Interactive Application Using UnrealScript

Tato práce se zaměřuje na tvorbu projektů v Unreal Development Kitu, především pak na jejich programování v doprovodném jazyce UnrealScript. Jsou zde stručně probrány nástroje, které jsou v UDK integrovány pro tvorbu scén a obsluhu jejich vnitřní logiky, a dále pak významnější z tříd UnrealScriptu pro práci s herní kamerou, HUDem, obsluhou uživatelských vstupů a jiné. Závěrem je připojeno i srovnání UnrealScriptu s některými programovacími jazyky, využitelnými pro tvorbu uživatelských rozhraní a vyhodnocení uživatelských testů, provedených nad výsledným projektem v UDK.This work is focused on the creating of projects in Unreal Developement Kit, mainly on their programming in language UnrealScript. There are shortly discused tools, integrated into UDK for creating scenes and operating their inner logic, same as some more significant classes of UnrealScript for work with game camera, HUD, elaborating user inputs and others. In the end, there is joined comparasion between UnrealScript and some other languages, usable for creation of user interfaces and evaluation of user tests, executed on final project in UDK.

Predictor of the Effect of Amino Acid Substitutions on Protein Function

Tato práce se zaobírá problematikou predikce škodlivosti aminokyselinových substitucí pomocí metody fylogenetické analýzy, inspirované nástrojem MAPP. Nezanedbatelné množství genetických onemocnění je způsobeno nesynonymními SNPs, projevujícími se jako jednobodové mutace na úrovni proteinů. Schopnost identifikovat tyto škodlivé substituce by mohla být užitečná v oblasti proteinového inženýrství pro testování, zda navržená mutace nepoškodí funkci proteinu a stejně tak k identifikaci choroby způsobujících škodlivých mutací. Experimentální ohodnocení navržených mutací je však nákladné a vyvstala tak potřeba pro predikci vlivu aminokyselinových substitucí počítačovými metodami. Tato práce popisuje návrh a implementaci nového predikčního nástroje, založeného na principech evoluční analýzy a studiu rozdílnosti fyzikálně-chemických vlastností mezi původní a substituovanou aminokyselinou. Vyvinutý algoritmus byl otestován na čtyř datasetech, čítajících celkem 74 192 mutací na 16 256 proteinových sekvencích. Prediktor dosáhl až 72 % přesnosti a ve srovnání s většinou v současné době existujících nástrojů je jeho výpočet výrazně méně náročný na počítačový čas. Ve snaze dosáhnout maximální možnou efektivitu nástroje byl optimalizační proces zaměřen na výběr nejvhodnějších (a) nástrojů třetích stran, (b) rozhodovacího prahu a (c) sady fyzikálně-chemických vlastností.This thesis discusses the issue of predicting of the effect of amino acid substitutions on protein funkcion, based on phylogenetic analysis method, inspired by tool MAPP. Significant number of genetic diseases is caused by nonsynonymous SNPs manifested as single point mutations on the protein level. The ability to identify deleterious substitutions could be useful for protein engineering to test whether the proposed mutations do not damage protein function same as for targeting disease causing harmful mutations. However the experimental validation is costly and the need of predictive computation methods has risen. This thesis describes desing and implementation of a new in silico predictor based on the principles of evolutionary analysis and dissimilarity between original and substituting amino acid physico-chemical properties. Developed algorithm was tested on four datasets with 74,192 mutations from 16,256 sequences in total. The predictor yields up to 72 % accuracy and in the comparison with the most existing tools, it is substantially less time consuming. In order to achieve the highest possible efficiency, the optimization process was focused on selection of the most suitable (a) third-party software for calculation of a multiple sequence alignment, (b) overall decision threshold and (c) a set of physico-chemical properties.

EIGENVALUES EVALUATION OF GENERALLY DAMPED ELASTIC DISC BRAKE MODEL LOADED WITH NON-CONSERVATIVE FRICTION FORCE

This paper deals with the evaluation of eigenvalues of a linear damped elastic two-degrees-of-freedom system under a non- onservative loading. As a physical interpretation of a proposed mathematical model, a simplified disk brake model is considered. A spectral analysis is performed to predict an eigenvalues bifurcation, known as the Krein collision, leading to double eigenvalues, one of them having a positive real part causing a vibration instability of the mechanical systems. This defective behaviour of eigenvalues is studied with respect to a magnitude of non-conservative Coulomb friction force, through the variation of the friction coefficient. The influence of a proportional versus general damping on the system stability is further analysed. The generalized non-symmetric eigenvalue problem calculation is employed for spectral analyses, while a modal decomposition is performed to obtain a time-domain response of the system. The analyses are compared with an experiment

Computational Design of Stable and Soluble Biocatalysts

Natural enzymes are delicate biomolecules possessing only marginal thermodynamic stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in the biotechnology and biopharmaceutical industries. Consequently, there is a need to design optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents. This has created great interest in using computational methods to enhance biocatalysts' robustness and solubility. Suitable methods include (i) energy calculations, (ii) machine learning, (iii) phylogenetic analyses, and (iv) combinations of these approaches. We have witnessed impressive progress in the design of stable enzymes over the last two decades, but predictions of protein solubility and expressibility are scarce. Stabilizing mutations can be predicted accurately using available force fields, and the number of sequences available for phylogenetic analyses is growing. In addition, complex computational workflows are being implemented in intuitive web tools, enhancing the quality of protein stability predictions. Conversely, solubility predictors are limited by the lack of robust and balanced experimental data, an inadequate understanding of fundamental principles of protein aggregation, and a dearth of structural information on folding intermediates. Here we summarize recent progress in the development of computational tools for predicting protein stability and solubility, critically assess their strengths and weaknesses, and identify apparent gaps in data and knowledge. We also present perspectives on the computational design of stable and soluble biocatalysts

Pendulum Energy Harvester with Amplifier

This paper presents a new principle of inductive vibration power harvester. Harvester is a pendulum that uses energy capacitor which is the mass. The mass is connected to the pendulum via a gearbox to achieve greater movement of the pendulum that generates an electromagnetic voltage. The harvester is developed at a very low frequency (1-10 Hz) which uses the rectified magnetic fluxes. Magnets are statically placed in the harvester case, and relative motion is carried out by the coil. Magnets are static, and the coil moves due to the weight ratio of magnets which the steel leads of the magnetic flux and the coil itself. This paper is focused on a harvester with a mechanical amplifier with the proposed technique is brings the plow harvester access with an auxiliary force. The experimental results indicate that the optimal results of the harvester with an accumulator for the resonant zone are 3.75 Hz, 7 Hz, and 10 Hz

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

FireProt(ASR): A Web Server for Fully Automated Ancestral Sequence Reconstruction

There is a great interest in increasing proteins' stability to widen their usability in numerous biomedical and biotechnological applications. However, native proteins cannot usually withstand the harsh industrial environment, since they are evolved to function under mild conditions. Ancestral sequence reconstruction is a well-established method for deducing the evolutionary history of genes. Besides its applicability to discover the most probable evolutionary ancestors of the modern proteins, ancestral sequence reconstruction has proven to be a useful approach for the design of highly stable proteins. Recently, several computational tools were developed, which make the ancestral reconstruction algorithms accessible to the community, while leaving the most crucial steps of the preparation of the input data on users' side. FireProt(ASR) aims to overcome this obstacle by constructing a fully automated workflow, allowing even the unexperienced users to obtain ancestral sequences based on a sequence query as the only input

FireProt: web server for automated design of thermostable proteins

There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.Web of Science45W1W399W39