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  • VILNIUS TECH Institutional Repository based on DSpace open source digital service, collects, preserves, and distributes VILNIUS TECH scientific and other production. It serves as important tool for adhering to and encouraging the best Open Science practices, preserving University's legacy, and facilitating knowledge dissemination and scholarly communication.

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  • Jan Evangelista Purkyně University Digital Repository. It is intended primarily for the storage, long-term preservation, and free access to full-text documents associated with research and educational activities within the University.

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  • SoluProtMutDB is a comprehensive, manually curated database of the protein solubility data. Low protein solubility presents major challenges to industrial applications and is often reported to be behind many human diseases. Understanding how mutations affect protein solubility can therefore help elucidate the mechanisms associated with the development of human diseases and better utilize protein engineering in industrial applications. Multiple factors may play a role here: the presence of a chaperone or co-factor required for correct protein folding, unnatural physiological conditions such as high temperature, pH, or protein concentration, tendency of a protein to aggregate due to aggregation-prone regions, etc. The predictive power of the existing protein engineering tools is often compromised by limited experimental data available for rigorous training and testing of solubility predictions. The published data used for solubility prediction upon mutation are usually scattered in the literature and had to be collected manually. The goal of the SoluProtMut database is to collect the reported evidence of solubility changes upon mutations from published sources to guide future protein engineering effort in producing soluble protein variants. The database currently contains data previously used for training Machine Learning-based predictors, such as PON-Sol, CamSol, AGGRESCAN3D, OptSolMut, as well as recently published datasets. We are providing manually curated and reliable data in the standardized format which are pre-processed for machine learning applications.

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  • This site provides access to the research output of the institution. The interface is available in Lithuanian and English.

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  • This site provides users with access to the output of the institution. The interface is available in Czech and English.

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  • This site provides access to the research outputs and ETDs of the Lithuanian Centre for Social Sciences. The interface is available in English and Lithuanian.

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  • This site provides access to the research outputs of the institution. The interface is available in English and Lithuanian. Users may set up RSS feeds to be alerted to new content.

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411 Data sources
  • VILNIUS TECH Institutional Repository based on DSpace open source digital service, collects, preserves, and distributes VILNIUS TECH scientific and other production. It serves as important tool for adhering to and encouraging the best Open Science practices, preserving University's legacy, and facilitating knowledge dissemination and scholarly communication.

    more_vert
  • Jan Evangelista Purkyně University Digital Repository. It is intended primarily for the storage, long-term preservation, and free access to full-text documents associated with research and educational activities within the University.

    more_vert
  • SoluProtMutDB is a comprehensive, manually curated database of the protein solubility data. Low protein solubility presents major challenges to industrial applications and is often reported to be behind many human diseases. Understanding how mutations affect protein solubility can therefore help elucidate the mechanisms associated with the development of human diseases and better utilize protein engineering in industrial applications. Multiple factors may play a role here: the presence of a chaperone or co-factor required for correct protein folding, unnatural physiological conditions such as high temperature, pH, or protein concentration, tendency of a protein to aggregate due to aggregation-prone regions, etc. The predictive power of the existing protein engineering tools is often compromised by limited experimental data available for rigorous training and testing of solubility predictions. The published data used for solubility prediction upon mutation are usually scattered in the literature and had to be collected manually. The goal of the SoluProtMut database is to collect the reported evidence of solubility changes upon mutations from published sources to guide future protein engineering effort in producing soluble protein variants. The database currently contains data previously used for training Machine Learning-based predictors, such as PON-Sol, CamSol, AGGRESCAN3D, OptSolMut, as well as recently published datasets. We are providing manually curated and reliable data in the standardized format which are pre-processed for machine learning applications.

    more_vert
  • This site provides access to the research output of the institution. The interface is available in Lithuanian and English.

    more_vert
  • This site provides users with access to the output of the institution. The interface is available in Czech and English.

    more_vert
  • This site provides access to the research outputs and ETDs of the Lithuanian Centre for Social Sciences. The interface is available in English and Lithuanian.

    more_vert
  • more_vert
  • This site provides access to the research outputs of the institution. The interface is available in English and Lithuanian. Users may set up RSS feeds to be alerted to new content.

    more_vert
  • more_vert
  • more_vert
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