Balachandran Manavalan

Molecular Modeling-Based Evaluation of hTLR10 and Identification of Potential Ligands in Toll-Like Receptor Signaling

Toll-like receptors (TLRs) are pattern recognition receptors that recognize pathogens based on distinct molecular signatures. The human (h)TLR1, 2, 6 and 10 belong to the hTLR1 subfamilies, which are localized in the extracellular regions and activated in response to diverse ligand molecules. Due to the unavailability of the hTLR10 crystal structure, the understanding of its homo and heterodimerization with hTLR2 and hTLR1 and the ligand responsible for its activation is limited. To improve our understanding of the TLR10 receptor-ligand interaction, we used homology modeling to construct a three dimensional (3D) structure of hTLR10 and refined the model through molecular dynamics (MD) simulations. We utilized the optimized structures for the molecular docking in order to identify the potential site of interactions between the homo and heterodimer (hTLR10/2 and hTLR10/1). The docked complexes were then used for interaction with ligands (Pam3CSK4 and PamCysPamSK4) using MOE-Dock and ASEDock. Our docking studies have shown the binding orientations of hTLR10 heterodimer to be similar with other TLR2 family members. However, the binding orientation of hTLR10 homodimer is different from the heterodimer due to the presence of negative charged surfaces at the LRR11-14, thereby providing a specific cavity for ligand binding. Moreover, the multiple protein-ligand docking approach revealed that Pam3CSK4 might be the ligand for the hTLR10/2 complex and PamCysPamSK4, a di-acylated peptide, might activate hTLR10/1 hetero and hTLR10 homodimer. Therefore, the current modeled complexes can be a useful tool for further experimental studies on TLR biology.

Toll-like receptor modulators: a patent review (2006 – 2010)

Introduction: The immune response is mediated via two parallel immune components, innate and adaptive, whose effector functions are highly integrated and coordinated for the protection of the human body against invading pathogens and transformed cells. The discovery of pathogen recognition receptors (PRRs), most notably toll-like receptors (TLRs), in innate immunity has evoked increased interest in the therapeutic handling of the innate immune system. TLRs are germ line-encoded receptors that play a potent role in the recognition of a diverse variety of ligands ranging from hydrophilic nucleic acids to lipopolysaccharide (LPS) or peptidoglycan (PGN) structures in pathogens. Areas covered: This review discusses recent updates (2006 – 2010) in completed, ongoing and planned clinical trials of TLR immunomodulator-based therapies for the treatment of infectious diseases, inflammatory disorders and cancer. Expert opinion: Since the discovery of human TLRs, modulating immune responses using TLR agonists or antagonists for therapeutic purposes has provoked intense activity in the pharmaceutical industry. The ability of TLRs to initiate and propagate inflammation makes them attractive therapeutic targets. We are now at the stage of evaluating such molecules in human diseases. Additionally, there is also extensive literature available on TLRs in diseased states. These data provide a basis for the identification of novel immunomodulators (agonists and antagonists) for the therapeutic targeting of TLRs.

Similar Structures but Different Roles – An Updated Perspective on TLR Structures

Toll-like receptors (TLRs) are pattern recognition receptors that recognize conserved structures in pathogens, trigger innate immune responses, and prime antigen-specific adaptive immunity. Elucidation of crystal structures of TLRs interacting with their ligands such as TLR1-2 with triacylated lipopeptide, TLR2-6 with diacylated lipopeptide, TLR4–MD-2 with LPS, and TLR3 with double-stranded RNA (dsRNA) have enabled an understanding of the initiation of TLR signaling. Agonistic ligands such as LPS, dsRNA, and lipopeptides induce “m” shaped TLR dimers in which C-termini converge at the center. Such central convergence is necessary to bring the two intracellular receptor TIR domains closer together and promote their dimerization, which serves as an essential step in downstream signaling. In this review, we summarize TLR ECD structures that have been reported to date with special emphasis on ligand recognition and activation mechanism.

Structure-Function Relationship of Cytoplasmic and Nuclear IκB Proteins: An In Silico Analysis.

Cytoplasmic IκB proteins are primary regulators that interact with NF-κB subunits in the cytoplasm of unstimulated cells. Upon stimulation, these IκB proteins are rapidly degraded, thus allowing NF-κB to translocate into the nucleus and activate the transcription of genes encoding various immune mediators. Subsequent to translocation, nuclear IκB proteins play an important role in the regulation of NF-κB transcriptional activity by acting either as activators or inhibitors. To date, molecular basis for the binding of IκBα, IκBβ and IκBζ along with their partners is known; however, the activation and inhibition mechanism of the remaining IκB (IκBNS, IκBε and Bcl-3) proteins remains elusive. Moreover, even though IκB proteins are structurally similar, it is difficult to determine the exact specificities of IκB proteins towards their respective binding partners. The three-dimensional structures of IκBNS, IκBζ and IκBε were modeled. Subsequently, we used an explicit solvent method to perform detailed molecular dynamic simulations of these proteins along with their known crystal structures (IκBα, IκBβ and Bcl-3) in order to investigate the flexibility of the ankyrin repeat domains (ARDs). Furthermore, the refined models of IκBNS, IκBε and Bcl-3 were used for multiple protein-protein docking studies for the identification of IκBNS-p50/p50, IκBε-p50/p65 and Bcl-3-p50/p50 complexes in order to study the structural basis of their activation and inhibition. The docking experiments revealed that IκBε masked the nuclear localization signal (NLS) of the p50/p65 subunits, thereby preventing its translocation into the nucleus. For the Bcl-3- and IκBNS-p50/p50 complexes, the results show that Bcl-3 mediated transcription through its transactivation domain (TAD) while IκBNS inhibited transcription due to its lack of a TAD, which is consistent with biochemical studies. Additionally, the numbers of identified flexible residues were equal in number among all IκB proteins, although they were not conserved. This could be the primary reason for their binding partner specificities.

Molecular modeling of the reductase domain to elucidate the reaction mechanism of reduction of peptidyl thioester into its corresponding alcohol in non-ribosomal peptide synthetases

BackgroundNonribosomal peptide synthetases (NRPSs) are multienzymatic, multidomain megasynthases involved in the biosynthesis of pharmaceutically important nonribosomal peptides. The peptaibol synthetase from Trichoderma virens (TPS) is an important member of the NRPS family that exhibits antifungal properties. The majority of the NRPSs terminate peptide synthesis with the thioesterase (TE) domain, which either hydrolyzes the thioester linkage, releasing the free peptic acid, or catalyzes the intramolecular macrocyclization to produce a macrolactone product. TPS is an important NRPS that does not encompass a TE domain, but rather a reductase domain (R domain) to release the mature peptide product reductively with the aid of a NADPH cofactor. However, the catalytic mechanism of the reductase domain has not yet been elucidated.ResultsWe present here a three-dimensional (3D) model of the reductase domain based on the crystal structure of vestitone reductase (VR). VR belongs to the short-chain dehydrogenase/reductase (SDR) superfamily and is responsible for the nicotinamide dinucleotide phosphate (NADPH)-dependent reduction of the substrate into its corresponding secondary alcohol product. The binding sites of the probable linear substrates, alamethicin, trichotoxin, antiamoebin I, chrysopermin C and gramicidin, were identified within the modeled R domain using multiple docking approaches. The docking results of the ligand in the active site of the R domain showed that reductase side chains have a high affinity towards ligand binding, while the thioester oxygen of each substrate forms a hydrogen bond with the OH group of Tyr176 and the thiol group of the substrate is closer to the Glu220. The modeling and docking studies revealed the reaction mechanism of reduction of thioester into a primary alcohol.ConclusionPeptaibol biosynthesis incorporates a single R domain, which appears to catalyze the four-electron reduction reaction of a peptidyl carrier protein (PCP)-bound peptide to its corresponding primary alcohol. Analysis of R domains present in the non-redundant (nr) database of the NCBI showed that the R domain always resides in the last NRPS module and is involved in either a two or four-electron reduction reaction.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest

The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

Molecular modeling‐based evaluation of dual function of IκBζ ankyrin repeat domain in toll‐like receptor signaling

IκBζ (inhibitor of NF‐κB (nuclear factor κB) ζ) is a nuclear protein induced upon stimulation of toll‐like receptors (TLRs) and interleukin‐1 receptor. Induced IκBζ, especially its C‐terminal ankyrin repeat domain (ARD), interacts with NF‐κB in the nucleus, where it regulates the transcriptional activity of target genes. Recent studies have shown that human ARD of IκBζ binds with p50/p65 heterodimer and inhibits the transcription of NF‐κB regulated genes, whereas mouse ARD of IκBζ binds with p50/p50 homodimer and exhibits transcriptional activation activity. Since human and mouse IκBζ ARD are identical, it is unclear how IκBζ can be a positive and negative regulator of NF‐κB‐mediated transcription. Therefore, we generated a structural model of IκBζ ARD and constructed a detailed molecular dynamics (MD) simulation of IκBζ in explicit solvent to investigate ARD flexibility. In addition, we used molecular docking to screen for potential sites of interaction between IκBζ and the p50/p65 heterodimer and IκBζ and the p50/p50 homodimer. The docking experiments revealed that the binding of IκBζ ankyrin repeats with the p50/p65 N‐terminal DNA binding domain prevents NF‐κB‐mediated transcriptional activation. Furthermore, the IκBζ–p50 homodimer complex, which lacks Pro, Glu (and Asp), Ser and Thr (PEST motif), facilitated gene expression. These two different binding schemes of IκBζ may be responsible for its opposite function, which is consistent with the currently available biochemical data. Moreover, our data implicate structurally highly flexible ARD residues as the prime contributors to this dual function. Copyright