[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ background_color=”#072c72″ border_color_all=”#3255c9″ text_orientation=”right” background_layout=”dark” custom_padding=”20px|15px|15px|” z_index_tablet=”500″]<\/p>\n
2024 <\/em><\/p>\n Master’s Projects<\/p>\n @Biology<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”3.22.1″ text_orientation=”right” z_index_tablet=”500″]<\/p>\n #Transformers network finetuning<\/p>\n #Structural bioinformatics<\/p>\n #Protein\/peptide prediction interaction<\/p>\n #Knottin<\/p>\n \u00a0<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n The Project explores the design of protein knottins to target P-selectin and integrins involved in vaso-occlusive crises in sickle cell disease (SCD). Leveraging advanced AI technologies such as AlphaFold2, AlphaFold3, RFdiffusion, and ProteinMPNN, integrins \u03b1M\u03b22, \u03b14\u03b21, \u03b1L\u03b22, \u03b1V\u03b23, and P-selectin were modeled. These deep learning tools enabled the engineering of knottins to block pathological cell adhesion interactions. The use of AI and deep learning led to promising candidates, particularly for binding the Mac-1 integrin. Further experimental validation is needed to confirm these findings, potentially leading to novel, effective therapeutic approaches for SCD.<\/em><\/p>\n <\/p>\n Professor at Universit\u00e9 Paris Cit\u00e9<\/p>\n Biologie Int\u00e9gr\u00e9e du Globule Rouge Azouzi Slim<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_margin=”120px||” admin_label=”Row” _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.22.1″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”33″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″ locked=”off”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 2024 Master’s Projects@Biology #Transformers network finetuning#Structural bioinformatics#Protein\/peptide prediction interaction#Knottin\u00a0 Project Summary The Project explores the design of protein knottins to target P-selectin and integrins involved in vaso-occlusive crises in sickle cell disease (SCD). Leveraging advanced AI technologies such as AlphaFold2, AlphaFold3, RFdiffusion, and ProteinMPNN, integrins \u03b1M\u03b22, \u03b14\u03b21, \u03b1L\u03b22, \u03b1V\u03b23, and P-selectin were modeled. These deep… Lire la suite Towards design of protein knottin using deep learning<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2263,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[51,33,1,29],"tags":[],"class_list":["post-1530","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-51","category-biology","category-diip","category-masters-internship"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1530"}],"version-history":[{"count":6,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1530\/revisions"}],"predecessor-version":[{"id":3153,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/1530\/revisions\/3153"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2263"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=1530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=1530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Project Summary<\/strong><\/h3>\n
Jean-Christophe Gelly
<\/strong>jean-christophe.gelly@wordpress-test.app.u-pariscite.fr<\/h3>\n
INSERM UMR_S1134 – Universit\u00e9 Paris Cit\u00e9
H\u00f4pital Necker APHP | B\u00e2timent Lavoisier
149 Rue de S\u00e8vres, 75015 Paris<\/p>\nProjects in the same discipline
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