A simple method for visualizing the differences between related receptor sites

Pastor and Cruciani [J. Med. Chem. 38 (1995) 4637] and Kastenholz et al. [J. Med. Chem. 43 (2000) 3033] pioneered methods for comparing related receptors, with the ultimate goal of designing selective ligands. Such methods start with a reasonable superposition of high-resolution three-dimensional (3D) structures of the receptors. Next, molecular field maps are calculated for each receptor. Then the maps are analyzed to determine which map features are correlated with a particular subset of receptors. We present a method FLOGTV, based on the trend vector paradigm [J. Chem. Inf. Comput. Sci. 25 (1985) 64] to perform the analysis. This is mathematically simpler than the GRID/CPCA method of Kastenholz et al. and allows for the simultaneous comparison of many receptor structures. Also, the trend vector paradigm provides a method of selecting isopotential contours that are well above "noise". We demonstrate the method on four examples: HIV proteases versus two-domain acid proteases, thrombin versus trypsin and factor Xa, bacterial dihydrofolate reductases (DHFRs) versus vertebrate DHFRs, and P38 versus ERK protein kinases.     

A simple method for visualizing the differences between related receptor sites

Pastor and Cruciani [J. Med. Chem. 38 (1995) 4637] and Kastenholz et al. [J. Med. Chem. 43 (2000) 3033] pioneered methods for comparing related receptors, with the ultimate goal of designing selective ligands. Such methods start with a reasonable superposition of high-resolution three-dimensional (3D) structures of the receptors. Next, molecular field maps are calculated for each receptor. Then the maps are analyzed to determine which map features are correlated with a particular subset of receptors. We present a method FLOGTV, based on the trend vector paradigm [J. Chem. Inf. Comput. Sci. 25 (1985) 64] to perform the analysis. This is mathematically simpler than the GRID/CPCA method of Kastenholz et al. and allows for the simultaneous comparison of many receptor structures. Also, the trend vector paradigm provides a method of selecting isopotential contours that are well above "noise". We demonstrate the method on four examples: HIV proteases versus two-domain acid proteases, thrombin versus trypsin and factor Xa, bacterial dihydrofolate reductases (DHFRs) versus vertebrate DHFRs, and P38 versus ERK protein kinases.     

Toward the three-dimensional structure and lysophosphatidic acid binding characteristics of the LPA4/p2y9/GPR23 receptor: A homology modeling study

Lysophosphatidic acid (LPA) is a naturally occurring phospholipid that initiates a broad array of biological processes, including those involved in cell proliferation, survival and migration via activation of specific G protein-coupled receptors located on the cell surface. To date, at least five receptor subtypes (LPA_1–5) have been identified. The LPA_1–3 receptors are members of the endothelial cell differentiation gene (Edg) family. LPA₄, a member of the purinergic receptor family, and the recently identified LPA₅ are structurally distant from the canonical Edg LPA_1–3 receptors. LPA₄ and LPA₅ are linked to G_q, G_12/13 and G_s but not G_i, while LPA_1–3 all couple to G_i in addition to G_q and G_12/13. There is also evidence that LPA₄ and LPA₅ are functionally different from the Edg LPA receptors. Computational modeling has provided useful information on the structure–activity relationship (SAR) of the Edg LPA receptors. In this work, we focus on the initial analysis of the structural and ligand-binding properties of LPA₄, a prototype non-Edg LPA receptor. Three homology models of the LPA₄ receptor were developed based on the X-ray crystal structures of the ground state and photoactivated bovine rhodopsin and the recently determined human β₂-adrenergic receptor. Docking studies of LPA in the homology models were then conducted, and plausible LPA binding loci were explored. Based on these analyses, LPA is predicted to bind to LPA₄ in an orientation similar to that reported for LPA_1–3, but through a different network of hydrogen bonds. In LPA_1–3, the ligand polar head group is reported to interact with residues at positions 3.28, 3.29 and 7.36, whereas three non-conserved amino acid residues, S114(3.28), T187(EL2) and Y265(6.51), are predicted to interact with the polar head group in the LPA₄ receptor models.     

Controlled multibatch self-assembly of microdevices

A technique is described for assembly of multiple batches of micro components onto a single substrate. The substrate is prepared with hydrophobic alkanethiol-coated gold binding sites. To perform assembly, a hydrocarbon oil, which is applied to the substrate, wets exclusively the hydrophobic binding sites in water. Micro components are then added to the water, and assembled on the oil-wetted binding sites. Moreover, assembly can be controlled to take place on desired binding sites by using an electrochemical method to deactivate specific substrate binding sites. By repeatedly applying this technique, different batches of micro components can be sequentially assembled to a single substrate. As a post assembly procedure, electroplating is incorporated into the technique to establish electrical connections for assembled components. Important issues presented are: substrate fabrication techniques, electrochemical modulation by using a suitable alkanethiol (dodecanethiol), electroplating of tin and lead alloy and binding site design simulations. Finally, we demonstrate a two-batch assembly of silicon square parts, and establishing electrical connectivity for assembled surface-mount light emitting diodes (LEDs) by electroplating.     

Toward an understanding of the sequence and structural basis of allosteric proteins

Allostery is the most efficient means of regulating protein functions, ranging from the control of metabolic mechanisms to signal transduction pathways. Although allosteric regulation has been recognized for half a century, our knowledge is limited to the characteristics of allosteric proteins and the structural coupling of allosteric sites and modulators. In this paper, we present a comprehensive analysis of allosteric proteins that provides insight into the foundation of allosteric interactions by revealing a series of common features in the allosteric proteins. Allosteric proteins mainly appear in transferases, and phosphorylation is the most common type of modification found in allosteric proteins. Disorders related to allosteric proteins primarily comprise metabolic diseases and cancers. In general, allosteric proteins prefer to exist as monomers or even-numbered multimers. Greater stability and hydrophobicity are observed in allosteric proteins than in general proteins. Further analysis of the allosteric sites reveals a series of buried and compact pockets composed of significantly greater hydrophobic surface area than the corresponding orthosteric sites. The hydrophobicity of the allosteric sites plays a dominant role in the binding of allosteric modulators as observed in the analysis of 106 diverse allosteric protein–modulator pairs. These results may be of great significance in predicting which proteins are allosteric and in designing novel triggers to inhibit or activate proteins of interest.     

带有蛋白质输入的RNA-蛋白质结合位点预测方法

RNA及RNA结合蛋白之间的相互作用在基因调控中扮演着重要角色。许多预测RNA-蛋白质结合位点的深度学习方法陆续提出。目前多数研究没有将RNA结合蛋白作为模型输入,限制了深度学习模型的规模。对此问题,提出一个带有RNA结合蛋白输入的深度学习模型,通过扩大训练集的规模挖掘RNA-蛋白质结合位点的公共知识。模型将RNA序列先后经过卷积神经网络和门控循环单元来得到序列特征;将序列特征与RNA结合蛋白的独热编码拼接,作为全连接层的输入;通过一个Sigmoid单元输出该RNA结合蛋白对RNA序列的结合概率。在两个权威数据集上,该方法相比其他模型均具有一定优势。     

Identification of tubulin drug binding sites and prediction of relative differences in binding affinities to tubulin isotypes using digital signal processing

Microtubules are involved in numerous cellular processes including chromosome segregation during mitosis and, as a result, their constituent protein, tubulin, has become a successful target of several chemotherapeutic drugs. In general, these drugs bind indiscriminately to tubulin within both cancerous and healthy cells, resulting in unwanted side effects. However, differences between beta-tubulin isotypes expressed in a wide range of cell types may aid in the development of anti-tubulin drugs having increased specificity for only certain types of cells. Here, we describe a digital signal processing (DSP) method that is capable of predicting hot spots for the tubulin family of proteins as well as determining relative differences in binding affinities to these hot spots based only on the primary sequence of 10 human tubulin isotypes. Due to the fact that several drug binding sites have already been characterized within beta-tubulin, we are able to correlate hot spots with the binding sites for known chemotherapy drugs. We have also verified the accuracy of this method using the correlation between the binding affinities of characterized drugs and the tubulin isotypes. Additionally, the DSP method enables the rapid estimation of relative differences in binding affinities within the binding sites of tubulin isotypes that are yet to be experimentally determined.     

Sequence conservation and correlation measures in protein structure prediction

The rapid elucidation of protein sequences has allowed multiple sequence alignments to be calculated for a wide variety of proteins. Such alignments reveal positions that exhibit amino acid conservation--either of specific chemical groups in active and binding sites or of the more chemically inert hydrophobic residues that contribute to the protein core. The latter can provide constraints on the position of the protein chain and any local periodicity can suggest the type of secondary structure. Conservation measures, however, cannot provide specific pairwise packing information (each conserved hydrophobic position might pack against any other). However, if correlated changes between positions were observed then specific pairs of residue could be identified as interacting and therefore probably spatially adjacent. Most 'observations' of correlated changes have been anecdotal and of the few systematic studies that have been made, most have mistakenly incorporated a strong bias towards selecting conserved positions. When the conservation effect is separated (as best as possible) then little correlation signal remains to help identify adjacent positions.     

'Multifrequency' location and clustering of sequence patterns from proteins

In previous work, we have shown that a set of characteristics, defined as (code frequency) pairs, can be derived from a protein family by the use of a signal-processing method. This method enables the location and extraction of sequence patterns by taking into account each (code frequency) pair individually. In the present paper, we propose to extend this method in order to detect and visualize patterns by taking into account several pairs simultaneously. Two 'multifrequency' methods are described. The first one is based on a rewriting of the sequences with new symbols which summarize the frequency information. The second method is based on a clustering of the patterns associated with each pair. Both methods lead to the definition of significant consensus sequences. Some results obtained with calcium-binding proteins and serine proteases are also discussed.     

基于快速多示例多标记学习的G蛋白偶联受体生物学功能预测

G蛋白偶联受体(G protein-coupled receptors, GPCRs)是人类中最庞大的膜蛋白家族,也是很多药物的重要靶点,准确了解GPCRs生物学功能是理解它们参与的生物学过程及其药物作用机制的关键.以前的研究表明,蛋白质功能预测可抽象为多示例多标记学习(multi-instance multi-label learning, MIML)问题.设计了一种基于快速多示例多标记学习方法MIMLfast的GPCRs生物学功能预测模型.该模型采用了一种新的混合特征,它考虑了GPCRs结构域的三联氨基酸、氨基酸关联、进化、二级结构关联、信号肽及无序残基等多种信息.实验结果证明,该模型获得了很好的性能,优于目前最优的多示例多标记学习、多标记学习的预测方法和CAFA蛋白质功能预测方法.     

A simple method to calculate the accessible volume of protein-bound ligands: application for ligand selectivity

We here present a method for estimating the accessible volume surrounding each atom of a ligand when bound to a receptor. The accessible volumes are calculated as the volume of a sphere with fixed radius, centered on each ligand atom, not simultaneously occupied by the volume of the receptor. The method is capable of describing the packing of the ligand in the binding pocket. Moreover, where structural models of several receptors in complex with the same ligand are available, a comparative study discriminating on accessible volumes can be performed. For these cases, a relatively large accessible volume in a particular receptor might indicate that this receptor has a unique cavity that might be exploited to develop a selective ligand analog. The ligand atoms showing variation in surrounding volume accessibility when bound to different receptors constitute attractive anchor points where one might want to attach substituents that modify the selectivity of the ligand. We have applied the described method to two different enzyme-ligand systems that bind tetrahydrobiopterin, i.e. the aromatic amino acid hydroxylases and nitric oxygen synthases. Our results yield new insights into the specificity of cofactor binding to these protein families.     

The function of the amino terminal domain in NMDA receptor modulation

N-methyl-D-aspartate (NMDA) receptors are ligand-gated channels important in neurotransmission which are activated by the combined presence of glutamate and glycine. They are comprised of four subunits that form a dimer of dimers. The activity of NMDA receptors is modulated by a variety of endogenous ligands such as zinc ions, phenylethanolamines, polyamines and protons. Findings show that the binding sites for these modulators are found in the amino terminal domain of such receptors, but different modulators appear to affect different subunits. However, despite the enormous efforts expended in mutagenesis and patch clamp experiments on NMDA receptors, the exact assembly of these subunits and the effects of the modulatory species are not well understood. We have modelled dimers of the amino terminal domains of these receptors based on their homology with the extracellular dimer of a metabotropic glutamate receptor. Conserved cysteine residues, which have been highlighted as important in previous work, are shown to form a disulphide bridge, stabilizing a four-helix bundle between subunits. This establishes a hinge in the receptor. The model also highlights a zinc binding site in the binding crevice of the NR2a subunit of the receptor that stabilizes the open state of the amino terminal domain. The similar effect of ifenprodil is thus explained by its stabilization of the open state of the amino terminal domain (ATD). The presence of three histidine residues in the zinc site is used to explain the pH dependence of zinc inhibition. Previous work has also implicated certain residues in spermine stimulation of such receptors. The homology model shows that this site is found at the inter-subunit boundary of the dimer. This predicts a binding site between subunits, a result not calculable by the homology modelling of single subunits done previously. Finally, these results are drawn together to yield a consistent picture of NMDA receptor activation and desensitization. An understanding of how these receptors work and how they can be modulated is an important step toward rational drug design.     

Induced-fit docking of mometasone furoate and further evidence for glucocorticoid receptor 17alpha pocket flexibility

An induced-fit docking method was used to characterize the interactions of the glucocorticoid receptor binding-site with mometasone furoate, a glucocorticoid with a lipophilic ester at the C17alpha position. Two validation studies demonstrated that the protocol can reproduce crystal structures of nuclear receptors, and is appropriate for modeling ligand binding to the glucocorticoid receptor. Key hydrogen bonding interactions between mometasone furoate and the glucocorticoid receptor, as well as favorable hydrophobic interactions between the furoate group and the 17alpha pocket, contribute to high affinity and specificity of this ligand for the receptor. Using the glucocorticoid des-ciclesonide, which has an even larger moiety at the 16,17alpha position, induced-fit docking demonstrates the ability of the 17alpha pocket of the receptor to expand even further to accommodate the ligand.     

Molecular determinants of LXRalpha agonism

Liver X receptors (LXRs) are nuclear receptors that participate in the regulation of cholesterol, bile acid, and glucose metabolism. Despite the identification of the natural oxysterol and nonsteroidal ligands for LXRalpha, little is known about the structure of the LXRalpha ligand-binding domain (LBD). We constructed a three-dimensional (3D) homology model of the LBD of LXRalpha based on the crystal structure of the retinoic acid receptor gamma (RARgamma) and all-trans retinoic acid complex. We combined molecular modeling and classical structure-function techniques to define the interactions between the LBD and three structurally diverse ligands, 22(R)-hydroxycholesterol (22RHC), N-(2,2,2-trifluoro-ethyl)-N-[4-(2,2,2-trifluoro-1-hydroxy-1-trifluoromethyl-ethyl)-phenyl]-benzenesulfonamide (T0901317) and (3-[3-[(2-chloro-3-trifluoromethyl-benzyl)-(2,2-diphenyl-ethyl)-amino]-propoxy]-phenyl)-acetic acid (GW3965). Sixteen individual amino acid point mutations were made in the predicted ligand-binding cavity of the LBD, and each of these mutant receptors was assessed for their ability to be activated by these three ligands. The majority of individual mutations resulted in lack of activation by all three ligands. Two residues were identified that resulted in a significant increase in basal activity while retaining responsiveness to the ligands. Interestingly, a number of residues were identified that appear to be selective in their response to a particular ligand, indicating that these three ligands recognize distinct structural components within the ligand-binding cavity. These data, together with our docking study, enable us to identify the amino acids that coordinate the interaction of both steroidal and non-steroidal ligands in the ligand-binding pocket of LXRalpha.     

Novel ligands that target the mitochondrial membrane protein mitoNEET

Ligands of the thiazolidinedione (TZD) class of compounds, pioglitazone (Actos?) and rosiglitazone (Avandia?) are currently approved for treatment of type 2 diabetes and are known to bind to the PPAR-γ nuclear receptor subtype. Recent evidence suggesting PPAR-γ independent action of the TZDs led to the discovery of a novel integral outer mitochondrial membrane protein, mitoNEET. In spite of the several reported X-ray crystal structures of the unbound form of mitoNEET, the location and nature of the mitoNEET ligand binding sites (LBS) remain unknown. In this study, a molecular blind docking (BD) method was used to discover potential mitoNEET LBS and novel ligands, utilizing the program AutoDock Vina (v 1.0.2). Validation of BD was performed on the PPAR-γ receptor (PDB ID: 1ZGY) with the test compound rosiglitazone, demonstrating that the binding conformation of rosiglitazone determined by AutoDock Vina matches well with that of the cocrystallized ligand (root mean square deviation of the heavy atoms 1.45?). The locations and a general ligand binding interaction model for the LBS were determined, leading to the discovery of novel mitoNEET ligands. An in vitro fluorescence binding assay utilizing purified recombinant mitoNEET protein was used to determine the binding affinity of a predicted mitoNEET ligand, and the data obtained is in good agreement with AutoDock Vina results. The discovery of potential mitoNEET ligand binding sites and novel ligands, opens up the possibility for detailed structural studies of mitoNEET-ligand complexes, as well as rational design of novel ligands specifically targeted for mitoNEET.     

AI-based algorithms for protein surface comparisons.

Many current methods for protein analysis depend on the detection of similarity in either the primary sequence, or the overall tertiary structure (the Calpha atoms of the protein backbone). These common sequences or structures may imply similar functional characteristics or active properties. Active sites and ligand binding sites usually occur on or near the surface of the protein; so similarly shaped surface regions could imply similar functions. We investigate various methods for describing the shape properties of protein surfaces and for comparing them. Our current work uses algorithms from computer vision to describe the protein surfaces, and methods from graph theory to compare the surface regions. Early results indicate that we can successfully match a family of related ligand binding sites, and find their similarly shaped surface regions. This method of surface analysis could be extended to help identify unknown surface regions for possible ligand binding or active sites.     

小分子配体模仿结合位点水分子: 相同配体与不同蛋白靶点之间作用模式的计算研究

在计算化学和药物设计领域中,精确预测小分子配体的蛋白质靶标是一件极具挑战性工作,特别是那些与相同小分子配体相互作用,但蛋白间并不具有显著序列或结构相似性的靶标蛋白。为了研究相同小分子配体与不相关的蛋白质靶标之间的结合特性,我们使用蛋白水合效应分析 (SPA) 程序研究了 33 对药物靶蛋白结合位点水分子的结构特性。每个蛋白对中的两个蛋白质,均由可与相同配体小分子相结合的两个不相关的蛋白质组成。通过计算两蛋白质间的水化位点替代重叠率,我们发现共有高达 73% 的蛋白对中有着显著的水分子替代重叠率值。特别是我们发现相同小分子与不同蛋白相互作用时,或许存在一种特别的“识别代码”,即小分子配体的功能基团与结合位点的水分子之间存在着较好的几何构型相似性。在无法确定小分子与蛋白相互作用模式时,可以依靠活性结合腔穴中水分子的结合构型特征,进行粗略估算与预测,对蛋白-配体相互作用模式研究有重要的实用价值。     

BINANA: a novel algorithm for ligand-binding characterization

Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at http://www.nbcr.net/binana/. To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand-receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand-receptor complexes for key binding characteristics.