Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries

Table_1_Histone Deacetylase HDA9 With ABI4 Contributes to Abscisic Acid Homeostasis in Drought Stress Response.docx

Drought stress, a major environmental factor, significantly affects plant growth and reproduction. Plants have evolved complex molecular mechanisms to tolerate drought stress. In this study, we investigated the function of the Arabidopsis thaliana RPD3-type HISTONE DEACETYLASE 9 (HDA9) in response to drought stress. The loss-of-function mutants hda9-1 and hda9-2 were insensitive to abscisic acid (ABA) and sensitive to drought stress. The ABA content in the hda9-1 mutant was reduced in wild type (WT) plant. Most histone deacetylases in animals and plants form complexes with other chromatin-remodeling components, such as transcription factors. In this study, we found that HDA9 interacts with the ABA INSENSITIVE 4 (ABI4) transcription factor using a yeast two-hybrid assay and coimmunoprecipitation. The expression of CYP707A1 and CYP707A2, which encode (+)-ABA 8′-hydroxylases, key enzymes in ABA catabolic pathways, was highly induced in hda9-1, hda9-2, abi4, and hda9-1 abi4 mutants upon drought stress. Chromatin immunoprecipitation and quantitative PCR showed that the HDA9 and ABI4 complex repressed the expression of CYP707A1 and CYP707A2 by directly binding to their promoters in response to drought stress. Taken together, these data suggest that HDA9 and ABI4 form a repressive complex to regulate the expression of CYP707A1 and CYP707A2 in response to drought stress in Arabidopsis.</p

DataSheet_1_Negative regulation of floral transition in Arabidopsis by HOS15-PWR-HDA9 complex.docx

Arabidopsis HOS15/PWR/HDA9 repressor complex, which is similar to the TBL1/NcoR1/HDAC complex in animals, plays a well-known role in epigenetic regulation. PWR and HDA9 have been reported to interact with each other and modulate the flowering time by repressing AGL19 expression, whereas HOS15 and HDA9, together with the photoperiodic evening complex, regulate flowering time through repression of GI transcription. However, the role of the HOS15/PWR/HDA9 core repressor complex as a functional unit in the regulation of flowering time is yet to be explored. In this study, we reported that the loss-of-function hos15-2/pwr/hda9 triple mutant accumulates higher transcript levels of AGL19 and exhibits an early flowering phenotype similar to those of hos15, pwr, and hda9 single mutants. Interestingly, the accumulation of HOS15 in the nucleus was drastically reduced in pwr and hda9 mutants. As a result, HOS15 could not perform its role in histone deacetylation or interaction with H3 in the nucleus. Furthermore, HOS15 is also associated with the same region of the AGL19 promoter known for PWR-HDA9 binding. The acetylation level of the AGL19 promoter was increased in the hos15-2 mutant, similar to the pwr and hda9 mutants. Therefore, our findings reveal that the HOS15/PWR/HDA9 repressor complex deacetylates the promoter region of AGL19, thereby negatively regulating AGL19 transcription, which leads to early flowering in Arabidopsis.</p