A5009862187- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Genetics, Bioinformatics, and Biomedical Research
- Mass Spectrometry Techniques and Applications
- Protein Structure and Dynamics
- Advanced Proteomics Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Ion-surface interactions and analysis
- Enzyme Structure and Function
- Bioinformatics and Genomic Networks
- Analytical Chemistry and Chromatography
- Machine Learning in Bioinformatics
The Ohio State University
2021-2023
Abstract Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCS IM ). While data have been used with various computational methods, they not yet utilized to predict monomeric structure from sequence. Here, we show that can significantly improve determination using modelling suite Rosetta. We develop Rosetta Projection Approximation Rough Circular Shapes (PARCS) algorithm allows...
The combination of deep learning and sequence data has transformed protein structure prediction modeling, evidenced in the success AlphaFold (AF). For this reason, many methods have been developed to take advantage areas where inaccurate structural modeling may limit computational predictiveness. example, predict intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions proteins are parts that do not form ordered, folded...
Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems optimizing use of sparse structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety methods collect proteins. One example surface-induced dissociation (SID), which break apart complexes (via surface collision) into intact subcomplexes performed at multiple laboratory frame SID collision energies. These energy-resolved...
Ion mobility (IM) coupled to mass spectrometry informs on the shape and size of protein structures in form a collision cross section (CCSIM). While there are several computational methods for predicting CCSIM based structures, including our previously developed PARCS, process usually requires prior experience with command-line interface (CLI). To overcome this challenge, here we present web application ROSIE webserver predict from structure using projection approximation PARCS. In interface,...
Abstract Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations proteins, ion mobility (IM) provides information about protein shape and size in the form an orientationally averaged collision cross-section (CCS). While IM data have been predominantly employed assessment complexes, CCS from experiments not yet used to predict tertiary structure sequence. Here, we are showing that can significantly improve determination using modeling suite...
Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems optimizing use of sparse structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety methods collect proteins. One example surface-induced dissociation (SID), which break apart complexes (via surface collision) into intact subcomplexes performed at multiple laboratory frame SID collision energies. These energy-resolved...