Identification of Ligand Templates using Local Structure Alignment for Structure-based Drug Design

With a rapid increase in the number of high-resolution protein-ligand structures, the known protein-ligand structures can be used to gain insight into ligand-binding modes in a target protein. Based on the fact that the structurally similar binding sites share information about their ligands, we have developed a local structure alignment tool, G-LoSA (Graph-based Local Structure Alignment). In G-LoSA, the known protein-ligand binding-site structure library is searched to detect binding-site structures with similar geometry and physicochemical properties to a query binding-site structure regardless of sequence continuity and protein fold. Then, the ligands in the identified complexes are used as templates (i.e., template ligands) to predict/design a ligand for the target protein. The performance of G-LoSA is validated against 76 benchmark targets from the Astex diverse set. Using the currently available protein-ligand structure library, G-LoSA is able to identify a single template ligand (from a non-homologous protein complex) that is highly similar to the target ligand in more than half of the benchmark targets. In addition, our benchmark analyses show that an assembly of structural fragments from multiple template ligands with partial similarity to the target ligand can be used to design novel ligand structures specific to the target protein. This study clearly indicates that a template-based ligand modeling has potential for de novo ligand design and can be a complementary approach to the receptor structure based methods

Predicting the accuracy of protein-ligand docking on homology models

Ligand-protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand-protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target-template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.Contract/grant sponsor: National Institutes of Health; contract/grant numbers: ES00768

Virtual Screening of Plant Volatile Compounds Reveals a High Affinity of Hylamorpha elegans (Coleoptera: Scarabaeidae) Odorant-Binding Proteins for Sesquiterpenes From Its Native Host

Indexación: Web of ScienceHylamorpha elegans (Burmeister) is a native Chilean scarab beetle considered to be a relevant agricultural pest to pasture and cereal and small fruit crops. Because of their cryptic habits, control with conventional methods is difficult; therefore, alternative and environmentally friendly control strategies are highly desirable. The study of proteins that participate in the recognition of odorants, such as odorant-binding proteins (OBPs), offers interesting opportunities to identify new compounds with the potential to modify pest behavior and computational screening of compounds, which is commonly used in drug discovery, may help to accelerate the discovery of new semiochemicals. Here, we report the discovery of four OBPs in H. elegans as well as six new volatiles released by its native host Nothofagus obliqua (Mirbel). Molecular docking performed between OBPs and new and previously reported volatiles from N. obliqua revealed the best binding energy values for sesquiterpenic compounds. Despite remarkable divergence at the amino acid level, three of the four OBPs evaluated exhibited the best interaction energy for the same ligands. Molecular dynamics investigation reinforced the importance of sesquiterpenes, showing that hydrophobic residues of the OBPs interacted most frequently with the tested ligands, and binding free energy calculations demonstrated van der Waals and hydrophobic interactions to be the most important. Altogether, the results suggest that sesquiterpenes are interesting candidates for in vitro and in vivo assays to assess their potential application in pest management strategies.http://jinsectscience.oxfordjournals.org/content/16/1/3

Ligand Binding Site Detection b Local Structure Alignment and Its Performance Complementarity

Accurate determination of potential ligand binding sites (BS) is a key step for protein function characterization and structure-based drug design. Despite promising results of template-based BS prediction methods using global structure alignment (GSA), there is a room to improve the performance by properly incorporating local structure alignment (LSA) because BS are local structures and often similar for proteins with dissimilar global folds. We present a template-based ligand BS prediction method using G-LoSA, our LSA tool. A large benchmark set validation shows that G-LoSA predicts drug-like ligands’ positions in single-chain protein targets more precisely than TM-align, a GSA-based method, while the overall success rate of TM-align is better. G-LoSA is particularly efficient for accurate detection of local structures conserved across proteins with diverse global topologies. Recognizing the performance complementarity of G-LoSA to TM-align and a non-template geometry-based method, fpocket, a robust consensus scoring method, CMCS-BSP (Complementary Methods and Consensus Scoring for ligand Binding Site Prediction), is developed and shows improvement on prediction accuracy. The G-LoSA source code is freely available at http://im.bioinformatics.ku.edu/GLoSA

Ligand-guided homology modeling drives identification of novel histamine H3 receptor ligands

In this study, we report a ligand-guided homology modeling approach allowing the analysis of relevant binding site residue conformations and the identification of two novel histamine H3 receptor ligands with binding affinity in the nanomolar range. The newly developed method is based on exploiting an essential charge interaction characteristic for aminergic G-protein coupled receptors for ranking 3D receptor models appropriate for the discovery of novel compounds through virtual screening

Using bioinformatics tools to screen for trypanosomal cathepsin B cysteine protease inhibitors from the SANCDB as a novel therapeutic modality against Human African Trypanosomiasis (HAT)

Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a fatal chronic disease that is caused by flagellated protozoans, Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense. HAT is spread by a bite from an infected tsetse fly of the Glosina genus. Up to 60 million people in 36 countries in sub-Saharan Africa are at a risk of infection from HAT with up to 30 000 deaths reported every year. Current chemotherapy for HAT is insufficient since the available drugs exhibit unacceptable side effects (toxicity) and parasite resistance. Novel treatments and approaches for development of specific and more potent drugs for HAT are therefore required. One approach is to target vital proteins that are essential to the life cycle of the parasite. The main interest of this study is to explore Trypanosoma brucei cathepsin B-like protease (TbCatB) structural and functional properties with the primary goal of discovering non peptide small molecule inhibitors of TbCatB using bioinformatics approaches. TbCatB is a papain family C1 cysteine protease which belongs to clan CA group and it has emerged as a potential HAT drug target. Papain family cysteine proteases of Clan CA group of Trypanosoma brucei (rhodesain and TbCatB) have demonstrated potential as chemotherapeutic targets using synthetic protease inhibitors like Z-Phe-Ala-CHN2 to kill the parasite in vitro and in vivo. TbCatB has been identified as the essential cysteine protease of T. brucei since mRNA silencing of TbCatB killed the parasite and resulted in a cure in mice infected with T. brucei while mRNA silencing of rhodesain only extended mice life. TbCatB is therefore a promising drug target against HAT and the discovery and development of compounds that can selectively inhibit TbCatB without posing any danger to the human host represent a great therapeutic solution for treatment of HAT. To understand protein-inhibitor interactions, useful information can be obtained from high resolution protease-inhibitor crystal structure complexes. This study aims to use bioinformatics approaches to carry out comparative sequence, structural and functional analysis of TbCatB protease and its homologs from T. congolense, T, cruzi, T. vivax and H. sapien as well as to identify non-peptide small molecule inhibitors of TbCatB cysteine proteases from natural compounds of South African origin. Sequences of TbCatB (PDB ID: 3HHI) homologs were retrieved by a BLAST search. Human cathepsin B (PDB ID: 3CBJ) was selected from a list of templates for homology modelling found by HHpred. MODELLER version 9.10 program was used to generate a hundred models for T. congolense, T, cruzi and T. vivax cathepsin B like proteases using 3HHI and 3CBJ as templates. The best models were chosen based on their low DOPE Z scores before validation using MetaMQAPII, ANOLEA, PROCHECK and QMEAN6. The DOPE Z scores and the RMSD (RMS) values of the calculated models indicate that the models are of acceptable energy (stability) and fold (conformation). Results from the different MQAPs indicate the models are of acceptable quality and they can be used for docking studies. High throughput screening of SANCDB using AutoDock Vina revealed nine compounds, SANC00 478, 479, 480, 481, 482, 488, 489, 490 and 491, having a strong affinity for Trypanosoma spp. cathepsin B proteases than HsCatB. SANC00488 has the strongest binding to Trypanosoma spp. cathepsin B proteases and the weakest binding to HsCatB protease. Molecular dynamics (MD) simulations show that the complexes between SANC00488 and TbCatB, TcCatB, TcrCatB and TvCatB are stable and do not come apart during simulation. The complex between this compound and HsCatB however is unstable and comes apart during simulation. Residues that are important for the stability of SANC00488-TbCatB complex are Gly328 of the S2 subsite, Phe208, and Ala256. In conclusion SANC00488 is a good candidate for development of a drug against HAT

Protein structure prediction and structure-based protein function annotation

Nature tends to modify rather than invent function of protein molecules, and the log of the modifications is encrypted in the gene sequence. Analysis of these modification events in evolutionarily related genes is important for assigning function to hypothetical genes and their products surging in databases, and to improve our understanding of the bioverse. However, random mutations occurring during evolution chisel the sequence to an extent that both decrypting these codes and identifying evolutionary relatives from sequence alone becomes difficult. Thankfully, even after many changes at the sequence level, the protein three-dimensional structures are often conserved and hence protein structural similarity usually provide more clues on evolution of functionally related proteins. In this dissertation, I study the design of three bioinformatics modules that form a new hierarchical approach for structure prediction and function annotation of proteins based on sequence-to-structure-to-function paradigm. First, we design an online platform for structure prediction of protein molecules using multiple threading alignments and iterative structural assembly simulations (I-TASSER). I review the components of this module and have added features that provide function annotation to the protein sequences and help to combine experimental and biological data for improving the structure modeling accuracy. The online service of the system has been supporting more than 20,000 biologists from over 100 countries. Next, we design a new comparative approach (COFACTOR) to identify the location of ligand binding sites on these modeled protein structures and spot the functional residue constellations using an innovative global-to-local structural alignment procedure and functional sites in known protein structures. Based on both large-scale benchmarking and blind tests (CASP), the method demonstrates significant advantages over the state-of-the- art methods of the field in recognizing ligand-binding residues for both metal and non- metal ligands. The major advantage of the method is the optimal combination of the local and global protein structural alignments, which helps to recognize functionally conserved structural motifs among proteins that have taken different evolutionary paths. We further extend the COFACTOR global-to-local approach to annotate the gene- ontology and enzyme classifications of protein molecules. Here, we added two new components to COFACTOR. First, we developed a new global structural match algorithm that allows performing better structural search. Second, a sensitive technique was proposed for constructing local 3D-signature motifs of template proteins that lack known functional sites, which allows us to perform query-template local structural similarity comparisons with all template proteins. A scoring scheme that combines the confidence score of structure prediction with global-local similarity score is used for assigning a confidence score to each of the predicted function. Large scale benchmarking shows that the predicted functions have remarkably improved precision and recall rates and also higher prediction coverage than the state-of-art sequence based methods. To explore the applicability of the method for real-world cases, we applied the method to a subset of ORFs from Chlamydia trachomatis and the functional annotations provided new testable hypothesis for improving the understanding of this phylogenetically distinct bacterium

eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models

© 2014 Michal Brylinski. Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite