A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles.
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ABSTRACT:
SUBMITTER: Kurzawa N
PROVIDER: S-EPMC7666118 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein-ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.
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Project description:The study of low-abundance proteins is a challenge to discovery-based proteomics. Mass spectrometry (MS) applications, such as thermal proteome profiling (TPP), face specific challenges in the detection of the whole proteome as a consequence of the use of nondenaturing extraction buffers. TPP is a powerful method for the study of protein thermal stability, but quantitative accuracy is highly dependent on consistent detection. Therefore, TPP can be limited in its amenability to study low-abundance proteins that tend to have stochastic or poor detection by MS. To address this challenge, we incorporated an affinity-purified protein complex sample at submolar concentrations as an isobaric trigger channel into a mutant TPP (mTPP) workflow to provide reproducible detection and quantitation of the low-abundance subunits of the cleavage and polyadenylation factor (CPF) complex. The inclusion of an isobaric protein complex trigger channel increased detection an average of 40× for previously detected subunits and facilitated detection of CPF subunits that were previously below the limit of detection. Importantly, these gains in CPF detection did not cause large changes in melt temperature (Tm) calculations for other unrelated proteins in the samples, with a high positive correlation between Tm estimates in samples with and without isobaric trigger channel addition. Overall, the incorporation of an affinity-purified protein complex as an isobaric trigger channel within a tandem mass tag (TMT) multiplex for mTPP experiments is an effective and reproducible way to gather thermal profiling data on proteins that are not readily detected using the original TPP or mTPP protocols.
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Project description:Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.
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