A Conserved Local Structural Motif Controls the Kinetics of PTP1B Catalysis.
Journal of chemical information and modeling
Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin signaling pathways, making it a highly attractive target for the treatment of type II diabetes. For PTP1B to perform its enzymatic function, a loop referred to as the "WPD loop" must transition between open (catalytically incompetent) and closed (catalytically competent) conformations, which have both been resolved by X-ray crystallography. Although prior studies have established this transition as the rate-limiting step for catalysis, the transition mechanism for PTP1B and other PTPs has been unclear. Here we present an atomically detailed model of WPD loop transitions in PTP1B based on unbiased, long-timescale molecular dynamics simulations and weighted ensemble simulations. We found that a specific WPD loop region─the PDFG motif─acted as the key conformational switch, with structural changes to the motif being necessary and sufficient for transitions between long-lived open and closed states of the loop. Simulations starting from the closed state repeatedly visited open states of the loop that quickly closed again unless the infrequent conformational switching of the motif stabilized the open state. The functional importance of the PDFG motif is supported by the fact that it is well conserved across PTPs. Bioinformatic analysis shows that the PDFG motif is also conserved, and adopts two distinct conformations, in deiminases, and the related DFG motif is known to function as a conformational switch in many kinases, suggesting that PDFG-like motifs may control transitions between structurally distinct, long-lived conformational states in multiple protein families.
View details for DOI 10.1021/acs.jcim.3c00286
View details for PubMedID 37378552
Discovery and Validation of the Binding Poses of Allosteric Fragment Hits to Protein Tyrosine Phosphatase 1b: From Molecular Dynamics Simulations to X-ray Crystallography.
Journal of chemical information and modeling
Fragment-based drug discovery has led to six approved drugs, but the small sizes of the chemical fragments used in such methods typically result in only weak interactions between the fragment and its target molecule, which makes it challenging to experimentally determine the three-dimensional poses fragments assume in the bound state. One computational approach that could help address this difficulty is long-timescale molecular dynamics (MD) simulations, which have been used in retrospective studies to recover experimentally known binding poses of fragments. Here, we present the results of long-timescale MD simulations that we used to prospectively discover binding poses for two series of fragments in allosteric pockets on a difficult and important pharmaceutical target, protein tyrosine phosphatase 1b (PTP1b). Our simulations reversibly sampled the fragment association and dissociation process. One of the binding pockets found in the simulations has not to our knowledge been previously observed with a bound fragment, and the other pocket adopted a very rare conformation. We subsequently obtained high-resolution crystal structures of members of each fragment series bound to PTP1b, and the experimentally observed poses confirmed the simulation results. To the best of our knowledge, our findings provide the first demonstration that MD simulations can be used prospectively to determine fragment binding poses to previously unidentified pockets.
View details for DOI 10.1021/acs.jcim.3c00236
View details for PubMedID 37086179
Lineage tracing of CAR T cells in patients with B cell malignancies
American Association for Cancer Research
View details for DOI 10.1158/1538-7445.AM2023-1128
Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI.
2022; 8 (11): eabi4757
Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset, then leverage single-cell transcriptomics data to increase the cell type resolution and identify LR prognostic signatures among tumor-stroma-immune subpopulations. We experimentally confirmed colocalization of CTGF:LRP6 among malignant cell subtypes as an interaction predicted to be associated with LUAD progression. Our work presents a computational approach to reconstruct interactomes and identify clinically relevant CCIs.
View details for DOI 10.1126/sciadv.abi4757
View details for PubMedID 35302849
A Conserved Local Structural Motif Controls the Kinetics of PTP1b Catalysis
CELL PRESS. 2020: 518A
View details for Web of Science ID 000513023203337
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss.
Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.
View details for PubMedID 29361466
Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer
2017; 5: 30
Quantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike 'omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms.We obtained ten blood plasma "case" samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes.Although we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly (p < 1E-01) altered expression levels between cases and controls, with no proteins retaining statistical significance after false discovery correction.Technical, practical, and study design considerations are essential to maximize information obtained in limited pre-diagnostic samples of cancer patients. This study provides a framework that estimates biological effect sizes critical for consideration in designing studies for pre-diagnostic blood-based biomarker detection.
View details for DOI 10.1186/s40364-017-0110-y
View details for PubMedCentralID PMC5645980
Vitamin D supplementation decreases serum 27-hydroxycholesterol in a pilot breast cancer trial.
Breast cancer research and treatment
27-hydroxycholesterol (27HC), an endogenous selective estrogen receptor modulator (SERM), drives the growth of estrogen receptor-positive (ER+) breast cancer. 1,25-dihydroxyvitamin D (1,25(OH)2D), the active metabolite of vitamin D, is known to inhibit expression of CYP27B1, which is very similar in structure and function to CYP27A1, the synthesizing enzyme of 27HC. Therefore, we hypothesized that 1,25(OH)2D may also inhibit expression of CYP27A1, thereby reducing 27HC concentrations in the blood and tissues that express CYP27A1, including breast cancer tissue.27HC, 25-hydroxyvitamin D (25OHD), and 1,25(OH)2D were measured in sera from 29 breast cancer patients before and after supplementation with low-dose (400 IU/day) or high-dose (10,000 IU/day) vitamin D in the interval between biopsy and surgery.A significant increase (p = 4.3E-5) in 25OHD and a decrease (p = 1.7E-1) in 27HC was observed in high-dose versus low-dose vitamin D subjects. Excluding two statistical outliers, 25OHD and 27HC levels were inversely correlated (p = 7.0E-3).Vitamin D supplementation can decrease circulating 27HC of breast cancer patients, likely by CYP27A1 inhibition. This suggests a new and additional modality by which vitamin D can inhibit ER+ breast cancer growth, though a larger study is needed for verification.
View details for PubMedID 29116467