Visualization of large molecular trajectories

The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.Peer ReviewedPostprint (author's final draft

Sampling-Based Motion Planning for Tunnel Detection in Protein Structures

Studium proteinů je hlavní oblastí výzkumu molekulární biologie. Jelikož je struktura a funkce proteinů těsně svázaná, získání informací o jejich prostorové konfiguraci patří mezi hlavní cíle jejich zkoumání. V minulosti se již podařilo získat velké množství struktur různých proteinů a volný přístup k těmto informacím nabízí velký potenciál pro teoretické výpočty strukturní bioinformatiky. Jednou z velmi zajímavých oblastí je i hledání tunelů ve strukturách proteinů. Vytvoření spolehlivých algoritmů detekce tunelů ve strukturách proteinů a simulace průchodu chemických látek těmito tunely může značně urychlit a zefektivnit výzkum v oblasti molekulární biologie, a proto se těmto metodám věnuje mnoho výzkumných skupin po celém světě. Plánování cest patří mezi dobře prozkoumané oblasti kybernetiky a robotiky, kde se používá k návrhu možných cest robotů ve stavovém prostoru. Pokud stavový prostor tvoří struktura proteinu a místo robota použijeme sondu definované velikosti, můžeme algoritmy plánování cest převést z robotické do biologické domény. V rámci této diplomové práce byly implementovány algoritmy známé z plánování cest robotů tak, aby nalezly tunely ve statických i dynamických proteinových strukturách. Dále pak byla navržena a implementována metoda pro simulaci průchodu molekul nalezenými tunely.Study of proteins is a major area of molecular biology research. Since the structure and function of proteins are tightly bound, obtaining information about their spatial configuration is one of the main goals of their research. In the past, a large number of different protein structures has been obtained and free access to this information offers a great potential for calculations in structural bioinformatics. One of the most interesting areas is the detection of tunnels in protein structures. Development of reliable algorithms, which detect tunnels in protein structures, and simulation of the passage of chemicals through these tunnels can greatly accelerate research in the field of molecular biology and so, many research groups are devoted to these methods all around the world. Motion planning is a well--known area of cybernetics and robotics, where it is used to design robot paths in state space. If the state space is formed by the structure of protein and instead of the robot we use a probe of a defined size, we can transfer the motion planning algorithms from the robotic to the biological domain. Within this diploma thesis, algorithms known from the motion planning of robots were modified for the task of tunnel detection in both static and dynamic protein structures. Furthermore, a method for geometrical analysis of the passage of molecules through tunnels was proposed and implemented

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

The role of PNPLA3 in the development and progression of chronic liver injury

Chronic liver disease is now of great international concern due to rapidly increasing morbidity and mortality associated with the disease. There is significant evidence that carriage of the patatin-like phospholipase domain containing protein 3 (PNPLA3) risk allele, rs738409:G, plays a key role in determining risk for the development of chronic liver disease from a variety of causes. rs738409 is a common single nucleotide polymorphism which results in substitution of an isoleucine residue for methionine at position 148 of PNPLA3 (Ile148Met; I148M). However, the physiological role of PNPLA3 and the functionality of its I148M variant, are currently largely unknown. The central aim of this thesis was to investigate the biological function of PNPLA3 and elucidate the complex role that the I148M variant plays in the development and progression of liver disease. Investigation into the primary sequence of PNPLA3 was undertaken to characterise the protein and inform latter experimental design. Phylogenetic investigation revealed human PNPLA5 to share the highest homology with PNPLA3, and revealed more distant, previously undescribed relationships with the bacterial protein ExoU. A combination of expression trials and subsequent in vitro investigation into the behaviour of PNPLA3 was attempted. Despite attempts with numerous constructs, PNPLA3 remained unstable when expressed using an E. coli expression system and was not able to be produced in sufficient quantity to facilitate structural analysis. In the latter half of the thesis, both variants of PNPLA3 are investigated through in silico structural modelling and subsequent molecular dynamic simulation. The first simulation of full-length PNPLA3 is reported, revealing a more detailed description of the domain architecture of PNPLA3 and the local impact of the I148M variation. A novel disease mechanism is proposed, in which methionine at residue 148 effects the conformational stability of the PNPLA3 active site, resulting in a loss of lipase activity

FireProt(DB): database of manually curated protein stability data

The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProt(DB). The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb