74 research outputs found
Development And Application Of Chemical Tools For The Study Of S-Adenosyl-L-Methionine-Dependent Methyltransferases
No full textMethyltransferases represent a class of enzyme responsible for the modification of biomolecules through the transfer of individual methyl units. The cofactor, S-adenosyl-L-methionine (SAM), serves as the methyl source for the vast majority of these enzyme-catalyzed reactions. These transformations have broad implications for many biological processes, ranging from the biosynthesis of essential cellular metabolites and pharmaceutically relevant natural products to the regulation of gene expression and protein function through the modification of nucleic acids and polypeptides. In addition, the malfunction of methyltransferase activity has been strongly implicated in a number of disease states including developmental disorders and carcinogenesis. As such, there has been significant effort in recent years to better understand these enzymes, their substrates, and the biological effects associated with their activity. Despite increased interest, the study of these processes has proven difficult using traditional biochemical or genetic techniques. In light of this, the research described herein has been aimed at the development of novel chemical tools and approaches for the study of these enzymes, with an emphasis on protein methyltransferases (PMTs). This research can be broadly categorized into two main focuses: (i) the implementation of Bioorthogonal Profiling of Protein Methylation (BPPM), in which substrates of specific PMTs are determined through the use of engineered enzymes, SAM analogues and bioorthogonal chemistry; and (ii) the development of a selenium-based SAM analogues, one of which has shown broad compatibility toward a wide variety of wild-type enzymes including: protein, nucleic acid and small-molecule methyltransferases. With these tools in hand, novel substrates for the G9a and GLP1 protein lysine methyltransferases have been identified, and a versatile selenium-based SAM mimic has demonstrated potential as a useful tool for the enzymatic functionalization of proteins and small molecules
Leveraging BPPM Technology to Investigate Noncanonical Histone Targets of Protein Methyltransferases DOT1L and NSD2
No full textThere are ~70 protein methyltransferases (PMTs) encoded in the human genome, but over 16,000 characterized methylation events occurring on histone and nonhistone substrates. This suggests that each PMT has both a canonical function and noncanonical activities contributing to its biological role(s). Since interrogating protein methylation poses challenges in the forms of redundancy, transiency, and specificity, these noncanonical activities are still largely undiscovered. Our lab has developed a technique called Bioorthogonal Profiling of Protein Methylation (BPPM) that has lifted the curtain on interrogating PMT activities. Applying this technique to two PMTs, DOT1L and NSD2, we have uncovered a novel histone target for each enzyme. For DOT1L, BPPM has revealed the substrate H4K5. Further interrogation of this activity has shown that this activity is specific to MLLr leukemia, where it is read by the transcriptional coactivator SPIN1. Dependent on DOT1L activity, SPIN1 binds and induces expression of key MLLr genes, shown by CUT&TAG and RNAseq. Furthermore, SPIN1 shRNA knockdown causes growth impairment in MLLr cells. This indicates DOT1L-catalyzed noncanonical H4K5 methylation contributes to MLLr cell proliferation through SPIN1. In another study, we were interested to interrogate NSD2’s role in the DNA damage response after a recent paper had debunked its proposed activity of H4K20me2 to recruit 53BP1. Hypothesizing that another histone mark may be involved, we used BPPM to discover NSD2-mediated methylation of H3K18. While we could confirm loss of NSD2 impairs DNA repair by I-SceI-based assays and quantification of 53BP1 foci, we could not measure the role of H3K18me1 likely due to PMT redundancy. Therefore, further investigation is necessary to study the role of H3K18 methylation in the recruitment of 53BP1
Abstract 1394: Bioorthogonal Profiling of Protein Methylation (BPPM) identified MCM5 as a new substrate for SETD8 in DNA replication
No full textAbstract
SETD8 is the only member of the SET domain containing methyltransferase family, which catalyzes mono-methylation of K20 on histone H4 (H4K20me1). Lysine residues of non-histone proteins such as p53 and proliferating cell nuclear antigen (PCNA) are also monomethylated, while lysine residues in Numb were found to be dimethylated. As a consequence, SETD8 methyltransferase activity is implicated in several fundamental cellular processes such as transcriptional regulation and heterochromatin formation as well as processes that ensures genomic stability including DNA replication and the DNA damage response. Although it has been suggested that SETD8 is involved in DNA replication as a positive regulator of origin licensing through H4K20 methyation and by supporting Okazaki fragment processing through PCNA methylation, to date, there is no evidence whether other key protein in the replication fork is directly modified by SETD8. To address this question, we used Bioorthogonal Profiling of Protein Methylation (BPPM) with engineered enzyme and synthetic SAM analogues to profile new substrates for SETD8. We genetically engineered SETD8 and identified mutants amenable to accommodate non-native SAM analogues containing a terminal alkyne moiety for click chemistry. The engineered SETD8 can transfer this distinct chemical moiety into target proteins for subsequent pulldown and identification of the modified substrates. Among the new substrates discovered, we identified MCM5, a subunit of the hexameric minichromosome maintenance (MCM) DNA helicase complex. MCM5 directly interacts with MCM2 and 3 to form the MCM2-7 hexamer which associates with the origins of DNA replication to form part of the pre-replicative complex (preRC), playing a key role during replication initiation and elongation. We found that SETD8 can mono-methylate MCM5 directly affecting its binding affinity to MCM2 and 3. In addition, MCM5 mutations at the methylated lysine further evidenced a stronger binding to its interacting partners contributing to the formation of the MCM hexamer. Taken together our results indicate that MCM5 methylation contributes to the assembly of the MCM complex suggesting an important role for SETD8 in replication initiation. Our findings may bring new perspectives on the biological importance of SETD8 during DNA replication.
Citation Format: Fabio Pittella Silva, Gil Blum, Chamara Senevirathne, Luo Minkui. Bioorthogonal Profiling of Protein Methylation (BPPM) identified MCM5 as a new substrate for SETD8 in DNA replication [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1394. doi:10.1158/1538-7445.AM2017-1394</jats:p
The Development And Application Of Small Molecule Probes For The Identification And Characterization Of Receptor Targets And The Total Synthesis Of The Caeliferins, Elicitors Of Plant Immune Response
No full textAdvancing Small Molecule Physicochemical Property Predictions For Computational Drug Discovery
No full textComputer-aided drug design approaches aim to guide the discovery of new chemical entities with optimal pharmaceutical and physicochemical properties. With computational predictions, vast libraries of virtual molecules can be evaluated based on their predicted values to aid prioritization of which new compounds to synthesize and test. Among others, protein target binding affinity, lipophilicity, membrane permeability, and solubility are desirable properties to predict. Modeling of these properties of drug candidates with physical modeling methodologies requires being able to accurately predict small-molecule protonation states and solvation. Therefore, evaluating the prediction accuracy of physicochemical properties such as acid dissociation constants and partition coefficients helps us learn how reliable these models are and anticipate how their inaccuracy can impact other physical models such as binding affinity predictions. Here, we present the results of two community-wide blind challenges in which we benchmark the accuracy of protonation state and lipophilicity predictions. We constructed new experimental datasets of experimental acid dissociation constants (pKa) and octanol-water partition coefficients (log P) of drug-like molecules to serve as reference benchmark sets to prospectively and fairly evaluate computational methods. We organized challenges to evaluate the accuracy of pKa and log P predictions to isolate prediction errors. Wide participation from the computational chemistry community allowed the assessment of a large variety of physical and empirical prediction methods. Small molecule drugs frequently possess multiple titratable and tautomerizable moieties that complicate pKa predictions. We designed a blind challenge to evaluate macroscopic and microscopic pKa predictions for drug-like molecules, based on full understanding of the experimental data and determined assessment strategies taking microspeciation in consideration. The detailed evaluation strategy that we demonstrated can guide future improvement of pKa prediction methods. We have determined that inaccuracies observed in current state of the art pKa predictions can cause significant errors in protein-ligand binding affinity predictions, both in terms of predicted protonation states and their relative solution energies. We have also determined that a number of chemical moieties are associated with higher pKa prediction errors. The complementary exercise of evaluating partition coefficient prediction methods allowed us the assess how accurately physical methods can capture the solvation of small organic molecules which can contribute to errors in protein-ligand binding affinity predictions. log P describes the solvation propensity of the neutral state of a small molecule between two phases. Among the many lessons learned from the log P blind challenge, we found that molecular mechanics-based methods were were not as accurate as quantum-mechanics-based or empirical prediction methods, and modest protocol variations caused large performance differences. We identified tautomer selection and charging protocols as areas that require more attention for improving log P predictions. We have also identified challenging sets of molecules for each method category. Lessons learned from these blind challenges on how to critically evaluate these physicochemical properties, the performance of current prediction methodologies, and their strengths and weaknesses will aid the community in improving these models. The road to more reliable computational methods for structure-based drug design passes through better modeling of small molecules, not just the protein target. Improving protonation, tautomerization, and solvation models are promising directions to achieve more accurate binding affinity predictions
Org Lett
No full textA strategy for introducing structural diversity into polyketides by exploiting the promiscuity of an in-line methyltransferase domain in a multidomain polyketide synthase is reported. In vitro investigations using the highly-reducing fungal polyketide synthase CazF revealed that its methyltransferase domain accepts the nonnatural cofactor propargylic Se-adenosyl-l-methionine and can transfer the propargyl moiety onto its growing polyketide chain. This propargylated polyketide product can then be further chain-extended and cyclized to form propargyl-\u3b1 pyrone or be processed fully into the alkyne-containing 4'-propargyl-chaetoviridin A.R01 GM096056/GM/NIGMS NIH HHSUnited States/DP1 GM106413/GM/NIGMS NIH HHSUnited States/DP2 OD007335/OD/NIH HHSUnited States/T32 GM008496/GM/NIGMS NIH HHSUnited States/S10 RR025631/RR/NCRR NIH HHSUnited States/S10RR025631/RR/NCRR NIH HHSUnited States/1DP2OD007335/OD/NIH HHSUnited States/1DP1GM106413/DP/NCCDPHP CDC HHSUnited States/1R01GM096056/GM/NIGMS NIH HHSUnited States
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