Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Non canonical amino acids (NCAAs) occupy an important place, both in natural biology and synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training datasets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs. We discuss the various aspects of parameterizing a NCAA for use in Rosetta, identifying rotamer distribution modeling as one of the most impactful factors of NCAA parameterization on Rosetta performance. To this end, we also present FakeRotLib, a method which uses statistical fitting of small molecule conformer to create rotamer distributions. We find that FakeRotLib outperforms existing methods in a fraction of the time and is able to parameterize NCAA types previously unmodeled by Rosetta.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908200 | PMC |
| http://dx.doi.org/10.1101/2025.02.27.640629 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Non canonical amino acids (NCAAs) occupy an important place, both in natural biology and synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training datasets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs.
View Article and Find Full Text PDFCorrection of instrument temperature dependence of detector responsivity for measurements of comet 67P acquired by Rosetta's Visible and InfraRed Thermal Imaging Spectrometer, Mapping channel (VIRTIS-M).
Rev Sci Instrum
October 2024
Institute of Planetary Research, German Aerospace Center (DLR), Rutherfordstr. 2, 12489 Berlin, Germany.
Article Synopsis
- - The VIRTIS-M instrument on the Rosetta spacecraft collected hyperspectral images of comet 67P/Churyumov-Gerasimenko, with the VIS channel detector's responsivity influenced by its temperature (TVIS).
- - Analyzed data showed that a 1 K increase in TVIS resulted in varying percentage increases in measured spectral values, which affects the accuracy of the spectra recorded.
- - A new parameterization was developed to correct for this temperature-related bias, ensuring consistency in the spectral data across the mission and enhancing future analyses.
Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions.
Front Mol Biosci
April 2022
Department of Early Discovery Biochemistry, Genentech, South San Francisco, CA, United States.
Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta.
View Article and Find Full Text PDFStructure Based Prediction of Neoantigen Immunogenicity.
Front Immunol
October 2020
Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States.
The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity.
View Article and Find Full Text PDFHigh-quality protein backbone reconstruction from alpha carbons using Gaussian mixture models.
J Comput Chem
August 2013
Division of Molecular Biosciences, Imperial College, South Kensington Campus, London, United Kingdom.
Coarse-grained protein structure models offer increased efficiency in structural modeling, but these must be coupled with fast and accurate methods to revert to a full-atom structure. Here, we present a novel algorithm to reconstruct mainchain models from C traces. This has been parameterized by fitting Gaussian mixture models (GMMs) to short backbone fragments centered on idealized peptide bonds.
View Article and Find Full Text PDF