Biology subjects

Wan, F.

Publications and source records attributed to Wan, F..

29 records · Page 2Linked to original sources

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

CAMP: a Convolutional Attention-based Neural Network for Multifaceted Peptide-protein Interaction Prediction

Peptide-protein interactions (PepPIs) are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. To facilitate the peptide drug discovery process, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Although several deep-learning-based frameworks have been proposed to predict compound-protein interactions or protein-protein interactions, few of them are particularly designed to specifically predict peptide-protein interactions. In this paper, We present a sequence-based Convolutional Attention-based neural network for Multifaceted prediction of Peptide-protein interactions, called CAMP, including predicting binary peptide-protein interactions and corresponding binding residues in the peptides. We also construct a benchmark dataset containing high-quality peptide-protein interaction pairs with the corresponding peptide binding residues for model training and evaluation. CAMP incorporates convolution neural network architectures and attention mechanism to fully exploit informative sequence-based features, including secondary structures, physicochemical properties, intrinsic disorder features and position-specific scoring matrix of the protein. Systematical evaluation of our benchmark dataset demonstrates that CAMP outperforms the state-of-the-art baseline methods on binary peptide-protein interaction prediction. In addition, CAMP can successfully identify the binding residues involved non-covalent interactions for peptides. These results indicate that CAMP can serve as a useful tool in peptide-protein interaction prediction and peptide binding site identification, which can thus greatly facilitate the peptide drug discovery process. The source code of CAMP can be found in https://github.com/twopin/CAMP.

bioinformatics↗

bioRxiv2020

Decreased miR-24-3p potentiates DNA damage responses and increases susceptibility to COPD

Activation of the DNA damage response (DDR) due to chronic exposure to cigarette smoke (CS) is implicated in the pathogenesis of Chronic Obstructive Pulmonary Disease (COPD). However, not all smokers develop COPD and the pathologic consequences of CS exposure are heterogenous. Cellular mechanisms that regulate the DDR and contribute to disease progression in susceptible individuals are poorly understood. Because microRNAs are well known regulators of the DDR, we evaluated microRNA expression arrays performed on lung samples from 172 subjects with and without COPD. We identified miR-24-3p as the microRNA best correlated with radiographic emphysema ({rho}=-0.353, P=1.3e-04) and validated this finding in multiple cohorts. In a CS-exposure mouse model, miR-24-3p inhibition increased emphysema severity. In human airway epithelial cells, miR-24-3p suppressed apoptosis through the BH3-only protein BIM and suppressed homology-directed DNA repair and the DNA repair protein BRCA1. Finally, we found BIM and BRCA1 were increased in COPD lung tissue and inversely correlated with miR-24-3p expression. We concluded that decreased miR-24-3p expression increases COPD susceptibility and potentiates the DDR through BIM and BRCA1.

cell biology↗

bioRxiv2020

A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

systems biology↗

bioRxiv2020

Giant African snail genomes provide insights into molluscan whole-genome duplication and aquatic-terrestrial transition

Whole-genome duplication (WGD) has been observed across a wide variety of eukaryotic groups, contributing to evolutionary diversity and environmental adaptability. Mollusks are the second largest group of animals, and are among the organisms that have successfully adapted to the nonmarine realm through aquatic-terrestrial (A-T) transition, and no comprehensive research on WGD has been reported in this group. To explore WGD and the A-T transition in Mollusca, we assembled a chromosome-level reference genome for the giant African snail Achatina immaculata, a global invasive species, and compared the genomes of two giant African snails (A. immaculata and Achatina fulica) to the other available mollusk genomes. The chromosome-level macrosynteny, colinearity blocks, Ks peak and Hox gene clusters collectively suggested the occurrence of a WGD event shared by A. immaculata and A. fulica. The estimated timing of this WGD event ([~]70 MYA) was close to the speciation age of the Sigmurethra-Orthurethra (within Stylommatophora) lineage and the Cretaceous-Tertiary (K-T) mass extinction, indicating that the WGD reported herein may have been a common event shared by all Sigmurethra-Orthurethra species and could have conferred ecological adaptability and genomic plasticity allowing the survival of the K-T extinction. Based on macrosynteny, we deduced an ancestral karyotype containing 8 conserved clusters for the Gastropoda-Bivalvia lineage. To reveal the mechanism of WGD in shaping adaptability to terrestrial ecosystems, we investigated gene families related to the respiration, aestivation and immune defense of giant African snails. Several mucus-related gene families expanded early in the Stylommatophora lineage, functioning in water retention, immune defense and wound healing. The hemocyanins, PCK and FBP families were doubled and retained after WGD, enhancing the capacity for gas exchange and glucose homeostasis in aestivation. After the WGD, zinc metalloproteinase genes were highly tandemly duplicated to protect tissue against ROS damage. This evidence collectively suggests that although the WGD may not have been the direct driver of the A-T transition, it provided an important legacy for the terrestrial adaptation of the giant African snail.

genomics↗

bioRxiv2020

sgRNA level determines CRISPRi knockdown efficiency in K562 cells

To determine how nuclease deactivated Cas9 (dCas9) or sgRNA expression level affects the knockdown efficiency of CRISPRi, K562 cell clones expressing KRAB-dCas9 protein either with the inducible Tet-on system or with the constitutive SFFV promotor were created by lentiviral transduction, and single clones were selected by fluorescence-activated cell sorting (FACS) for further study. Six genes with various expression levels were targeted using lentiviral sgRNA from two libraries in four cell clones with various KRAB-dCas9 expression levels. We determined the knockdown efficiency and the expression level of the dCas9 protein /sgRNA level by flow cytometry. The cell clone with the highest KRAB-dCas9 expression level achieved effective CRISPRi knockdown, and is statistically different from other clones, indicating enough KRAB-dCas9 expression might be a prerequisite for CRISPRi. Utilizing this clone, we modified the expression level of sgRNA by adopting different multiplicity of infection (MOI)in lentiviral transduction and found that the knockdown efficiency was neither affected by the target gene expression level nor does it correlate with KRAB-dCas9 level, which remained relatively constant (CV=2.2%) across knockdown experiments. 74.72%, 72.28%, 39.08% knockdown of mmadhc, rpia, znf148 genes were achieved, and the knockdown efficiency correlated well with the sgRNA expression level. Linear regression modeling of the data revealed that the knockdown efficiency is significantly affected by both KRAB-dCas9 and sgRNA level, and the sgRNA level has a greater impact, based on the standardized coefficient (0.525 for KRAB-dCas9, 0.981 for sgRNA), indicating that sgRNA level is a major factor affecting CRISPRi efficiency.

cancer biology↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2019

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

Computational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.

bioinformatics↗

bioRxiv2018

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

Prediction of peptide binding to MHC molecules plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Although numerous computational methods have been developed to this end, several challenges still remain in predicting peptide-MHC interactions. Many previous methods are allele-specific, training separate models for individual alleles and are thus unable to yield accurate predictions for those alleles with limited training data. Despite that there exist several pan-specific algorithms that train a common model for different alleles, they only adopt simple model structures that generally have limited performance in capturing the complex underlying patterns of peptide-MHC interactions. Here we present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson Correlation Coefficient by up to 23 percent. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide-MHC class I interactions.

bioinformatics↗

bioRxiv2018

NeoDTI: Neural integration of neighborinformation from a heterogeneous network fordiscovering new drug-target interactions

MotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.\n\nResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.\n\nAvailability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI.\n\nContactzengjy321@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2016

Deep learning with feature embedding for compound-proteininteraction prediction

Accurately identifying compound-protein interactions in silico can deepen our understanding of the mechanisms of drug action and significantly facilitate the drug discovery and development process. Traditional similarity-based computational models for compound-protein interaction prediction rarely exploit the latent features from current available large-scale unlabelled compound and protein data, and often limit their usage on relatively small-scale datasets. We propose a new scheme that combines feature embedding (a technique of representation learning) with deep learning for predicting compound-protein interactions. Our method automatically learns the low-dimensional implicit but expressive features for compounds and proteins from the massive amount of unlabelled data. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline for accurate compound-protein interaction prediction, even when the interaction knowledge of compounds and proteins is entirely unknown. Evaluations on current large-scale databases of the measured compound-protein affinities, such as ChEMBL and BindingDB, as well as known drug-target interactions from DrugBank have demonstrated the superior prediction performance of our method, and suggested that it can offer a useful tool for drug development and drug repositioning.

bioinformatics↗