Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.     

药物重定位的计算分析方法

全新结构药物的研发存在周期长、耗资大、风险高的问题.通过各种技术预测已有药物的新适应症,即药物重定位,可以缩短药物研发时间、降低研发成本和风险.由于疾病种类和已知药物的数量繁多,完全通过实验筛选已知药物的新用途仍然具有很高的成本.随着组学和药物信息学数据的积累,药物重定位进入到了理性设计和实验筛选相结合的阶段,药物重定位的计算预测已经成为计算生物学和系统生物学的重要研究方向.本文将目前药物重定位计算分析的策略归纳为药物-靶标关系分析、药物-药物关系分析和药物-疾病关系分析,对已报道的技术方法及其成功应用实例进行了综述.     

Predicting protein-ligand binding sites based on an improved geometric algorithm

Knowledge of protein-ligand binding sites is very important for structure-based drug designs. To get information on the binding site of a targeted protein with its ligand in a timely way, many scientists tried to resort to computational methods. Although several methods have been released in the past few years, their accuracy needs to be improved. In this study, based on the combination of incremental convex hull, traditional geometric algorithm, and solvent accessible surface of proteins, we developed a novel approach for predicting the protein-ligand binding sites. Using PDBbind database as a benchmark dataset and comparing the new approach with the existing methods such as POCKET, Q-SiteFinder, MOE-SiteFinder, and PASS, we found that the new method has the highest accuracy for the Top 2 and Top 3 predictions. Furthermore, our approach can not only successfully predict the protein-ligand binding sites but also provide more detailed information for the interactions between proteins and ligands. It is anticipated that the new method may become a useful tool for drug development, or at least play a complementary role to the other existing methods in this area.     

Directional and quantitative phosphorylation networks.

Directionality in protein signalling networks is due to modulated protein-protein interactions and is fundamental for proper signal progression and response to external and internal cues. This property is in part enabled by linear motifs embedding post-translational modification sites. These serve as recognition sites, guiding phosphorylation by kinases and subsequent binding of modular domains (e.g. SH2 and BRCT). Characterization of such modification-modulated interactions on a proteome-wide scale requires extensive computational and experimental analysis. Here, we review the latest advances in methods for unravelling phosphorylation-mediated cellular interaction networks. In particular, we will discuss how the combination of new quantitative mass-spectrometric technologies and computational algorithms together are enhancing mapping of these largely uncharted dynamic networks. By combining quantitative measurements of phosphorylation events with computational approaches, we argue that systems level models will help to decipher complex diseases through the ability to predict cellular systems trajectories.     

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.     

Analyzing protein-DNA recognition mechanisms

We present a computational algorithm that can be used to analyze the generic mechanisms involved in protein-DNA recognition. Our approach is based on energy calculations for the full set of base sequences that can be threaded onto the DNA within a protein-DNA complex. It is able to reproduce experimental consensus binding sequences for a variety of DNA binding proteins and also correlates well with the order of measured binding free energies. These results suggest that the crystal structure of a protein-DNA complex can be used to identify all potential binding sequences. By analyzing the energy contributions that lead to base sequence selectivity, it is possible to quantify the concept of direct versus indirect recognition and to identify a new concept describing whether the protein-DNA interaction and DNA deformation terms select optimal binding sites by acting in accord or in disaccord.     

Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-Hotspots)

BackgroundFragment-based ligand design is used for the development of novel ligands that target macromolecules, most notably proteins. Central to its success is the identification of fragment binding sites that are spatially adjacent such that fragments occupying those sites may be linked to create drug-like ligands. Current experimental and computational approaches that address this problem typically identify only a limited number of sites as well as use a limited number of fragment types.MethodsThe site-identification by ligand competitive saturation (SILCS) approach is extended to the identification of fragment bindings sites, with the method termed SILCS-Hotspots. The approach involves precomputation of the SILCS FragMaps following which the identification of Hotspots, performed by identifying of all possible fragment binding sites on the full 3D structure of the protein followed by spatial clustering.ResultsThe SILCS-Hotspots approach identifies a large number of sites on the target protein, including many sites not accessible in experimental structures due to low binding affinities and binding sites on the protein interior. The identified sites are shown to recapitulate the location of known drug-like molecules in both allosteric and orthosteric binding sites on seven proteins including the androgen receptor, the CDK2 and Erk5 kinases, PTP1B phosphatase and three GPCRs; the β2-adrenergic, GPR40 fatty-acid binding and M2-muscarinic receptors. Analysis indicates the importance of considering all possible fragment binding sites, and not just those accessible to experimental methods, when identifying novel binding sites and performing ligand design versus just considering the most favorable sites. The approach is shown to identify a larger number of known binding sites of drug-like molecules versus the commonly used FTMap and Fpocket methods.General significanceThe present results indicate the potential utility of the SILCS-Hotspots approach for fragment-based rational design of ligands, including allosteric modulators.     

Affinity and Specificity of Levamlodipine-Human Serum Albumin Interactions: Insights into Its Carrier Function

The affinity and specificity of drugs with human serum albumin (HSA) are crucial factors influencing the bioactivity of drugs. To gain insight into the carrier function of HSA, the binding of levamlodipine with HSA has been investigated as a model system by a combined experimental and theoretical/computational approach. The fluorescence properties of HSA and the binding parameters of levamlodipine indicate that the binding is characterized by one binding site with static quenching mechanism, which is related to the energy transfer. As indicated by the thermodynamic analysis, hydrophobic interaction is the predominant force in levamlodipine-HSA complex, which is in agreement with the computational results. And the hydrogen bonds can be confirmed by computational approach between levamlodipine and HSA. Compared to predicted binding energies and binding energy spectra at seven sites on HSA, levamlodipine binding HSA at site I has a high affinity regime and the highest specificity characterized by the largest intrinsic specificity ratio (ISR). The binding characteristics at site I guarantee that drugs can be carried and released from HSA to carry out their specific bioactivity. Our concept and quantification of specificity is general and can be applied to other drug-target binding as well as molecular recognition of peptide-protein, protein-protein, and protein-DNA interactions.     

Computational Prediction and Analysis of Breast Cancer Targets for 6-Methyl-1, 3, 8-Trichlorodibenzofuran

Breast cancer is one of the most known cancer types caused to the women around the world. Dioxins on the other hand are a wide range of chemical compounds known to cause the effects on human health. Among them, 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) is a relatively non toxic prototypical alkyl polychlorinated dibenzofuran known to act as a highly effective agent for inhibiting hormone-responsive breast cancer growth in animal models. In this study, we have developed a multi-level computational approach to identify possible new breast cancer targets for MCDF. We used PharmMapper Server to predict breast cancer target proteins for MCDF. Search results showed crystal Structure of the Antagonist Form of Glucocorticoid Receptor with highest fit score and AutoLigand analysis showed two potential binding sites, site-A and site-B for MCDF. A molecular docking was performed on these two sites and based on binding energy site-B was selected. MD simulation studies on Glucocorticoid receptor-MCDF complex revealed that MCDF conformation was stable at site-B and the intermolecular interactions were maintained during the course of simulation. In conclusion, our approach couples reverse pharmacophore analysis, molecular docking and molecular dynamics simulations to identify possible new breast cancer targets for MCDF.     

Computational design of protein-ligand interfaces: potential in therapeutic development

Computational design of protein-ligand interfaces finds optimal amino acid sequences within a small-molecule binding site of a protein for tight binding of a specific small molecule. It requires a search algorithm that can rapidly sample the vast sequence and conformational space, and a scoring function that can identify low energy designs. This review focuses on recent advances in computational design methods and their application to protein-small molecule binding sites. Strategies for increasing affinity, altering specificity, creating broad-spectrum binding, and building novel enzymes from scratch are described. Future prospects for applications in drug development are discussed, including limitations that will need to be overcome to achieve computational design of protein therapeutics with novel modes of action.