Abstract
Identification of new effective inhibitors of apoptosis is an important task for drug development for treatment of a number diseases including neurogenerative diseases. Initiation of apoptosis occurs via the formationof macromolecular protein complexes. In these complexes, activation of key enzymes in apoptosis, caspases, takes place. One of those macromolecular complexes is DISC (death- inducing signaling complex) playing a central role in the induction of the extrinsic apoptosis pathway. The adaptor protein FA DD has a major role in the formation of the DISC. Therefore, inhibitors of FA DD, preventing its function in the DISC, can act as potential drugs inhibiting apoptosis. Furthermore, the study of the mechanisms of action of these inhibitors is of great interest for understanding the mechanisms of the signal transduction pathways of apoptosis. It has been reported that a natural protein inhibitor of FA DD is mucin-type 1 glycoprotein (MUC1). In particular, two fragments of the primary structure of the cytoplasmic domain of MUC1 (MUC1- CD) are capable of inhibiting the binding of caspase-8 to FA DD. However, the three-dimensional structure of MUC1 has not been obtained yet. It complicates significantly the rational design of potential drugs on the basis of these peptides. In this context, the aim of the present study was in silico prediction of three-dimensional structures of MUC1-CD peptides corresponding to protein fragments (1-20 and 46-72), as well as analysis of their conformational properties. The main focus of the work was given to the peptide MUC1-CD (46-72), which is capable of binding to FA DD. Using the methods of molecular dynamics in the implicit water it was shown that the peptide MUC1-CD (46-72) can take conformations similar to the conformations of a number of fragments of the caspase-8 DED domain. It was found that the structure of the peptide MUC1-CD (46-72) is similar to the spatial structure of at least four fragments of caspase-8. These results indicate that the molecular mechanism of the inhibitory activity of the peptide can be explained by competitive binding with FA DD due to the structural and conformational similarity with the fragments of the caspase-8 DED domain.
Highlights
Федеральное государственное бюджетное научное учреждение «Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук», Новосибирск, Россия 2 Федеральное государственное автономное образовательное учреждение высшего образования «Новосибирский национальный исследовательский государственный университет», Новосибирск, Россия 3 Факультет прикладных исследований воспалительных процессов, Институт экспериментальной внутренней медицины, Университет Отто фон Гюрике, Магдебург, Германия
The three-dimensional structure of mucin-type 1 glycoprotein (M c1) has not been obtained yet. it complicates significantly the rational design of potential drugs on the basis of these peptides. in this context the aim of the present study was in silico prediction of three-dimensional structures of M c1-cD peptides corresponding to protein fragments (1-20 and 46-72) as well as analysis of their conformational properties
The main focus of the work was given to the peptide M c1-cD (46-72) which is capable of binding to FADD. sing the methods of molecular dynamics in the implicit water it was shown that the peptide M c1cD (46-72) can take conformations similar to the conformations of a number of fragments of the caspase-8 Death Effector Domain (DED) domain. it was found that the structure of the peptide M c1-cD (46-72) is similar to the spatial structure of at least four fragments of caspase-8
Summary
Молекулярная динамика Моделирование молекулярной динамики (МД) с неявным представлением воды проводили с помощью модуля pmemd.cuda пакета программ Amber 14 (Case et al, 2015) на графических картах NVIDIA Tesla M 2090 в комбинации с моделью неявной воды GB-Neck (Nguyen et al, 2013) с использованием атомных радиусов mbondi и силового поля ff14SBonlysc. Начальные структуры пептидов генерировали с использованием модуля cpptraj (AmberTools 14), затем структуры минимизировали и уравновешивали в три шага: 1 000 циклов минимизации, нагрев от 0 до 300 К в течение первых 100 пс, эквилибрация в течение первых 10 нс. Анализ траектории молекулярной динамики Кластеризацию конформаций пептида проводили с использованием алгоритма ближайшего соседа, реализованного в инструменте MaxCluster (Herbert, Sternberg, 2014). Конформаций пептида с использованием координат Cα атомов пептидов. Расчет RMSD проводили с использованием модуля cpptraj (AmberTools 14), расчет вторичной структуры – методом DSSP Secondary Structure of Proteins) (Kabsch et al, 1983), реализованным в модуле cpptraj (AmberTools 14)
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