[NMR paper] Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data
Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data
Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling...
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10-15-2024 11:40 PM
[NMR paper] Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Fragment-Based Deep Learning for Simultaneous Prediction of Polarizabilities and NMR Shieldings of Macromolecules and Their Aggregates
Simultaneous prediction of the molecular response properties, such as polarizability and the NMR shielding constant, at a low computational cost is an unresolved issue. We propose to combine a linear-scaling generalized energy-based fragmentation (GEBF) method and deep learning (DL) with both molecular and atomic information-theoretic approach (ITA) quantities as effective descriptors. In GEBF, the total molecular polarizability can be assembled as a linear...
[NMR paper] Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative...
[NMR paper] NMRNet: A deep learning approach to automated peak picking of protein NMR spectra.
NMRNet: A deep learning approach to automated peak picking of protein NMR spectra.
NMRNet: A deep learning approach to automated peak picking of protein NMR spectra.
Bioinformatics. 2018 Mar 14;:
Authors: Klukowski P, Augoff M, Zieba M, Drwal M, Gonczarek A, Walczak MJ
Abstract
Motivation: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would...
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03-17-2018 12:12 PM
Solid state NMR chemical shift assignment and conformational analysis of a cellulose binding protein facilitated by optimized glycerol enrichment
Solid state NMR chemical shift assignment and conformational analysis of a cellulose binding protein facilitated by optimized glycerol enrichment
Abstract
Magic-angle spinning solid-state NMR has been applied to study CBM3bâ??Cbh9A (CBM3b), a cellulose binding module protein belonging to family 3b. It is a 146-residue protein having a unique nine-stranded β-sandwich fold, in which 35Â*% of the structure is in a β-sheet conformation and the remainder of the protein is composed of loops and unstructured regions. Yet, the protein can be crystalized...
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06-19-2014 10:21 PM
[NMR paper] Solid state NMR chemical shift assignment and conformational analysis of a cellulose binding protein facilitated by optimized glycerol enrichment.
Solid state NMR chemical shift assignment and conformational analysis of a cellulose binding protein facilitated by optimized glycerol enrichment.
http://www.bionmr.com//www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--production.springer.de-OnlineResources-Logos-springerlink.gif Related Articles Solid state NMR chemical shift assignment and conformational analysis of a cellulose binding protein facilitated by optimized glycerol enrichment.
J Biomol NMR. 2014 May 14;
Authors: Ivanir H, Goldbourt A
Abstract
Magic-angle spinning...
Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction - Science
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