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3 results for “Backmapping”
HEroBM: a deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures
<p><span>Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the</span><br><span>study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged</span><br><span>as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending</span><br><span>simulation timescales by simplifying system representation. However, CG approaches involve a trade-</span><br><span>off: they sacrifice atomistic details that may be crucial for understanding the underlying processes.</span><br><span>To address this challenge, a recommended strategy is to identify key CG conformations and employ</span><br><span>backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield</span><br><span>suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In</span><br><span>contrast, machine learning techniques offer higher accuracy but may lack transferability between</span><br><span>systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable</span><br><span>method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve</span><br><span>high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a</span><br><span>versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded</span><br><span>in local principles, HEroBM spans the entire chemical space and can be applied across systems of</span><br><span>varying composition and sizes. We demonstrate the versatility of our framework through a range of</span><br><span>biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach</span><br><span>accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small</span><br><span>molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables</span><br><span>researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented</span><br><span>avenues for molecular investigations.</span></p>
FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping
<p>Train/validation/test splits for protein and DNA-protein structures used for FlowBack training and evaluation as well as pre-processed features.</p>
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
<p>Training, test datasets of the paper "Chemically Transferable Generative Backmapping of Coarse-Grained Proteins".</p> <p>The PDB files are downloaded from PED [1] and processed according to the instruction described in our paper [link]. </p> <p> </p> <p>[1] <strong>PED in 2021: a major update of the Protein Ensemble Database for intrinsically disordered proteins</strong></p> <p>Lazar, T., Martı́nez-Pérez, E., Quaglia, F., Hatos, A., Chemes, L.B., Iserte, J.A., Méndez, N.A., Garrone, N.A., Saldaño, T.E., Marchetti, J., Velez Rueda, A.J., Bernadó, P., Blackledge, M., Cordeiro, T.N., Fagerberg, E., Forman-Kay, J.D., Fornasari, M.S., Gibson, T.J., Gomes, G-N.W., Gradinaru, C.C., Head-Gordon, T., Ringkjøbing Jensen, M., Lemke, E.A., Longhi, S., Marino-Buslje, C., Minervini, G., Mittag, T., Monzon, A.M., Pappu, R.V., Parisi, G., Ricard-Blum, S., Ruff, K.M., Salladini, E., Skepö, M., Svergun, D., Vallet, S.D., Varadi, M., Tompa, P., Tosatto, S.C.E., Piovesan D.</p> <p>(2021) <em>Nucleic Acids Research</em>, Database Issue, <a href="https://www.ncbi.nlm.nih.gov/pubmed/33305318"><strong>PubMed </strong><em>link</em></a>, <a href="https://academic.oup.com/nar/article/49/D1/D404/6030232"><strong>NAR </strong><em>link</em></a></p> <p> </p> <p> </p>
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