Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

Alpha transmembrane proteins (αTMPs) profoundly affect many critical biological processes and are major drug targets due to their pivotal protein functions. At present, even though the non-transmembrane secondary structures are highly relevant to the biological functions of αTMPs along with their transmembrane structures, they have not been unified to be studied yet. In this study, we present a novel computational method, TMPSS, to predict the secondary structures in non-transmembrane parts and the topology structures in transmembrane parts of αTMPs. TMPSS applied a Convolutional Neural Network (CNN), combined with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network, to extract the local contexts and long-distance interdependencies from primary sequences. In addition, a multi-task learning strategy was used to predict the secondary structures and the transmembrane helixes. TMPSS was thoroughly trained and tested against a non-redundant independent dataset, where the Q3 secondary structure prediction accuracy achieved 78% in the non-transmembrane region, and the accuracy of the transmembrane region prediction achieved 90%. In sum, our method showcased a unified model for predicting the secondary structure and topology structure of αTMPs by only utilizing features generated from primary sequences and provided a steady and fast prediction, which promisingly improves the structural studies on αTMPs.

Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target MPs. Therefore, accurately identifying the membrane protein-ligand binding sites (MPLs) will significantly improve drug discovery. In this paper, we propose a sequence-based MPLs predictor called MPLs-Pred, where evolutionary profiles, topology structure, physicochemical properties, and primary sequence segment descriptors are combined as features applied to a random forest classifier, and an under-sampling scheme is used to enhance the classification capability with imbalanced samples. Additional ligand-specific models were taken into consideration in refining the prediction. The corresponding experimental results based on our method achieved an appreciable performance, with 0.63 MCC (Matthews correlation coefficient) as the overall prediction precision, and those values were 0.604, 0.7, and 0.692, respectively, for the three main types of ligands: drugs, metal ions, and biomacromolecules. MPLs-Pred is freely accessible at http://icdtools.nenu.edu.cn/.

Motivation: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug–target binding during drug development. However, the inherent diversity among molecular properties, coupled with limited training data availability, poses challenges to the accuracy and generalizability of these methods beyond their training domain. Results: In this work, we proposed a neural networks construction for high accurate and generalizable drug–target binding prediction, named Pre-trained Multi-view Molecular Representations (PMMR). The method uses pre-trained models to transfer representations of target proteins and drugs to the domain of drug–target binding prediction, mitigating the issue of poor generalizability stemming from limited data. Then, two typical representations of drug molecules, Graphs and SMILES strings, are learned respectively by a Graph Neural Network and a Transformer to achieve complementarity between local and global features. PMMR was evaluated on drug–target affinity and interaction benchmark datasets, and it derived preponderant performance contrast to peer methods, especially generalizability in cold-start scenarios. Furthermore, our state-of-the-art method was indicated to have the potential for drug discovery by a case study of cyclin-dependent kinase 2. Availability and implementation: https://github.com/NENUBioCompute/PMMR.

Neuropathic pain (NP) is a common debilitating chronic pain condition with limited effective therapeutics. Further investigating mechanisms underlying NP is therefore of great importance for discovering more promising therapeutic targets. In the current study, we employed high-throughput RNA sequencing to explore transcriptome profiles of mRNAs and microRNAs in the dorsal root ganglia (DRG) following chronic constriction injury (CCI) and also integrated published datasets for comprehensive analysis. First, we established CCI rat model confirmed by behavioral testings, and excavated 467 differentially expressed mRNAs (DEGs) and 16 differentially expressed microRNAs (DEmiRNAs) in the ipsilateral lumbar 4–6 DRG of CCI rats 11 days after surgery. Functional enrichment analysis of 337 upregulated DEGs showed that most of the DEGs were enriched in inflammation- and immune-associated biological processes and signaling pathways. The protein-protein interaction networks were constructed and hub DEGs were screened. Besides hub DEGs, we also identified 113 overlapped DEGs by intersecting our dataset with dataset GSE100122. Subsequently, we predicted potential miRNA-mRNA regulatory pairs using DEmiRNAs and a given set of key DEGs (including hub and overlapped DEGs). By integrative analysis, we found commonly differentially expressed mRNAs and miRNAs following CCI of different time points and different nerve injury types. Highlighted mRNAs include Atf3, Vip, Gal, Npy, Adcyap1, Reg3b, Jun, Cd74, Gadd45a, Tgm1, Csrp3, Sprr1a, Serpina3n, Gap43, Serpinb2 and Vtcn1, while miRNAs include miR-21-5p, miR-34a-5p, miR-200a-3p, miR-130a-5p, miR-216b-5p, miR-217-5p, and miR-541-5p. Additionally, 15 DEGs, including macrophages-specific (Cx3cr1, Arg1, Cd68, Csf1r) and the ones related to macrophages’ involvement in NP (Ccl2, Fcgr3a, Bdnf, Ctss, Tyrobp) were verified by qRT-PCR. By functional experiments in future studies, promising therapeutic targets for NP treatment may be identified among these mRNAs and miRNAs.

In recent years, the rapid development of high-throughput technology has led to huge amounts of protein-protein interaction (PPI) data and unannotated protein sequences. Many approaches for protein function prediction have been developed which use PPI networks information. Traditional methods usually use the dependencies among interacting proteins for each same function only. However, the functions which are barely linked with the same function are more difficult to predict. In multi-label settings, the dependencies among related instances with multiple labels are more complex; rationally using these associations can make up for the shortcomings of traditional methods. In this paper, an iterative algorithm is applied to predict protein function based on the new network. The proposed method is able to capture the dependencies among functions based on proteins and interactions for protein function prediction. The test results show that the algorithm performs better than most of existing network based PPI algorithms; adding sequence similarity edges and spread function information can really improve the prediction performance. In addition, the dependencies among functions based on proteins and interactions can be effectively applied to the prediction of protein function.