Topic
Talos
About: Talos is a research topic. Over the lifetime, 14 publications have been published within this topic receiving 5504 citations.
Papers
TL;DR: Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%, and addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%.
Abstract: NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between 13C, 15N and 1H chemical shifts and backbone torsion angles ϕ and ψ (Cornilescu et al. J Biomol NMR 13 289–302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted ϕ and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy.
2,619 citations
TL;DR: The structure of PR was solved in the short-chain lipid diC7PC combining long-range NOEs with restraints derived from paramagneticrelaxation enhancement (PRE) and residual dipolar cou-plings (RDCs) and the loop betweenhelices D and E is longer than predicted by the secondary structure prediction program TMHMM.
Abstract: and we show herein the de novo structure ofthegreenvariantofproteorhodopsinsolvedbysolutionNMRspectroscopy.The structure of PR (Figure 1) was solved in the short-chain lipid diC7PC (diheptanoyl-phosphocholine) combininglong-range NOEs with restraints derived from paramagneticrelaxation enhancement (PRE) and residual dipolar cou-plings (RDCs). The seven transmembrane helices are con-nected by short loops. Instead of the anti-parallel b-sheet thatis observed between helices B and C in other microbialrhodopsins, torsion angles derived from the protein backbonedihedral angle prediction program TALOS+ suggest that PRresidues G87–P90 form a short b-turn. The loop betweenhelices D and E is longer than predicted by the secondarystructure prediction program TMHMM.
178 citations
TL;DR: The results show that reasonably reliable secondary-structure information can be obtained from 1H secondary chemical shifts (SCS) alone by using the sum of 1Hα and 1HN SCS rather than by TALOS.
Abstract: 1H-detected solid-state NMR experiments feasible at fast magic-angle spinning (MAS) frequencies allow accessing 1H chemical shifts of proteins in solids, which enables their interpretation in terms of secondary structure. Here we present 1H and 13C-detected NMR spectra of the RNA polymerase subunit Rpo7 in complex with unlabeled Rpo4 and use the 13C, 15N, and 1H chemical-shift values deduced from them to study the secondary structure of the protein in comparison to a known crystal structure. We applied the automated resonance assignment approach FLYA including 1H-detected solid-state NMR spectra and show its success in comparison to manual spectral assignment. Our results show that reasonably reliable secondary-structure information can be obtained from 1H secondary chemical shifts (SCS) alone by using the sum of 1Hα and 1HN SCS rather than by TALOS. The confidence, especially at the boundaries of the observed secondary structure elements, is found to increase when evaluating 13C chemical shifts, here either by using TALOS or in terms of 13C SCS.
15 citations
TL;DR: SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space, and can be seen as a complement to currently available methods.
Abstract: We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods.
14 citations
TL;DR: Targeted adversarial learning optimized sampling (TALOS) as mentioned in this paper combines the strengths of statistical mechanics and deep learning to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free energy barrier is lowered.
Abstract: Boosting transitions of rare events is critical to modern-day simulations of complex dynamic systems. We present a novel approach to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free energy barrier is lowered. The new approach, called targeted adversarial learning optimized sampling (TALOS) combines the strengths of statistical mechanics and deep learning. By casting the enhanced sampling problem as a competing game between a real sampling engine and a virtual discriminator, TALOS enables unsupervised construction of bias potential on an arbitrary dimensional space and seeks for an optimal transport plan that transforms the system into target. Through multiple experiments we show that on-the-fly training of TALOS benefits from the state-of-art optimization techniques in deep learning, thus is efficient, robust and interpretable. Additionally, TALOS is closely connected to actor-critic reinforcement learning, giving rise to a new approach to manipulating the Hamiltonian systems via deep learning.
10 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2019 | 3 |
| 2011 | 1 |
| 2009 | 2 |
| 2007 | 2 |
| 2006 | 1 |