dr inż. Rafał Madaj
Zainteresowania badawcze
- Zastosowanie technik dokowania molekularnego oraz dynamiki molekularnej w analizie zmian konformacyjnych białek oraz kompleksów ligand-białko.
- Mutacje Koneksyny 32 i ich wpływ na funkcjonalność kanału jonowego warunkującego chorobę Charcot-Marie-Tooth typu 1X.
Publikacje
Geoffrey, Ben AS.; Madaj, Rafał; Valluri, Pavan Preetham
In: Journal of Biomolecular Structure and Dynamics, pp. 1-8, 2022, ISSN: 0739-1102.
@article{RM2,
title = {QPoweredCompound2DeNovoDrugPropMax – a novel programmatic tool incorporating deep learning and \textit{in silico} methods for automated in silico bio-activity discovery for any compound of interest},
author = {Ben AS. Geoffrey and Rafał Madaj and Pavan Preetham Valluri},
doi = {10.1080/07391102.2021.2024450},
issn = {0739-1102},
year = {2022},
date = {2022-01-01},
journal = {Journal of Biomolecular Structure and Dynamics},
pages = {1-8},
abstract = {Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided below. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction have been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the presented work, compound-drug target interaction network data set from bindingDB has been used to train deep learning neural network and a multi class classification has been implemented to classify PubChem compound queried by the user into class labels of PBD IDs. This way target interaction prediction for PubChem compounds is carried out using deep learning. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction for the input CID. Further the tool also optimizes the compound of interest of the user toward drug likeness properties through a deep learning based structure optimization protocol. The tool also incorporates a feature to perform automated In Silico modelling to find the interaction between the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The program is hosted, supported and maintained at the following GitHub repository. https://github.com/bengeof/Compound2DeNovoDrugPropMax. Anticipating the use of quantum computing and quantum machine learning in drug discovery we use the Penny-lane interface to quantum hardware to turn classical Keras layers used in our machine/deep learning models into a quantum layer and introduce quantum layers into classical models to produce a quantum-classical machine/deep learning hybrid model of our tool and the code corresponding to the same is provided below. https://github.com/bengeof/QPoweredCompound2DeNovoDrugPropMax.
Kamiński, Kamil; Ludwiczak, Jan; Jasiński, Maciej; Bukala, Adriana; Madaj, Rafal; Szczepaniak, Krzysztof; Dunin-Horkawicz, Stanisław
Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins Journal Article
In: Briefings in Bioinformatics, vol. 23, 2022, ISSN: 1467-5463.
@article{SDH5,
title = {Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins},
author = {Kamil Kamiński and Jan Ludwiczak and Maciej Jasiński and Adriana Bukala and Rafal Madaj and Krzysztof Szczepaniak and Stanisław Dunin-Horkawicz},
doi = {10.1093/bib/bbab371},
issn = {1467-5463},
year = {2022},
date = {2022-01-01},
journal = {Briefings in Bioinformatics},
volume = {23},
abstract = {The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.
Szczupak, Patrycja; Radzikowska-Cieciura, Ewa; Kulik, Katarzyna; Madaj, Rafał; Sierant, Małgorzata; Krakowiak, Agnieszka; Nawrot, Barbara
Escherichia coli tRNA 2-selenouridine synthase SelU selects its prenyl substrate to accomplish its enzymatic function Journal Article
In: Bioorganic Chemistry, vol. 122, pp. 105739, 2022, ISSN: 00452068.
@article{RM1,
title = {Escherichia coli tRNA 2-selenouridine synthase SelU selects its prenyl substrate to accomplish its enzymatic function},
author = {Patrycja Szczupak and Ewa Radzikowska-Cieciura and Katarzyna Kulik and Rafał Madaj and Małgorzata Sierant and Agnieszka Krakowiak and Barbara Nawrot},
doi = {10.1016/j.bioorg.2022.105739},
issn = {00452068},
year = {2022},
date = {2022-01-01},
journal = {Bioorganic Chemistry},
volume = {122},
pages = {105739},
abstract = {Bacterial tRNA 2-selenouridine synthase (SelU) in vitro converts S2U-RNA to its selenium analog (Se2U-RNA) in a two-step process: (i) geranylation of S2U-RNA (with geranyl pyrophosphate, gePP), and (ii) selenation of the resulting geS2U-RNA (with the selenophosphate anion, SePO33−). Using an S2U-containing anticodon stem-loop fragment derived from tRNALys (S2U-RNA) and recombinant SelU with an MBP tag, we found that only geranyl (C10) pyrophosphate is the substrate for this enzyme, while other pyrophosphates such as isopentenyl (C5), dimethylallyl (C5), farnesyl (C15) and geranylgeranyl (C20) are not. Interestingly, methyl (C1)− and C5−, C10−, and C15-prenyl-containing S2U-RNAs (which were chemically obtained) underwent the selenation reaction promoted by SelU, although the Se2U-RNA product was obtained in decreasing yields in the following order: geranyl ≥ farnesyl > dimethylallyl ≫ methyl. Microscale thermophoresis showed an affinity between gePP and SelU in the micromolar range, while the other pyrophosphates tested, such as isopentenyl, dimethylallyl, farnesyl and geranylgeranyl, either did not bind to the protein or their binding affinity was above 1 mM. These results agree well with the in silico analysis, with gePP being the best binding substrate (the lowest relative free energy of binding (ΔG) and a small solvent-accessible surface area (SASA)). These results suggest that SelU has high substrate specificity for the prenylation reaction (only gePP is accepted), whereas there is little discrimination for the selenation reaction. We therefore suggest that only gePP and the geranylated tRNA serve as substrates for the conversion of 2-thio-tRNAs to 2-seleno-tRNAs, as it is found in the bacterial system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bacterial tRNA 2-selenouridine synthase (SelU) in vitro converts S2U-RNA to its selenium analog (Se2U-RNA) in a two-step process: (i) geranylation of S2U-RNA (with geranyl pyrophosphate, gePP), and (ii) selenation of the resulting geS2U-RNA (with the selenophosphate anion, SePO33−). Using an S2U-containing anticodon stem-loop fragment derived from tRNALys (S2U-RNA) and recombinant SelU with an MBP tag, we found that only geranyl (C10) pyrophosphate is the substrate for this enzyme, while other pyrophosphates such as isopentenyl (C5), dimethylallyl (C5), farnesyl (C15) and geranylgeranyl (C20) are not. Interestingly, methyl (C1)− and C5−, C10−, and C15-prenyl-containing S2U-RNAs (which were chemically obtained) underwent the selenation reaction promoted by SelU, although the Se2U-RNA product was obtained in decreasing yields in the following order: geranyl ≥ farnesyl > dimethylallyl ≫ methyl. Microscale thermophoresis showed an affinity between gePP and SelU in the micromolar range, while the other pyrophosphates tested, such as isopentenyl, dimethylallyl, farnesyl and geranylgeranyl, either did not bind to the protein or their binding affinity was above 1 mM. These results agree well with the in silico analysis, with gePP being the best binding substrate (the lowest relative free energy of binding (ΔG) and a small solvent-accessible surface area (SASA)). These results suggest that SelU has high substrate specificity for the prenylation reaction (only gePP is accepted), whereas there is little discrimination for the selenation reaction. We therefore suggest that only gePP and the geranylated tRNA serve as substrates for the conversion of 2-thio-tRNAs to 2-seleno-tRNAs, as it is found in the bacterial system.
Madaj, Rafał; Sroczyński, Witold; Sójka, Michał; Olejnik, Tomasz P; Sobiecka, Elżbieta
Batch Mode Reactor for 3,5-Dinitrosalicylic Acid Degradation by Phanerochaete chrysosporium Journal Article
In: Processes, vol. 9, pp. 105, 2021, ISSN: 2227-9717.
@article{RM6,
title = {Batch Mode Reactor for 3,5-Dinitrosalicylic Acid Degradation by Phanerochaete chrysosporium},
author = {Rafał Madaj and Witold Sroczyński and Michał Sójka and Tomasz P Olejnik and Elżbieta Sobiecka},
doi = {10.3390/pr9010105},
issn = {2227-9717},
year = {2021},
date = {2021-01-01},
journal = {Processes},
volume = {9},
pages = {105},
abstract = {A new batch mode reactor was constructed to conduct continuous biodegradation of 3,5-dinitrosalicylic acid. Various types of matrices with immobilized Phanerochaete chrysosporium were immersed in a solution containing pollutant and mineral nutrients. Three parameters were chosen to optimize the process. The nitrate and nitrite ions concentrations and HPLC analysis were used to prove the biodegradation of 3,5-dinitrosalicylic acid, and the mixed effects model using one-factor ANOVA was used for statistical calculations. The results showed the correlation between the initial pH, a medium composition, and the process time. In pH = 6.5, the degradation effectiveness was estimated at 99% decrease in the substrate within 14 days, while an 80% decrease of acid concentration was indicated in pH = 3.5 after 28 days of the process duration.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A new batch mode reactor was constructed to conduct continuous biodegradation of 3,5-dinitrosalicylic acid. Various types of matrices with immobilized Phanerochaete chrysosporium were immersed in a solution containing pollutant and mineral nutrients. Three parameters were chosen to optimize the process. The nitrate and nitrite ions concentrations and HPLC analysis were used to prove the biodegradation of 3,5-dinitrosalicylic acid, and the mixed effects model using one-factor ANOVA was used for statistical calculations. The results showed the correlation between the initial pH, a medium composition, and the process time. In pH = 6.5, the degradation effectiveness was estimated at 99% decrease in the substrate within 14 days, while an 80% decrease of acid concentration was indicated in pH = 3.5 after 28 days of the process duration.
Madaj, Rafal; Pawlowska, Roza; Chworos, Arkadiusz
In silico exploration of binding of selected bisphosphonate derivatives to placental alkaline phosphatase via docking and molecular dynamics Journal Article
In: Journal of Molecular Graphics and Modelling, vol. 103, pp. 107801, 2021, ISSN: 10933263.
@article{RM7,
title = {In silico exploration of binding of selected bisphosphonate derivatives to placental alkaline phosphatase via docking and molecular dynamics},
author = {Rafal Madaj and Roza Pawlowska and Arkadiusz Chworos},
doi = {10.1016/j.jmgm.2020.107801},
issn = {10933263},
year = {2021},
date = {2021-01-01},
journal = {Journal of Molecular Graphics and Modelling},
volume = {103},
pages = {107801},
abstract = {Bisphosphonates constitute a group of pyrophosphate analogues therapeutically active against bone diseases. Numerous studies confirm their anticancer and antimetastatic potential as well as ability to relieve pathological pain. Although this is a known class of compounds, many aspects of their action remain unexplained and their new interaction partners are still being discovered. Due to the structural similarity to pyrophosphate, their interaction with pyrophosphate-recognizing enzymes seems to be feasible. In current work, the placental alkaline phosphatase (PLAP) is considered as a potential target for these class of compounds. PLAP is one of the enzymes responsible for degradation of pyrophosphate with high clinical significance. An elevation of PLAP level are considered as a potential cancer marker. An in silico study of complexes formed between selected phosphate derivatives and PLAP was performed. It indicates that all tested compounds: alendronic acid, clodronic acid, etidronic acid, zoledronic acid, imidodiphosphoric acid, pyrophosphoric acid, medronic acid, chloromethylenediphosphonic acid and hypophosphoric acid form a complexes with PLAP, stabilized by hydrogen bonds, hydrophobic and van der Waals interactions. Zoledronic acid, drug used in prevention of bone complications during cancer treatment was found to have the lowest estimated energy of binding (-6.6 kcal/mol). In silico study yielded very low energy of binding also for hypophosphate, equal -6.4 kcal/mol, despite having no identified hydrogen bonds. Subsequent molecular dynamic simulations, followed by molecular mechanics generalized-born surface area with pairwise decomposition calculations confirmed the stability of protein-ligand complexes. The results indicate that selected phosphate derivatives may potentially interact with the enzyme, changing its function, what should be investigated during in vitro studies. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bisphosphonates constitute a group of pyrophosphate analogues therapeutically active against bone diseases. Numerous studies confirm their anticancer and antimetastatic potential as well as ability to relieve pathological pain. Although this is a known class of compounds, many aspects of their action remain unexplained and their new interaction partners are still being discovered. Due to the structural similarity to pyrophosphate, their interaction with pyrophosphate-recognizing enzymes seems to be feasible. In current work, the placental alkaline phosphatase (PLAP) is considered as a potential target for these class of compounds. PLAP is one of the enzymes responsible for degradation of pyrophosphate with high clinical significance. An elevation of PLAP level are considered as a potential cancer marker. An in silico study of complexes formed between selected phosphate derivatives and PLAP was performed. It indicates that all tested compounds: alendronic acid, clodronic acid, etidronic acid, zoledronic acid, imidodiphosphoric acid, pyrophosphoric acid, medronic acid, chloromethylenediphosphonic acid and hypophosphoric acid form a complexes with PLAP, stabilized by hydrogen bonds, hydrophobic and van der Waals interactions. Zoledronic acid, drug used in prevention of bone complications during cancer treatment was found to have the lowest estimated energy of binding (-6.6 kcal/mol). In silico study yielded very low energy of binding also for hypophosphate, equal -6.4 kcal/mol, despite having no identified hydrogen bonds. Subsequent molecular dynamic simulations, followed by molecular mechanics generalized-born surface area with pairwise decomposition calculations confirmed the stability of protein-ligand complexes. The results indicate that selected phosphate derivatives may potentially interact with the enzyme, changing its function, what should be investigated during in vitro studies.
Madaj, Rafal; Geoffrey, Ben; Sanker, Akhil; Valluri, Pavan Preetham
Target2DeNovoDrug: a novel programmatic tool for in silico -deep learning based de novo drug design for any target of interest Journal Article
In: Journal of Biomolecular Structure and Dynamics, pp. 1-6, 2021, ISSN: 0739-1102.
@article{RM5,
title = {Target2DeNovoDrug: a novel programmatic tool for \textit{in silico} -deep learning based \textit{de novo} drug design for any target of interest},
author = {Rafal Madaj and Ben Geoffrey and Akhil Sanker and Pavan Preetham Valluri},
doi = {10.1080/07391102.2021.1898474},
issn = {0739-1102},
year = {2021},
date = {2021-01-01},
journal = {Journal of Biomolecular Structure and Dynamics},
pages = {1-6},
abstract = {The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrug},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates in silico and deep learning based approaches for de novo drug design for any target of interest has been reported. Once the user specifies the target of interest in the form of a representative amino acid sequence or corresponding nucleotide sequence, the programmatic workflow of the tool generates compounds from the PubChem ligand library and novel SMILES of compounds not present in any ligand library but are likely to be active against the target. Following this, the tool performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results of the protein-ligand interaction in the working folder of the user. Further, for the protein-ligand complex associated with the best protein-ligand interaction, the tool performs an automated Molecular Dynamics (MD) protocol and generates plots such as RMSD (Root Mean Square Deviation) which reveal the stability of the complex. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High-Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained, and supported at the GitHub repository given in the link below https://github.com/bengeof/Target2DeNovoDrug
Madaj, Rafal; Gostynski, Bartlomiej; Pawlowska, Roza; Chworos, Arkadiusz
In: Biomolecules, vol. 11, pp. 1104, 2021, ISSN: 2218-273X.
@article{RM4,
title = {Tissue-Nonspecific Alkaline Phosphatase (TNAP) as the Enzyme Involved in the Degradation of Nucleotide Analogues in the Ligand Docking and Molecular Dynamics Approaches},
author = {Rafal Madaj and Bartlomiej Gostynski and Roza Pawlowska and Arkadiusz Chworos},
doi = {10.3390/biom11081104},
issn = {2218-273X},
year = {2021},
date = {2021-01-01},
journal = {Biomolecules},
volume = {11},
pages = {1104},
abstract = {Tissue-nonspecific alkaline phosphatase (TNAP) is known to be involved in the degradation of extracellular ATP via the hydrolysis of pyrophosphate (PPi). We investigated, using three different computational methods, namely molecular docking, thermodynamic integration (TI) and conventional molecular dynamics (MD), whether TNAP may also be involved in the utilization of β,γ-modified ATP analogues. For that, we analyzed the interaction of bisphosphonates with this enzyme and evaluated the obtained structures using in silico studies. Complexes formed between pyrophosphate, hypophosphate, imidodiphosphate, methylenediphosphonic acid monothiopyrophosphate, alendronate, pamidronate and zoledronate with TNAP were generated and analyzed based on ligand docking, molecular dynamics and thermodynamic integration. The obtained results indicate that all selected ligands show high affinity toward this enzyme. The forming complexes are stabilized through hydrogen bonds, electrostatic interactions and van der Waals forces. Short- and middle-term molecular dynamics simulations yielded very similar affinity results and confirmed the stability of the protein and its complexes. The results suggest that certain effectors may have a significant impact on the enzyme, changing its properties.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tissue-nonspecific alkaline phosphatase (TNAP) is known to be involved in the degradation of extracellular ATP via the hydrolysis of pyrophosphate (PPi). We investigated, using three different computational methods, namely molecular docking, thermodynamic integration (TI) and conventional molecular dynamics (MD), whether TNAP may also be involved in the utilization of β,γ-modified ATP analogues. For that, we analyzed the interaction of bisphosphonates with this enzyme and evaluated the obtained structures using in silico studies. Complexes formed between pyrophosphate, hypophosphate, imidodiphosphate, methylenediphosphonic acid monothiopyrophosphate, alendronate, pamidronate and zoledronate with TNAP were generated and analyzed based on ligand docking, molecular dynamics and thermodynamic integration. The obtained results indicate that all selected ligands show high affinity toward this enzyme. The forming complexes are stabilized through hydrogen bonds, electrostatic interactions and van der Waals forces. Short- and middle-term molecular dynamics simulations yielded very similar affinity results and confirmed the stability of the protein and its complexes. The results suggest that certain effectors may have a significant impact on the enzyme, changing its properties.
Geoffrey, Ben; Sanker, Akhil; Madaj, Rafal; Tresanco, Mario Sergio Valdés; Upadhyay, Manish; Gracia, Judith
In: Journal of Biomolecular Structure and Dynamics, pp. 1-9, 2020, ISSN: 0739-1102.
@article{RM8,
title = {A program to automate the discovery of drugs for West Nile and Dengue virus—programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated \textit{in silico} modelling},
author = {Ben Geoffrey and Akhil Sanker and Rafal Madaj and Mario Sergio Valdés Tresanco and Manish Upadhyay and Judith Gracia},
doi = {10.1080/07391102.2020.1856185},
issn = {0739-1102},
year = {2020},
date = {2020-01-01},
journal = {Journal of Biomolecular Structure and Dynamics},
pages = {1-9},
abstract = {Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning-based AutoQSAR algorithm to generate drug leads for the flaviviruses—Dengue and West Nile. The drug leads generated by the program are fed as programmatic inputs to AutoDock Vina package for automated in silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning-based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated, each time the program is run, are reflective of the constantly growing PubChem database which is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue viruses. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus was modelled in an automated way through the tool. The results are stored in the working folder of the user. Thus, our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning-based AutoQSAR algorithm and an automated in silico modelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus. The program is hosted, maintained and supported at the GitHub repository link given below},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning-based AutoQSAR algorithm to generate drug leads for the flaviviruses—Dengue and West Nile. The drug leads generated by the program are fed as programmatic inputs to AutoDock Vina package for automated in silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning-based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated, each time the program is run, are reflective of the constantly growing PubChem database which is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue viruses. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus was modelled in an automated way through the tool. The results are stored in the working folder of the user. Thus, our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning-based AutoQSAR algorithm and an automated in silico modelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus. The program is hosted, maintained and supported at the GitHub repository link given below
Madaj, R; Sobiecka, E; Kalinowska, H
Lindane, kepone and pentachlorobenzene: chloropesticides banned by Stockholm convention Journal Article
In: International Journal of Environmental Science and Technology, vol. 15, pp. 471-480, 2018, ISSN: 1735-1472.
@article{RM9,
title = {Lindane, kepone and pentachlorobenzene: chloropesticides banned by Stockholm convention},
author = {R Madaj and E Sobiecka and H Kalinowska},
doi = {10.1007/s13762-017-1417-9},
issn = {1735-1472},
year = {2018},
date = {2018-01-01},
journal = {International Journal of Environmental Science and Technology},
volume = {15},
pages = {471-480},
abstract = {Persistent organic pollutants are a serious problem to the environment due to their toxicity to both fauna and flora. Extremely resistant to biodegradation and prone to transfer through long distances via atmosphere can contaminate almost any place in the planet. They tend to bioaccumulate in fat tissue due to their lipophilicity and seriously affect poisoned organism’s nervous, hepatic, reproductive or hormonal system. Since 2009, due to the Stockholm convention on persistent organic pollutants production and utilisation of certain halogenated pesticides has been prohibited. This group includes hexachlorocyclohexane, chlordecone (kepone) and pentachlorobenzene. All of these chloropesticides pose a serious threat to environment, and careful control of their production and release to the environment is required. This paper is a review of physical and chemical properties as well as sources in environment, impact on animal organisms, methods of degradation of most broadly used chlorinated persistent organic pollutants and suggestions concerning their utilisation.},
keywords = {},
pubstate = {published},
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Persistent organic pollutants are a serious problem to the environment due to their toxicity to both fauna and flora. Extremely resistant to biodegradation and prone to transfer through long distances via atmosphere can contaminate almost any place in the planet. They tend to bioaccumulate in fat tissue due to their lipophilicity and seriously affect poisoned organism’s nervous, hepatic, reproductive or hormonal system. Since 2009, due to the Stockholm convention on persistent organic pollutants production and utilisation of certain halogenated pesticides has been prohibited. This group includes hexachlorocyclohexane, chlordecone (kepone) and pentachlorobenzene. All of these chloropesticides pose a serious threat to environment, and careful control of their production and release to the environment is required. This paper is a review of physical and chemical properties as well as sources in environment, impact on animal organisms, methods of degradation of most broadly used chlorinated persistent organic pollutants and suggestions concerning their utilisation.