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Uma Mudunuri

Bio: Uma Mudunuri is an academic researcher from United States Department of the Army. The author has contributed to research in topics: Ligand (biochemistry). The author has an hindex of 1, co-authored 1 publications receiving 267 citations.

Papers
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TL;DR: A new web-server tool estimates Ki values from experimentally determined IC50 values for inhibitors of enzymes and of binding reactions between macromolecules and ligands to enable end users to help gauge the quality of the underlying assumptions used in these calculations.
Abstract: A new web-server tool estimates Ki values from experimentally determined IC50 values for inhibitors of enzymes and of binding reactions between macromolecules (e.g. proteins, polynucleic acids) and ligands. This converter was developed to enable end users to help gauge the quality of the underlying assumptions used in these calculations which depend on the type of mechanism of inhibitor action and the concentrations of the interacting molecular species. Additional calculations are performed for nonclassical, tightly bound inhibitors of enzyme-substrate or of macromolecule-ligand systems in which free, rather than total concentrations of the reacting species are required. Required userdefined input values include the total enzyme (or another target molecule) and substrate (or ligand) concentrations, the Km of the enzyme-substrate (or the Kd of the target-ligand) reaction, and the IC50 value. Assumptions and caveats for these calculations are discussed along with examples taken from the literature. The host database for this converter contains kinetic constants and other data for inhibitors of the proteolytic clostridial neurotoxins (http:// botdb.abcc.ncifcrf.gov/toxin/kiConverter.jsp).

321 citations


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TL;DR: A deep learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities is proposed, outperforming the KronRLS algorithm and SimBoost, a state‐of‐the‐art method for DT binding affinity prediction.
Abstract: Motivation The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). Results The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction. Availability and implementation https://github.com/hkmztrk/DeepDTA. Supplementary information Supplementary data are available at Bioinformatics online.

634 citations

Journal ArticleDOI
07 Apr 2016-Nature
TL;DR: This study provides a mechanistic explanation for the selective efficacy of lenalidomide in del(5q) MDS therapy and predicts that high-affinity protein–protein interactions induced by small molecules will provide opportunities for drug development, particularly for targeted protein degradation.
Abstract: Thalidomide and its derivatives, lenalidomide and pomalidomide, are immune modulatory drugs (IMiDs) used in the treatment of haematologic malignancies. IMiDs bind CRBN, the substrate receptor of the CUL4-RBX1-DDB1-CRBN (also known as CRL4(CRBN)) E3 ubiquitin ligase, and inhibit ubiquitination of endogenous CRL4(CRBN) substrates. Unexpectedly, IMiDs also repurpose the ligase to target new proteins for degradation. Lenalidomide induces degradation of the lymphoid transcription factors Ikaros and Aiolos (also known as IKZF1 and IKZF3), and casein kinase 1α (CK1α), which contributes to its clinical efficacy in the treatment of multiple myeloma and 5q-deletion associated myelodysplastic syndrome (del(5q) MDS), respectively. How lenalidomide alters the specificity of the ligase to degrade these proteins remains elusive. Here we present the 2.45 A crystal structure of DDB1-CRBN bound to lenalidomide and CK1α. CRBN and lenalidomide jointly provide the binding interface for a CK1α β-hairpin-loop located in the kinase N-lobe. We show that CK1α binding to CRL4(CRBN) is strictly dependent on the presence of an IMiD. Binding of IKZF1 to CRBN similarly requires the compound and both, IKZF1 and CK1α, use a related binding mode. Our study provides a mechanistic explanation for the selective efficacy of lenalidomide in del(5q) MDS therapy. We anticipate that high-affinity protein-protein interactions induced by small molecules will provide opportunities for drug development, particularly for targeted protein degradation.

359 citations

Journal ArticleDOI
TL;DR: SD-36 achieves complete and long-lasting tumor regression in multiple xenograft mouse models at well-tolerated dose schedules and is a promising cancer therapeutic strategy.

311 citations

Journal ArticleDOI
TL;DR: In addition to its continued utilization in high-throughput screening, FP has expanded into new disease and target areas and has been marked by increased use of labeled small molecule ligands for receptor-binding studies.
Abstract: Importance of the field: Fluorescence polarization (FP) is a homogeneous method that allows rapid and quantitative analysis of diverse molecular interactions and enzyme activities. This technique has been widely utilized in clinical and biomedical settings, including the diagnosis of certain diseases and monitoring therapeutic drug levels in body fluids. Recent developments in the field have been symbolized by the facile adoption of FP in high-throughput screening and small molecule drug discovery of an increasing range of target classes. Areas covered in this review: The article provides a brief overview of the theoretical foundation of FP, followed by updates on recent advancements in its application for various drug target classes, including GPCRs, enzymes and protein–protein interactions. The strengths and weaknesses of this method, practical considerations in assay design, novel applications and future directions are also discussed. What the reader will gain: The reader is informed of the most recent...

301 citations

Journal ArticleDOI
02 Nov 2018-Science
TL;DR: The human ZF “degrome” is defined in the context of thalidomide, lenalidomid, and pomalidomides to characterize the ZF-drug-CRBN interaction structurally and functionally and determine whether different thalidmide analogs degrade distinct ZFs.
Abstract: The small molecules thalidomide, lenalidomide, and pomalidomide induce the ubiquitination and proteasomal degradation of the transcription factors Ikaros (IKZF1) and Aiolos (IKZF3) by recruiting a Cys2-His2 (C2H2) zinc finger domain to Cereblon (CRBN), the substrate receptor of the CRL4CRBN E3 ubiquitin ligase. We screened the human C2H2 zinc finger proteome for degradation in the presence of thalidomide analogs, identifying 11 zinc finger degrons. Structural and functional characterization of the C2H2 zinc finger degrons demonstrates how diverse zinc finger domains bind the permissive drug-CRBN interface. Computational zinc finger docking and biochemical analysis predict that more than 150 zinc fingers bind the drug-CRBN complex in vitro, and we show that selective zinc finger degradation can be achieved through compound modifications. Our results provide a rationale for therapeutically targeting transcription factors that were previously considered undruggable.

278 citations