Drug development encompasses multiple processes,wherein protein subcellular localization is essential.It promotes target identification,treatment development,and the design of drug delivery systems.In this research,a deep learning framework called LocPro is presented for predicting protein subcellular localization.Specifically,LocPro is unique in(a)combining protein representations from the pre-trained large language model(LLM)ESM2 and the expert-driven tool PROFEAT,(b)implementing a hybrid deep neural network architecture that integrates convolutional neural network(CNN),fully connected(FC)layer,and bidirectional long short-term memory(BiLSTM)blocks,and(c)developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels.Additionally,a dataset was curated and divided using a homology-based strategy for training and validation.Compar-ative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction.The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization.All in all,LocPro serves as a valuable complement to existing protein localization prediction tools.The web server is freely accessible at https://idrblab.org/LocPro/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.

Phytochemical investigation of the aerial parts of Baeckea frutescens resulted in the isolation of three new mono- or sesquiterpene-based meroterpenoids, frutescones S-U (1-3), and one pair of new (±)-5,7-dihydroxy-8-isobutyryl-6-methyldihydroflavonol (4). Their structures and absolute configurations were established by HR-ESI-MS, 1D and 2D NMR, and quantum chemical ECD calculation. Compound 1 exhibited inhibitory effect on NO production in LPS-activated RAW 264.7 macrophages with an IC value being 0.81 μmol·L.

The present study was designed to investigate the chemical constituents of the roots of Cyathula officinalis. Compounds were isolated by silica gel, Sephadex LH-20, ODS column chromatography, and preparative HPLC. Their structures were determined on the basis of 1D and 2D NMR techniques, mass spectrometry, and chemical methods. One new oleanane-type triterpenoid saponin, 28-O-[α-L-rhamnopyranosyl-(1→3)-β-D-glucuronopyranosyl-(1→3)-β-D-glucopyranosyl] hederagenin (1), was isolated from the roots of Cyathula officinalis. The anti-inflammatory activities of the isolates were evaluated for their inhibitory effects against LPS-induced nitric oxide (NO) production in RAW 264.7 macrophages cells. Compounds 2, 4, and 6 exhibited moderate anti-inflammatory activities.
Amaranthaceae ; chemistry ; Animals ; Anti-Inflammatory Agents ; isolation & purification ; Cells, Cultured ; Magnetic Resonance Spectroscopy ; Mice ; Nitric Oxide ; antagonists & inhibitors ; biosynthesis ; Plant Roots ; chemistry ; Saponins ; chemistry ; isolation & purification ; pharmacology ; Triterpenes ; chemistry ; isolation & purification ; pharmacology