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Abstract Motivation Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks. Availability and implementation CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). The assigned spectra involved in this study and tutorial videos using this dataset are available at https://sites.google.com/view/chescachespa-sparky. Supplementary information Supplementary data are available at Bioinformatics Online. 

Protein and peptide prenylation is an essential biological process involved in many signal transduction pathways. Hence, it plays a critical role in establishing many major human ailments, including Alzheimer's disease, amyotrophic lateral sclerosis (ALS), malaria, and Ras-related cancers. Yeast mating pheromone a-factor is a small dodecameric peptide that undergoes prenylation and subsequent processing in a manner identical to larger proteins. Due to its small size in addition to its well-characterized behavior in yeast, a-factor is an attractive model system to study the prenylation pathway. Traditionally, chemical synthesis and characterization of a-factor have been challenging, which has limited its use in prenylation studies. In this chapter, a robust method for the synthesis of a-factor is presented along with a description of the characterization of the peptide using MALDI and NMR. Finally, complete assignments of resonances from the isoprenoid moiety and a-factor from COSY, TOCSY, HSQC, and long-range HMBC NMR spectra are presented. This methodology should be useful for the synthesis and characterization of other mature prenylated peptides and proteins. 

Abstract Motivation Two-dimensional [15N-1H] separated local field solid-state nuclear magnetic resonance (NMR) experiments of membrane proteins aligned in lipid bilayers provide tilt and rotation angles for α-helical segments using Polar Index Slant Angle (PISA)-wheel models. No integrated software has been made available for data analysis and visualization. Results We have developed the PISA-SPARKY plugin to seamlessly integrate PISA-wheel modeling into the NMRFAM-SPARKY platform. The plugin performs basic simulations, exhaustive fitting against experimental spectra, error analysis and dipolar and chemical shift wave plotting. The plugin also supports PyMOL integration and handling of parameters that describe variable alignment and dynamic scaling encountered with magnetically aligned media, ensuring optimal fitting and generation of restraints for structure calculation. Availability and implementation PISA-SPARKY is freely available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers of the SBGrid (https://sbgrid.org). The pisa.py script is available and documented on GitHub (https://github.com/weberdak/pisa.py) along with a tutorial video and sample data. Supplementary information Supplementary data are available at Bioinformatics online.