20.500.12556/RUL-166791 Knots and $\theta$-curves identification in polymeric chains and native proteins using neural networks Entanglement in proteins is a fascinating structural motif that is neither easy to detect via traditional methods nor fully understood. Recent advancements in AI-driven models have predicted that millions of proteins could potentially have a nontrivial topology. Herein, we have shown that long short-term memory (LSTM)-based neural networks (NN) architecture can be applied to detect, classify, and predict entanglement not only in closed polymeric chains but also in polymers and protein-like structures with open knots, actual protein configurations, and also ▫$\theta$▫-curves motifs. The analysis revealed that the LSTM model can predict classes (up to the ▫$6_1$▫ knot) accurately for closed knots and open polymeric chains, resembling real proteins. In the case of open knots formed by protein-like structures, the model displays robust prediction capabilities with an accuracy of 99%. Moreover, the LSTM model with proper features, tested on hundreds of thousands of knotted and unknotted protein structures with different architectures predicted by AlphaFold 2, can distinguish between the trivial and nontrivial topology of the native state of the protein with an accuracy of 93%. machine learning topology protein databases entanglements open knots closed knots strojno učenje topologija proteinska baza podatkov zavozlanost odprti vozli sklenjeni vozli true false true Angleški jezik Slovenski jezik Članek v reviji 2025-01-24 14:08:44 2025-01-24 14:08:47 2025-01-31 10:07:20 0000-00-00 00:00:00 2024 0 0 Str. 4599-4608 iss. 9 Vol. 57 May 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:004.725.4 0024-9297 10.1021/acs.macromol.3c02479 194735875 Bruno_da_Silva_..._Knots_and_θ‑Curves_....pdf Bruno_da_Silva_..._Knots_and_θ‑Curves_....pdf 1 43918B104CED420C3C162B307FB6B591 beb697265ad14b8193b452f620bd822a5247420d6aaa45272fbed9ada4d7bb04 ceb52d99-da53-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=198703 https://pubs.acs.org/doi/10.1021/acs.macromol.3c02479 1 8ccd670c-da53-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=198702 Fakulteta za strojništvo Fakulteta za matematiko in fiziko 0 0 0