CLiBE: a database of computed ligand binding energy for ligand-receptor complexes

Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand-receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand-receptor interaction energy is developed from known ligand-receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand-receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.     

The role of LasR active site amino acids in the interaction with the Acyl Homoserine Lactones (AHLs) analogues: A computational study

The present work combines molecular docking calculations, 3D-QSAR, molecular dynamics simulations and free binding energy calculations (MM/PBSA and MM/GBSA) in a set of 28 structural analogues of acyl homoserine lactones with Quorum Sensing antagonist activity. The aim of this work is to understand how ligand binds and is affected by the molecular microenvironment in the active site of the LasR receptor for pseudomonas aeruginosa. We also study the stability of the interaction to find key structural characteristics that explain the antagonist activities of this set of ligands. This information is relevant for the rational modification or design of molecules and their identification as powerful LasR modulators.The analysis of molecular docking simulations shows that the 28 analogues have a similar binding mode compared to the native ligand. The carbonyl groups belonging to the lactone ring and the amide group of the acyl chain are oriented towards the amino acids forming hydrogen bond like interactions. The difference in antagonist activity is due to location and orientation of the LasR side chains within the hydrophobic pocket in its binding site. Additionally, we carried out molecular dynamics simulations to understand the conformational changes in the ligand-receptor interaction and the stability of each complex. Results show a direct relationship among the interaction energies of the ligands and the activities as an antagonist of the LasR receptor.     

Aggregation of a Distributed Source in Morphogen Gradient Formation

In the development of a biological entity, ligands (such as Decapentaplegic (Dpp) along the anterior-posterior axis of the Drosophila wing imaginal disc) are synthesized at a localized source and transported away from the source for binding with cell surface receptors to form concentration gradients of ligand-receptor complexes for cell signaling. Generally speaking, activities such as diffusion and reversible binding with degradable receptors also take place in the region of ligand production. The effects of such morphogen activities in the region of localized distributed ligand source on the ligand-receptor concentration gradient in the entire biological entity have been modeled and analyzed as System F in [1]. In this paper, we deduce from System F, a related end source model (System A) in which the effects of the distributed ligand source is replaced by an idealized point stimulus at the border between the (posterior) chamber and the ligand production region that simulates the average effects of the ligand activities in the production zone. This aggregated end source model is shown to adequately reproduce the significant implications of System F and to contain the corresponding ad hoc point source model, System R of [2], as a special case. Because of its simpler mathematical structure and the absence of any limitation on the ligand synthesis rate for the existence of steady-state gradients, System A type models are expected to be used widely. An example of such application is the recent study of the inhibiting effects of the formation of nonsignaling ligand-nonreceptor complexes [3].     

Identification of non-resistant ROS-1 inhibitors using structure based pharmacophore analysis

Proto-oncogene receptor tyrosine kinase ROS-1 plays a key role in regulating a variety of cancers mainly non-small cell lung cancer (NSCLC). The marketed ROS-1 inhibitors such as Crizotinib suffer from the tribulations of growing resistance due to mutations primarily Gly2032Arg in the ROS-1 protein. To curb the problem of resistance, researchers have developed inhibitors such as Lorlatinib against the mutant protein. The present study was designed to identify inhibitors against wild type (WT) as well as mutant ROS-1 protein that will offer a broader spectrum of activity. Exploring crystal structure of ROS-1 complexed with Lorlatinib, receptor-ligand pharmacophore model was developed using Discovery Studio (DS) software. The developed pharmacophore model consisted of one hydrogen bond acceptor (HBA), one hydrogen bond donor (HBD) and two hydrophobic features (HY), subsequently utilized for virtual screening of commercially available databases and the retrieved hits were further subjected to fitness score and Lipinski’s filter. Thereafter, the retrieved hits were docked in WT and mutated (Gly2032Arg) proteins of ROS-1. Total five molecules were retrieved with good docking scores and good binding interactions within the active site of WT and mutated ROS-1. The binding energies of the ligand-receptor complexes were predicted via calculation of MM-GBSA score. To predict the stability of the ligand receptor complexes with mutant and wild type proteins, molecular dynamic simulation was performed. Thus, these identified hits show good binding affinities with WT and mutant ROS-1 proteins that may be further evaluated for their in-vitro/in-vivo activity.     

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。     

PKR-inhibitor binds efficiently with human microtubule affinity-regulating kinase 4

MAP/microtubule affinity-regulating kinase 4 (MARK4) plays a central role in the cellular physiology, and it is inseparably linked with many human diseases including cancer, diet induced obesity, type2 diabetes and neurodegenerative disorders. Here, we studied the interaction of PKR-inhibitor with two variants of human MARK4. One variant is named as MARK4-F1 which has 59 N-terminal residues along with kinase domain while another variant is MARK4-F2 which has kinase domain only. Molecular-docking, molecular dynamics (MD) simulation and fluorescence-binding studies were undertaken to understand the role of N-terminal 59-residues in the binding of substrate/inhibitors. Molecular docking studies revealed that the PKR-inhibitor binds in the large hydrophobic cavity of the kinase domain of MARK4 through several hydrophobic and hydrogen-bonded interactions. Furthermore, MD simulation showed a stable parameters for the complexes of both MARK4-F1 and MARK4-F2 to PKR-inhibitor with marginal difference in their binding affinities. A significant decrease in the fluorescence intensity of MARK4 was observed on successive addition of the PKR-inhibitor. Using fluorescence data we have calculated the binding-affinity and the number of binding site of PKR-inhibitor to the MARK4. A significantly high binding affinity was observed for the PKR-inhibitor to the MARK4 variants. However, there is no any significant difference in the binding affinity of PKR-inhibitor to the MARK4 variants was observed, indicating that 59 N-terminal residues of MARK4-F1 are not playing a crucial role in the ligand binding. The present study will provide an insights into designing of new PKR-inhibitor derivative as potent and selective therapeutic agent against many life threatening diseases which are associated with MARK4.     

Predicting the structures of complexes between phosphoinositide 3-kinase (PI3K) and romidepsin-related compounds for the drug design of PI3K/histone deacetylase dual inhibitors using computational docking and the ligand-based drug design approach

Predictions of the three-dimensional (3D) structures of the complexes between phosphoinositide 3-kinase (PI3K) and two inhibitors were conducted using computational docking and the ligand-based drug design approach. The obtained structures were refined by structural optimizations and molecular dynamics (MD) simulations. The ligands were located deep inside the ligand binding pocket of the p110α subunit of PI3K, and the hydrogen bond formations and hydrophobic effects of the surrounding amino acids were predicted. Although rough structures were obtained for the PI3K–inhibitor complexes before the MD simulations, the refinement of the structures by these simulations clarified the hydrogen bonding patterns of the complexes.     

Could MM-GBSA be accurate enough for calculation of absolute protein/ligand binding free energies?

Implicit solvation methods such as MM-GBSA, when applied to evaluating protein/ligand binding free energies, are widely believed to be accurate only for the estimation of relative binding free energies for a congeneric series of ligands. In this work, we show that the MM-GBSA flavor of Prime 3.0, VSGB-2.0, with a variable dielectric model and a novel energy function, could be approaching the accuracy required for evaluating absolute binding free energies, albeit, through a linear regression fit. The data-set used for validation includes 106 protein–ligand complexes that were carefully selected to control for variability in the affinity data as well as error in the modeled complexes. Through systematic analysis, we also quantify the degradation in the R² of fit between experimental and calculated values with either greater variability in the affinity data or an increase in error in the modeled protein/ligand complexes. Limitations for its application in drug discovery are discussed along with the identification of areas for future development.     

氨基喹啉类抗疟疾药物的活性预测

采用分子全息距离矢量方法描述40个氨基喹啉类化合物的分子结构,运用主成分回归方法建模进行定量构效关系分析,预测其抗疟原虫活性。其两组活性数据所得结果相关系数分别为0．9438和0．9737,交互检验相关系数分别为0．8305和0．9098。由此表明所建立的多参数模型稳定,能较好地预测氨基喹啉类药物的抗疟原虫活性。为指导和设计新的高效低毒抗疟疾药物提供有力依据。     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins

LIGSITE is a new program for the automatic and time-efficient detection of pockets on the surface of proteins that may act as binding sites for small molecule ligands. Pockets are identified with a series of simple operations on a cubic grid. Using a set of receptor–ligand complexes we show that LIGSITE is able to identify the binding sites of small molecule ligands with high precision. The main advantage of LIGSITE is its speed. Typical search times are in the range of 5 to 20 s for medium-sized proteins. LIGSITE is therefore well suited for identification of pockets in large sets of proteins (e.g., protein families) for comparative studies. For graphical display LIGSITE produces VRML representations of the protein–ligand complex and the binding site for display with a VRML viewer such as WebSpace from SGI.     

An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data

Microarray technologies are employed to simultaneously measure expression levels of thousands of genes. Data obtained from such experiments allow inference of individual gene functions, help to identify genes from specific tissues, to analyze the behavior of gene expression levels under various environmental conditions and under different cell cycle stages, and to identify inappropriately transcribed genes and several genetic diseases, among many other applications. As thousands of genes may be involved in a microarray experiment, computational tools for organizing and providing possible visualizations of the genes and their relationships are crucial to the understanding and analysis of the data. This work proposes an algorithm based on artificial immune systems for organizing gene expression data in order to simultaneously reveal multiple features in large amounts of data. A distinctive property of the proposed algorithm is the ability to provide a diversified set of high-quality rearrangements of the genes, opening up the possibility of identifying various co-regulated genes from representative graphical configurations of the expression levels. This is a very useful approach for biologists, because several co-regulated genes may exist under different conditions.     

FLIP: An assisting software in structure based drug design using fingerprint of protein-ligand interaction profiles

With the growing number of labor-intensive data in the pharmaceutical industries and public domain for protein-ligand complexes, a significant challenge is still remaining in managing and leveraging this vast information. Here, a standalone application is presented for analysis, organization, and illustration of structural data and molecular interactions for exploiting 3D-structures into simple 1D fingerprints. The utility of the approach was shown in unraveling a feasible solution for post-processing of docking results in parallel with providing fruitful analysis for users in order to investigate molecular interactions. Remarkably, all interaction possibilities including (hydrogen bond, water-bridged, electrostatic, and hydrophobic as well as π- π and cation-π interactions) are supported both in the form of fingerprints and compelling reports. These investigations are mainly considered based on right orientation, location, and geometry of the interacting pairs rather than the acquisition of the energy terms. The reasonable efficiency of our application in different models was comparable to recent methods It is clearly presented that FLIP provides a faster way to generate usable fingerprints for ligand and protein binding modes. FLIP is free for academic use and is available at: http://zistrayan.com/development/download/flip/package.zip     

Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

We describe the application of knowledge-based potentials implemented in the MOE program to compare the ligand-binding sites of several proteins. The binding probabilities for a polar and a hydrophobic probe are calculated on a grid to allow easy comparison of binding sites of superimposed related proteins. The method is fast and simple enough to simultaneously use structural information of multiple proteins of a target family. The method can be used to rapidly cluster proteins into subfamilies according to the similarity of hydrophobic and polar fields of their ligand-binding sites. Regions of the binding site which are common within a protein family can be identified and analysed for the design of family-targeted libraries or those which differ for improvement of ligand selectivity. The field-based hierarchical clustering is demonstrated for three protein families: the ligand-binding domains of nuclear receptors, the ATP-binding sites of protein kinases and the substrate binding sites of proteases. More detailed comparisons are presented for serine proteases of the chymotrypsin family, for the peroxisome proliferator-activated receptor subfamily of nuclear receptors and for progesterone and androgen receptor. The results are in good accordance with structure-based analysis and highlight important differences of the binding sites, which have been also described in the literature.     

Structural model of the Plasmodium CDK, Pfmrk, a novel target for malaria therapeutics

Malaria, with 300-500 million clinical cases resulting in 1-3 million fatalities a year, is one of the most deadly tropical diseases. As current antimalarial therapeutics become increasingly ineffective due to parasitic resistance, there exists an urgent need to develop and pursue new therapeutic strategies. Recent genome sequencing and molecular cloning projects have identified several enzymes from Plasmodium (P.) falciparum that may represent novel drug targets, including a family of proteins that are homologous to the mammalian cyclin-dependent kinases (CDKs). CDKs are essential for the control of the mammalian cell cycle and, based on the conservation of the CDKs across species, the plasmodial CDKs are expected to play a crucial role in parasitic growth. Here we present a 3D structural model of Pfmrk, a putative human CDK activating kinase (CAK) homolog in P. falciparum. Notable features of the present structural model include: (1) parameterization of the Mg2+ hexacoordination system using ab initio quantum chemical calculations to accurately represent the ATP-kinase interaction; and (2) comparison between the docking scores and measured binding affinities for a series of oxindole-based Pfmrk inhibitors of known activity. Detailed analysis of inhibitor-Pfmrk binding interactions enabled us to identify specific residues (viz. Met66, Met75, Met91, Met94 and Phe143) within the Pfmrk binding pocket that may play an important role in inhibitor binding affinity and selectivity. The availability of this Pfmrk structural model, together with insights gained from analysis of ligand-receptor interactions, should promote the rational design of potent and selective Pfmrk inhibitors as antimalarial therapeutics.     

Simulation of alternative binding modes in a structure-based QSAR study of HIV-1 protease inhibitors

We have used a published set of inhibitors of HIV-1 protease to build a COMBINE-type structure-based QSAR model with good predictive ability (r² = 0.90, q² = 0.69). Since the compounds in the training series exhibit most of their structural variability on one-half of the pseudosymmetrical binding cavity and only one binding orientation was explored for each molecule, the model describes mainly the effect of the structural changes on interactions involving only one-half of the binding cavity (pockets S1′ and S2′). Thus, the model cannot be expected to give accurate predictions for new compounds exhibiting structural variation in both halves. The model does in fact show a tendency to underpredict slightly the biological activity of the molecules in the external test set. In an attempt to improve the quality of the model, both possible orientations of the ligands are now considered so that structural variation takes place in all binding pockets. One possibility would have been to build an additional set of complexes with the inhibitors docked in a reversed orientation. The alternative we have explored, however, consists of manipulating the data matrix describing the interaction energies so that each row is duplicated and the order of the variables in the duplicated rows is swapped between subunits. This simple approach has produced a new model that is similar in quality to the original model (r² = 0.89, q² = 0.64) but lacks the tendency to underpredict the activity of the compounds in the external set. Moreover, since equivalent residues are assigned equivalent weights, the model is insensitive to ligand orientation and is easier to interpret.     

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.     

Predicting different adhesive regimens of circulating particles at blood capillary walls

A fundamental step in the rational design of vascular targeted particles is the firm adhesion at the blood vessel walls. Here, a combined lattice Boltzmann–immersed boundary model is presented for predicting the near-wall dynamics of circulating particles. A moving least squares algorithm is used to reconstruct the forcing term accounting for the immersed particle, whereas ligand-receptor binding at the particle–wall interface is described via forward and reverse probability distributions. First, it is demonstrated that the model predicts with good accuracy the rolling velocity of tumor cells over an endothelial layer in a microfluidic channel. Then, particle–wall interactions are systematically analyzed in terms of particle geometries (circular, elliptical with aspect ratios 2 and 3), surface ligand densities (0.3, 0.5, 0.7 and 0.9), ligand-receptor bond strengths (1 and 2) and Reynolds numbers (Re = 0.01, 0.1 and 1.0). Depending on these conditions, four different particle–wall interaction regimens are identified, namely not adhering, rolling, sliding and firmly adhering particles. The proposed computational strategy can be efficiently used for predicting the near-wall dynamics of particles with arbitrary geometries and surface properties and represents a fundamental tool in the rational design of particles for the specific delivery of therapeutic and imaging agents.