Trans-Sialidase’s Role in Chronic Chagas Disease, and it’s Potential for Infection Inhibition by Employing Natural Products

Chronic Chagas Disease is a malady affecting a highly vulnerable population, with currently no promising cure. This study looked at natural products from the Taiwan Pharmaceutical Databank, and utilized an in-silico approach to discover potential new drug compounds. Two structures from the PDB were identified for docking, namely 1S0J and 1MS8. The crystal structures were of Trans-sialidase, a surface protein on the protozoan, essential for establishing short and long term infection. The results of the docking were ranked, and the top three compounds Dihalenaquinolide A, (+)-Ovigeridimerin, and Bisisodiospyrin were further discussed. The free binding energy of Dihalenaquinolide A was -13.6 kcal/mol and -15.23 kcal/mol when docked to 1S0J, and -12.7 to -16.86 kcal/mol when docked to 1MS8. (+)-Ovigeridimerin had free binding energies of -12.6 kcal/mol to -13.50 kcal/mol to 1S0J, and -14.4 to -15.92 kcal/mol to 1MS8. Finally, Bisisodiospyrin had free binding energies of -12.3 kcal/mol to around -15.73 kcal/mol when docked to 1S0J, and -13.8 to -16.76 kcal/mol when docked to 1MS8. The compounds need to be further studied to verify their actual efficiency as potential novel drug candidates. Introduction Currently there are eight to ten million people worldwide infected with a parasitic infection known as Chagas Disease, and an estimated 300,000 live in the US (Bern, 2011). However, the malady mainly affects people in poor, rural areas in Latin America, where there is limited health care access. The vectors of the disease are hematophagous triatomine bugs (Bern, 2011) that carry the flagellated parasite, known as Trypanosoma Cruzi (T. cruzi). The parasite lives in the hind-gut of the bug, and once the bug has its blood meal, when its host is asleep, it defecates, and the parasite enters through the bite site or via contact with mucous membranes. At this stage the parasite is a metacyclic trypomastigote and it invades many different kinds of cells, where it evades host cell defenses, such as lytic processes. The metacyclic trypomastigote transforms into amastigotes within the host cell, and starts replicating until the cytoplasm is full. The intracellular amastigotes transform back into trypomastigotes, where they burst out of the cell and continue to infect other host cells. The initial phase of infection is known as acute Chagas Disease, where the host can display symptoms similar to common cold, however, at times infected individuals have been symptom free, and the parasite proliferates without the knowledge of the host. The phase lasts about one to two months, until the replication is regulated by host immune responses (Bern, 2011). But the immune system fails to eliminate the parasite fully, and it continues to live within the host for decades. This is known as chronic Chagas Disease. During this phase, the parasite can live within the host without causing any further damage to its host, but in 20 to 50 percent of infected individuals the disease progresses into irreversible cardiac muscle damage, and gastrointestinal failure, resulting in death. Currently, there has been some success in eliminating the parasite during acute stage, with a parasitological cure of about 60 to 80 percent (Bern, 2011). However, once the disease enters chronic phase, a parasitological cure has been estimated to be around 10 to 20 percent (Jose Rodrigues Coura, 2002). The drugs used to treat Chagas Disease are Benznidazole, and Nifurtimox, which were both developed around late 1960s and early 1970s and prove to have adverse side effects when treating Chagas. In fact, the use of Nifuritimox was discontinued first in the 1980s in Brazil with other countries following suit (Jose Rodrigues Coura, 2002). In the US, both drugs are not approved by the FDA, and can only be obtained for usage under investigational protocols from the CDC (Bern, 2011). The issues with current drugs can be attributed to the fact that the drug is not uniquely targeting the parasite but also its host. Therefore, identifying a protein limited only to the protozoan is an attractive alternative. In this study Trans-sialidase (TcTS) was identified as a novel target. It’s found solely on the surface of the protozoan, and it scavenges sialic acid molecules from the host, due to its inability of synthesizing the molecule itself. The protein has been identified to be involved with host cell invasion, evade action of lytic antibodies, and even may have a role in immunomodulation, which allows the parasite to establish long term infection (Mendonca-Previato, 2010). Therefore, TcTS was selected as a novel target to find potential drugs to inhibit its catalytic activity. These potential drugs were obtained from Taiwan Pharmaceutical Databank. It houses over 2,000 natural compounds that have been collected and synthesized in Taiwan. Natural products are lucrative in their drug design and have shown to have an immense importance in the pharmaceutical industry where they, or their derivatives have been readily approved by the FDA (David J Newman, 2012). Therefore in this study natural products were identified as putative drug candidates against to combat chronic Chagas Disease. Methods/Materials Ten structures from the PDB were originally picked to be analyzed, namely: 1S0J, 2AH2, 1S0I, 1MR5, 1MS8, 1MS5, 1MS9, 1MS3, 1MS4, 1MS0. In order to determine their usefulness in virtual screening all these structures were actual crystal structures of T. cruzi’s TcTS, none of the structures were homology models derived from other species. Two of the structures were finally picked: 1S0J, and 1MS8, based on how accurate AutoDock Vina, and AutoDock 4.0 were able to reproduce the experimental results. The location of the ligand had to be in the same binding pocket, and the orientation of the ligand had to mimic the experimental results to some extent. Each selected PDB structure, had its ligand striped away from the pdb file by editing the identity tags, HETATM from the file. Water molecules were removed, polar hydrogens were added, and Kollman Charges were added using AutoDock Tools 1.5.6. The complete structure was saved as a pdbqt file, and was used in both AutoDock Vina and AutoDock 4.0 virtual screening processes. A gridbox was set up around the binding pocket to specify where AutoDock Vina should attempt to dock the ligands. The ligands were obtained from the Taiwan Pharmaceutical Databank (http://tpd.mc.ntu.edu.tw/index.php), which were already in pdbqt format. AutoDock Vina was first used in the screening process, the rational to use Vina first was due to the fact that its algorithm and processing time was a lot faster in obtaining results in comparison to AutoDock 4.0, so it was a good screening tool to obtain results fast. Once the docking was completed, the top results with lowest free binding energy were docked once again using AutoDock 4.0, in order to confirm and obtain the binding energies predicted by AutoDock 4.0. Unlike AutoDock Vina, AutoDock 4.0 accounts for the electrostatic interactions, which the majority of binding activity between ligand and Trans-sialidase are through hydrogen bond interactions. Therefore, electrostatic forces are thought to be significant in this reaction, in addition to several key residues in the binding pocket are charged. Figure 1: Key: Vina in yellow, AD4 in red, and experimental result in pink. Both Vina and AD4 were able to recreate the binding fairly accurate. Results After running AutoDock Vina to screen potential compounds in the Taiwan Pharmaceutical Databank, the compounds were ranked in base of their free energy results, from most negative (highest binding affinity) to the least negative (low binding affinity). A text file was generated with all the compounds ranked accordingly. The top ten compounds are demonstrated in the Table 1 and Table 2. To the most left of the table is the compound’s rank. Thereafter, the numbers following TPD is the compound’s SN number, the number that can be used to find the compounds in the Databank by using a URL link (http://tpd.mc.ntu.edu.tw/compound.php?SN=) where the ten to twelve digit number follows the equal sign. The binding affinity can be found to the right of the compound’s SN number. Five compounds show up in both tables, their SN numbers are: 71184833541, 91185766573, 281010096476, 71184834376, and 91186211040. However, in this paper the top three compounds are only highlighted namely, 71184833541, 91185766573, and 281010096476. These compounds were consistently among the top three regardless of what protein structure they were docked to. Ranked number one to 1S0J was Dihalenaquinolide A (SN: 71184833541) was derived from a Taiwanese marine sponge, petrosia elasticia. It’s a fairly non-polar compound with ten aromatic rings. Two regions of the compound have a higher electronegativity than other regions, however, that can probably be attributed to the two ketone groups and an ester group on the aromatic rings. The mass of the compound was around 694 amu. Top 10 Binding Affinities for 1S0J -------------------------------------------------------------------------------------Rank File Name Free Binding Energy -------------------------------------------------------------------------------------1 TPD.71184833541_out.pdbqt -13.6 2 TPD.91185766573_out.pdbqt -12.6 3 TPD.281010096476_out.pdbqt -12.3 4 TPD.281011317462_out.pdbqt -12.3 5 TPD.71184834376_out.pdbqt -12.2 6 TPD.281010461515_out.pdbqt -12.1 7 TPD.91186211040_out.pdbqt -12.1 8 TPD.101185345882_out.pdbqt -12.0 9 TPD.281011318172_out.pdbqt -12.0 10 TPD.281011317061_out.pdbqt -11.8 Top 10 Binding Affinities for 1MS8 ------------------------------------------------------------------------------------Rank File Name Free Binding Energy ------------------------------------------------------------------------------------1 TPD.91185766573_out.pdbqt -14.4 2 TPD.281010096476_out.pdbqt -13.8 3 TPD.71184833541_out.pdbqt -12.7 4 TPD.281011409415_out.pdbqt -12.4 5 TPD.281011409716_out.pdbqt -12.4 6 TPD.71184834376_out.pdbqt -12.3 7 TPD.91186211040_out.pdbqt -11.7 8 TPD.281011297903_out.pdbqt -11.6 9 TPD.281011398209_out.pdbqt -11.6 10 TPD.281011399613_out.pdbqt -11.6 Table 1