, the use of well-defined robust macromolecules as scaffolds for the introduction of function by grafting surface residues has become a major objective in protein engineering and de novo design. In this article, the concept of scaffolds is demonstrated on some selected examples, illustrating that novel types of functional molecules can be generated. Reengineered proteins and, most notably, de novo designed peptide scaffolds exhibiting molecular function, are ideal tools for structure-function studies and as leads in drug design.     

Protein design with fragment databases

Structure-based computational methods are popular tools for designing proteins and interactions between proteins because they provide the necessary insight and details required for rational engineering. Here, we first argue that large-scale databases of fragments contain a discrete but complete set of building blocks that can be used to design structures. We show that these structural alphabets can be saturated to provide conformational ensembles that sample the native structure space around energetic minima. Second, we show that catalogs of interaction patterns hold the key to overcome the lack of scaffolds when computationally designing protein interactions. Finally, we illustrate the power of database-driven computational protein design methods by recent successful applications and discuss what challenges remain to push this field forward.     

Design of Benzene-1,2-diamines as selective inducible nitric oxide synthase inhibitors: a combined de novo design and docking analysis

Selective inhibition of inducible nitric oxide synthases (iNOS) has been a challenging problem for researchers pursuing work in finding methods to treat inflammatory disorders, shock, etc. Though many inhibitors have been studied to date, all are associated with selectivity or potency problems. Additionally, most of the reported compounds have several similarities and fewer number of novel structures are being tried. There is an increasing need to design novel molecules for this target. In this work, de novo design using LUDI, combined with docking analysis using FlexX has been employed in an attempt to identify novel scaffolds. Benzene-1,2-diamines were identified which could mimic the interactions of the substrate analogs and other inhibitors. Comparative docking scores in each of the isoforms of nitric oxide synthase were employed to recognize hits for iNOS selectivity. Figure Figure shows the docked poses of the ligand M226 along with that of the reference GW274150. (FlexX analysis)     

Improved prediction of RNA tertiary structure with insights into native state dynamics

The importance of RNA tertiary structure is evident from the growing number of published high resolution NMR and X-ray crystallographic structures of RNA molecules. These structures provide insights into function and create a knowledge base that is leveraged by programs such as Assemble, ModeRNA, RNABuilder, NAST, FARNA, Mc-Sym, RNA2D3D, and iFoldRNA for tertiary structure prediction and design. While these methods sample native-like RNA structures during simulations, all struggle to capture the native RNA conformation after scoring. We propose RSIM, an improved RNA fragment assembly method that preserves RNA global secondary structure while sampling conformations. This approach enhances the quality of predicted RNA tertiary structure, provides insights into the native state dynamics, and generates a powerful visualization of the RNA conformational space. RSIM is available for download from http://www.github.com/jpbida/rsim.     

Selective inhibitors of human mPGES-1 from structure-based computational screening

Human mPGES-1 is recognized as a promising target for next generation of anti-inflammatory drugs. Although various mPGES-1 inhibitors have been reported in literature, few have entered clinical trials and none has been proven clinically useful so far. It is highly desired for developing the next generation of therapeutics for inflammation-related diseases to design and discover novel inhibitors of mPGES-1 with new scaffolds. Here, we report the identification of a series of new, potent and selective inhibitors of human mPGES-1 with diverse scaffolds through combined computational and experimental studies. The computationally modeled binding structures of these new inhibitors of mPGES-1 provide some interesting clues for rational design of modified structures of the inhibitors to more favorably bind with mPGES-1.     

Illuminator: increasing synergies between medicinal and computational chemists

We present Illuminator, a user-friendly web front end to computational models such as docking and 3D shape similarity calculations. Illuminator was specifically created to allow non-experts to design and submit molecules to computational chemistry programs. As such it provides a simple user interface allowing users to submit jobs starting from a 2D structure. The models provided are pre-optimized by computational chemists for each specific target. We provide an example of how Illuminator was used to prioritize the design of molecular substituents in the Anadys HCV Polymerase (NS5B) project. With 7500 submitted jobs in 1.5 years, Illuminator has allowed project teams at Anadys to accelerate the optimization of novel leads. It has also improved communication between project members and increased demand for computational drug discovery tools.     

A conserved surface loop in type I dehydroquinate dehydratases positions an active site arginine and functions in substrate binding

Dehydroquinate dehydratase (DHQD) catalyzes the third step in the biosynthetic shikimate pathway. We present three crystal structures of the Salmonella enterica type I DHQD that address the functionality of a surface loop that is observed to close over the active site following substrate binding. Two wild-type structures with differing loop conformations and kinetic and structural studies of a mutant provide evidence of both direct and indirect mechanisms of involvement of the loop in substrate binding. In addition to allowing amino acid side chains to establish a direct interaction with the substrate, closure of the loop necessitates a conformational change of a key active site arginine, which in turn positions the substrate productively. The absence of DHQD in humans and its essentiality in many pathogenic bacteria make the enzyme a target for the development of nontoxic antimicrobials. The structures and ligand binding insights presented here may inform the design of novel type I DHQD inhibiting molecules.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Recent advances in drug design methods: Where will they lead?

Drug design methods have made significant new advances over the last ten years, mainly in the areas of molecular modelling. In more recent times important developments in theory have led to a different type of modelling becoming possible, the so-called de novo or automated design algorithms. In this new method the programs perform much of the chemist's thinking, in finding appropriately sized chemical groups to fit into a target site. However this is a combinatoric problem which has no general analytical solution; it is ripe for optimization. Other advances, such as combinatorial chemical synthesis and screening, will dramatically influence the search for new lead structures for target sites, which at present are poorly understood. Already these methods are being applied to peptide libraries. Peptides do not make good drug compounds because of their poor bioavailability; further, their flexibility reduces their affinity. In some cases peptide backbones can be removed and replaced with rigid non-peptide scaffolds.     

Identification of novel allosteric modulators for the G-protein coupled US28 receptor of human cytomegalovirus

The highly constitutively active G-protein coupled receptor US28 of human cytomegalovirus (HCMV) is an interesting pharmacological target because of its implication on viral dissemination, cardiovascular diseases and tumorigenesis. We found that dihydroisoquinolinone and tetrahydroisoquinoline scaffolds may be promising lead structures for novel US28 allosteric inverse agonists. These scaffolds were rapidly synthesized by radical carboamination reactions followed by non-radical transformations. Our novel US28 allosteric modulators provide valuable scaffolds for further ligand optimization and may be helpful chemical tools to investigate molecular mechanisms of US28 constitutive signaling and its role in pathogenesis.