Homology modeling and docking of AahII-Nanobody complexes reveal the epitope binding site on AahII scorpion toxin
- Ksouri, Ayoub, Ghedira, Kais, Abderrazek, Rahma Ben, Shankar, B A Gowri, Benkahla, Alia, Tastan Bishop, Özlem, Bouhaouala-Zahar, Balkis
- Authors: Ksouri, Ayoub , Ghedira, Kais , Abderrazek, Rahma Ben , Shankar, B A Gowri , Benkahla, Alia , Tastan Bishop, Özlem , Bouhaouala-Zahar, Balkis
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/124604 , vital:35637 , https://doi.10.1016/j.bbrc.2018.01.036
- Description: Scorpion envenoming and its treatment is a public health problem in many parts of the world due to highly toxic venom polypeptides diffusing rapidly within the body of severely envenomed victims. Recently, 38 AahII-specific Nanobody sequences (Nbs) were retrieved from which the performance of NbAahII10 nanobody candidate, to neutralize the most poisonous venom compound namely AahII acting on sodium channels, was established. Herein, structural computational approach is conducted to elucidate the Nb-AahII interactions that support the biological characteristics, using Nb multiple sequence alignment (MSA) followed by modeling and molecular docking investigations (RosettaAntibody, ZDOCK software tools). Sequence and structural analysis showed two dissimilar residues of NbAahII10 CDR1 (Tyr27 and Tyr29) and an inserted polar residue Ser30 that appear to play an important role. Indeed, CDR3 region of NbAahII10 is characterized by a specific Met104 and two negatively chargedresidues Asp115 and Asp117. Complex dockings reveal that NbAahII17 and NbAahII38 share one common binding site on the surface of the AahII toxin divergent from the NbAahII10 one's. At least, a couple of NbAahII10 e AahII residue interactions (Gln38 e Asn44 and Arg62, His64, respectively) are mainly involved in the toxic AahII binding site. Altogether, this study gives valuable insights in the design and development of next generation of antivenom.
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- Authors: Ksouri, Ayoub , Ghedira, Kais , Abderrazek, Rahma Ben , Shankar, B A Gowri , Benkahla, Alia , Tastan Bishop, Özlem , Bouhaouala-Zahar, Balkis
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/124604 , vital:35637 , https://doi.10.1016/j.bbrc.2018.01.036
- Description: Scorpion envenoming and its treatment is a public health problem in many parts of the world due to highly toxic venom polypeptides diffusing rapidly within the body of severely envenomed victims. Recently, 38 AahII-specific Nanobody sequences (Nbs) were retrieved from which the performance of NbAahII10 nanobody candidate, to neutralize the most poisonous venom compound namely AahII acting on sodium channels, was established. Herein, structural computational approach is conducted to elucidate the Nb-AahII interactions that support the biological characteristics, using Nb multiple sequence alignment (MSA) followed by modeling and molecular docking investigations (RosettaAntibody, ZDOCK software tools). Sequence and structural analysis showed two dissimilar residues of NbAahII10 CDR1 (Tyr27 and Tyr29) and an inserted polar residue Ser30 that appear to play an important role. Indeed, CDR3 region of NbAahII10 is characterized by a specific Met104 and two negatively chargedresidues Asp115 and Asp117. Complex dockings reveal that NbAahII17 and NbAahII38 share one common binding site on the surface of the AahII toxin divergent from the NbAahII10 one's. At least, a couple of NbAahII10 e AahII residue interactions (Gln38 e Asn44 and Arg62, His64, respectively) are mainly involved in the toxic AahII binding site. Altogether, this study gives valuable insights in the design and development of next generation of antivenom.
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Development of Bioinformatics Infrastructure for Genomics Research:
- Mulder, Nicola J, Adebiyi, Ezekiel, Adebiyi, Marion, Adeyemi, Seun, Ahmed, Azza, Ahmed, Rehab, Akanle, Bola, Alibi, Mohamed, Armstrong, Don L, Aron, Shaun, Ashano, Efejiro, Baichoo, Shakuntala, Benkahla, Alia, Brown, David K, Chimusa, Emile Rugamika, Fadlelmola, Faisal M, Falola, Dare, Fatumo, Segun, Ghedira, Kais, Ghouila, Amel, Hazelhurst, Scott, Itunuoluwa Isewon, Segun Jung, Kassim, Samar Kamal, Kayondo, Jonathan K, Mbiyavanga, Mamana, Meintjes, Ayton, Mohammed, Somia, Mosaku, Abayomi, Moussa, Ahmed, Muhammd, Mustafa, Mungloo-Dilmohamud, Zahra, Nashiru, Oyekanmi, Odia, Trust, Okafor, Adaobi, Oladipo, Olaleye, Osamor, Victor, Oyelade, Jellili, Sadki, Khalid, Salifu, Samson Pandam, Soyemi, Jumoke, Panji, Sumir, Radouani, Fouzia, Souiai, Oussama, Tastan Bishop, Özlem
- Authors: Mulder, Nicola J , Adebiyi, Ezekiel , Adebiyi, Marion , Adeyemi, Seun , Ahmed, Azza , Ahmed, Rehab , Akanle, Bola , Alibi, Mohamed , Armstrong, Don L , Aron, Shaun , Ashano, Efejiro , Baichoo, Shakuntala , Benkahla, Alia , Brown, David K , Chimusa, Emile Rugamika , Fadlelmola, Faisal M , Falola, Dare , Fatumo, Segun , Ghedira, Kais , Ghouila, Amel , Hazelhurst, Scott , Itunuoluwa Isewon , Segun Jung , Kassim, Samar Kamal , Kayondo, Jonathan K , Mbiyavanga, Mamana , Meintjes, Ayton , Mohammed, Somia , Mosaku, Abayomi , Moussa, Ahmed , Muhammd, Mustafa , Mungloo-Dilmohamud, Zahra , Nashiru, Oyekanmi , Odia, Trust , Okafor, Adaobi , Oladipo, Olaleye , Osamor, Victor , Oyelade, Jellili , Sadki, Khalid , Salifu, Samson Pandam , Soyemi, Jumoke , Panji, Sumir , Radouani, Fouzia , Souiai, Oussama , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148239 , vital:38722 , DOI: 10.1016/j.gheart.2017.01.005
- Description: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.
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- Authors: Mulder, Nicola J , Adebiyi, Ezekiel , Adebiyi, Marion , Adeyemi, Seun , Ahmed, Azza , Ahmed, Rehab , Akanle, Bola , Alibi, Mohamed , Armstrong, Don L , Aron, Shaun , Ashano, Efejiro , Baichoo, Shakuntala , Benkahla, Alia , Brown, David K , Chimusa, Emile Rugamika , Fadlelmola, Faisal M , Falola, Dare , Fatumo, Segun , Ghedira, Kais , Ghouila, Amel , Hazelhurst, Scott , Itunuoluwa Isewon , Segun Jung , Kassim, Samar Kamal , Kayondo, Jonathan K , Mbiyavanga, Mamana , Meintjes, Ayton , Mohammed, Somia , Mosaku, Abayomi , Moussa, Ahmed , Muhammd, Mustafa , Mungloo-Dilmohamud, Zahra , Nashiru, Oyekanmi , Odia, Trust , Okafor, Adaobi , Oladipo, Olaleye , Osamor, Victor , Oyelade, Jellili , Sadki, Khalid , Salifu, Samson Pandam , Soyemi, Jumoke , Panji, Sumir , Radouani, Fouzia , Souiai, Oussama , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148239 , vital:38722 , DOI: 10.1016/j.gheart.2017.01.005
- Description: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.
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Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks:
- Amamuddy, Olivier S, Bishop, Nigel T, Tastan Bishop, Özlem
- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
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- Authors: Amamuddy, Olivier S , Bishop, Nigel T , Tastan Bishop, Özlem
- Date: 2017
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/148261 , vital:38724 , https://0-doi.org.wam.seals.ac.za/10.1186/s12859-017-1782-x
- Description: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.
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