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Schedule Interview NowMy name is Vladimir P. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: Kotlin, Java, Spring Boot, PostgreSQL, C#, etc.. I hold a degree in Doctor of Philosophy (PhD), Doctor of Philosophy (PhD). Some of the notable projects Iโve worked on include: Kafka Stream Processing: Monitoring & Handling of Errors, Spring Boot Service-based Database Sharding in Highload Environment, Service for Full-Text Searching, Kafka-based high-load monitoring service, Python module for protein structure analysis, etc.. I am based in Prague, Czech Republic. I've successfully completed 7 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
Main technologies
2 years
1 Year
1 Year
1 Year
Potentially possible
DataSentics
๐ง๐ฎ๐๐ธ: The project was related to data stream processing using Kafka. The data was represented by a sequence of events. These events were processed by different indexers related to business processes, statistics, and storage in databases such as MySQL or Elastic. I needed to develop a solution for handling service downtime, corrupted events, or indexer bugs. ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: I developed a tool that monitors indexers, identifies and marks problematic events and ensures normal data stream. Service managers are notified about these problematic events and then handle each of them manually. In particular, a specific "ProcessingErrors" Kafka topic is used to store failed events in the stream system. The developed "Monitoring" tool indexes these events in memory, manages operations and handles state snapshots. These snapshots allow speedy recovery of the state of the "Monitoring" tool in case of emergency or maintenance. My tool allows to query information on the problematic events and supports pagination and filtering. It is connected to the Admin Panel, used by service managers, via REST API. The developed solution is versatile and supports addition of new topics and indexers.
I WORKED HARD
๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ: Development of a python module for the analysis and modification of sequences and 3D structures of proteins and their assemblies that uses .cif/.pdb files deposited in RCSB Protein Data Bank. ๐ ๐ผ๐๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Solutions that existed either were not customizable enough (e.g., did not allow using custom sets of constants, potentials, symmetry matrices etc.), used overly complex models not suitable for carrying out certain types of computations for large structures with 10^7-10^8 atoms (for example viruses and their superstructures) or did not allow to perform all the required operations within a single environment. ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฑ ๐๐ผ๐น๐๐๐ถ๐ผ๐ป: I have developed a python module that allows to: โข Modify 3D structures of proteins by renaming, deleting, combining, moving, and applying symmetry to individual atoms, amino acids, whole proteins or their assemblies. โข Visualize in 3D structures at the level of individual atoms, amino acids and whole proteins. โข Calculate surface area of proteins and protein-protein contacts, similarity of protein structures, partial charges of amino acids and energy of electrostatic interactions in the assemblies depending on the pH and salinity of the environment. I and my colleagues used the module in combination with Jupyter Notebook when working on several bioinformatics related projects. The results of our work were published in multiple high impact scientific journals (Nanoscale Advances, Biomatererials Science, Soft Matter). ๐๐ฒ๐๐ฎ๐ถ๐น๐ ๐ผ๐ณ ๐ถ๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป:After parsing of .cif files (a textual file format describing the three-dimensional structures of molecules) the data is loaded into pandas dataframes. The majority of the operations supported by the developed module are then performed on the dataframes. Plotly library was used to visualize atoms, mass centers and partial charges of amino acids, and various protein representations. Matplotlib was used for 2D graphics (histograms, line plots, etc.). A significant computational challenge was calculating parameters like the surface area exposed to a solution in large structures. This required identifying all neighboring atoms within a specified radius. To efficiently handle this task, a specialized, partially parallel algorithm leveraging space partitioning was developed and implemented using Numba. This aspect of the project demanded advanced algorithmic skills and understanding of parallel computing concepts. Another interesting problem I had to face was that many structures deposited in Protein Data Bank use unconventional symmetry matrices and initial positions and orientations of the asymmetric structural unit (unique protein structures that are multiplied by application of symmetry matrices to obtain the whole assembly, e.g., virus), which makes comparing two structures of two protein assemblies extremely difficult even if they have the same symmetry and sometimes even correspond to the same species. Putting to work my knowledge of group theory, I developed a function that โrestoresโ such ill-defined structures by translating and rotating asymmetric structural units and replacing matrices with the proper ones.
๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ: The task was to determine which of the two protein structure assembly variants is more likely to appear in nature. The protein assemblies of cypoviruses that infect insects were studied. The genome of these viruses is protected by a spherical protein shell, which is embedded in an outer cubic protein crystal. Based on the theory of symmetry, our group proposed two potential variants of the structure of this assembly. I needed to compare them and ๐ฆ๐ด๐ต๐ข๐ฃ๐ญ๐ช๐ด๐ฉ ๐ธ๐ฉ๐ช๐ค๐ฉ ๐ท๐ข๐ณ๐ช๐ข๐ฏ๐ต ๐ฐ๐ง ๐ต๐ฉ๐ฆ ๐ด๐ฉ๐ฆ๐ญ๐ญ ๐ฆ๐ฎ๐ฃ๐ฆ๐ฅ๐ฅ๐ช๐ฏ๐จ ๐ช๐ด ๐ฎ๐ฐ๐ณ๐ฆ ๐ฑ๐ณ๐ฐ๐ฃ๐ข๐ฃ๐ญ๐ฆ/๐ฃ๐ช๐ฐ๐ญ๐ฐ๐จ๐ช๐ค๐ข๐ญ๐ญ๐บ ๐ณ๐ฆ๐ญ๐ฆ๐ท๐ข๐ฏ๐ต (๐ ๐ข๐ฏ๐ฅ ๐ ๐ช๐ฏ ๐ง๐ช๐จ๐ถ๐ณ๐ฆ 1). ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป: One of the main challenges of the problem stemmed from the size of the structure: the assembly of the protein shell and outer crystal had too many atoms to perform docking of the structures using publicly available software. Thus, I came up with a simple geometric approach and successfully implemented it. โข When solving the problem, I used Jupyter Notebook in combination with the python module for protein assembly analysis I developed (for more details, see my portfolio "Python module for protein structure analysis"). โข Structures of individual proteins comprising the assembly were taken from the Protein Data Bank. I based my analysis on the well-known fact that interaction strength between proteins is approximately proportional to the area of their contact (in other words, area of their interface). At the same time, the greater the volume of the intersection between two proteins in the specified configuration the less probable this configuration would be. Note, however, that small overlaps are acceptable since contacting proteins can deform to accommodate each other's structures. 1) First, I constructed arrays containing the coordinates of all atoms of the bulk protein crystal and the virus shell in two orientations. To do that, the structures of the individual proteins were multiplied by applying the rotation and translation matrices of the corresponding assemblies. 2) Proteins of the crystal were excluded if the degree of their overlap with the viral shell exceeded a certain threshold. Then the contact area between the virus and the protein crystal was calculated. 3) Figures 3 shows plots of the contact area and the number of crystal proteins in contact with the virus shell as functions of the allowed protein overlap threshold. 4) The plots showed that, in a major part of the region corresponding to biologically relevant overlap, orientation A corresponds to both a larger number of contacts between proteins and their total area, which indicates that it should be more stable and thus preferable from evolutionary perspective. The result of the study was confirmed by the analysis of electrostatic interactions between proteins, which I carried out using the developed python module. Moreover, results of my analysis were indirectly supported by a number of experimental data. Thanks to the developed model, we were able to identify a previously unknown function of one of the viral proteins, and also propose a mechanism of disassembly of the structure when it enters an environment favorable for infection. The results of my analysis were published in the high impact Q1 scientific journal Nanoscale Advances.
Doctor of Philosophy (PhD) in Solid State Physics
2014-01-01-2018-01-01
Doctor of Philosophy (PhD) in Physics
2019-01-01-2023-01-01