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Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server.
Harini, Kannan; Christoffer, Charles; Gromiha, M Michael; Kihara, Daisuke.
Afiliación
  • Harini K; Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
  • Christoffer C; Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
  • Gromiha MM; Department of Computer Science, Purdue University, West Lafayette, IN, USA.
  • Kihara D; Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
Methods Mol Biol ; 2690: 355-373, 2023.
Article en En | MEDLINE | ID: mdl-37450159
ABSTRACT
Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https//lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Tipo de estudio: Prognostic_studies Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: India

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Programas Informáticos / Biología Computacional Tipo de estudio: Prognostic_studies Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: India