Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins

Although
thermal proteome profiling (TPP) acts as a popular modification-free
approach for drug target deconvolution, some key problems are still
limiting screening sensitivity. In the prevailing TPP workflow, only
the soluble fractions are analyzed after thermal treatment, while
the precipitate fractions that also contain abundant information of
drug-induced stability shifts are discarded; the sigmoid melting curve
fitting strategy used for data processing suffers from discriminations
for a part of human proteome with multiple transitions. In this study,
a precipitate-supported TPP (PSTPP) assay was presented for unbiased
and comprehensive analysis of protein–drug interactions at
the proteome level. In PSTPP, only these temperatures where significant
precipitation is observed were applied to induce protein denaturation
and the complementary information contained in both supernatant fractions
and precipitate fractions was used to improve the screening specificity
and sensitivity. In addition, a novel image recognition algorithm
based on deep learning was developed to recognize the target proteins,
which circumvented the problems that exist in the sigmoid curve fitting
strategy. PSTPP assay was validated by identifying the known targets
of methotrexate, raltitrexed, and SNS-032 with good performance. Using
a promiscuous kinase inhibitor, staurosporine, we delineated 99 kinase
targets with a specificity up to 83% in K562 cell lysates, which represented
a significant improvement over the existing thermal shift methods.
Furthermore, the PSTPP strategy was successfully applied to analyze
the binding targets of rapamycin, identifying the well-known targets,
FKBP1A, as well as revealing a few other potential targets.