APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient.

Seroprevalence of Diphtheria and Tetanus Immunoglobulin G among the General Health Population in Guangzhou, China.

A seroepidemiological study was conducted in 2018 to assess diphtheria and tetanus antibodies in Guangzhou, China. Diphtheria and tetanus antibody concentrations were measured with an enzyme-linked immunosorbent assay. A total of 715 subjects were enrolled in the study.

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides.

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy.

Generation of novel Diels-Alder reactions using a generative adversarial network.

Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models. However, their application in the field of organic chemistry has been limited; thus, in this study, we attempt to utilize a GAN as a generative model for the generation of Diels-Alder reactions. A MaskGAN model was trained with 14 092 Diels-Alder reactions, and 1441 novel Diels-Alder reactions were generated.

Transformer-based multitask learning for reaction prediction under low-resource circumstances.

Recently, effective and rapid deep-learning methods for predicting chemical reactions have significantly aided the research and development of organic chemistry and drug discovery. Owing to the insufficiency of related chemical reaction data, computer-assisted predictions based on low-resource chemical datasets generally have low accuracy despite the exceptional ability of deep learning in retrosynthesis and synthesis. To address this issue, we introduce two types of multitask models: retro-forward reaction prediction transformer (RFRPT) and multiforward reaction prediction transformer (MFRPT).

Virtual data augmentation method for reaction prediction.

To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.

Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios.

In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problems. Using a self-supervised pretraining strategy called MAsked Sequence to Sequence (MASS), the Transformer model can absorb the chemical information of about 1 billion molecules and then fine-tune on a small-scale reaction prediction. To further strengthen the predictive performance of our model, we combine MASS with the reaction transfer learning strategy.

From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.

Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation.

Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions.

While state-of-art models can predict reactions through the transfer learning of thousands of samples with the same reaction types as those of the reactions to predict, how to prepare such models to predict "unseen" reactions remains an unanswered question. We aimed to study the Transformer model's ability to predict "unseen" reactions through "zero-shot reaction prediction (ZSRP)", a concept derived from zero-shot learning and zero-shot translation. We reproduced the human invention of the Chan-Lam coupling reaction where the inventor was inspired by the Suzuki reaction when improving Barton's bismuth arylation reaction.

A graph-convolutional neural network for addressing small-scale reaction prediction.

We describe a graph-convolutional neural network (GCN) model, the reaction prediction capabilities of which are as potent as those of the transformer model based on sufficient data, and we adopt the Baeyer-Villiger oxidation reaction to explore their performance differences based on limited data. The top-1 accuracy of the GCN model (90.4%) is higher than that of the transformer model (58.

[The molecular epidemiology characteristics of norovirus in environment and clinical samples in Guangzhou from 2009 to 2011].

Objective: To investigate the molecular epidemiological characteristics of norovirus in Guangzhou from 2009 to 2011.

Methods: A total of 183 water samples, 1162 seafood samples and 1066 diarrhea stool specimens were collected from January 2010 to May 2011, June 2009 to June 2011 and July 2009 to December 2010 respectively in Guangzhou. Norovirus was detected by real time reverse transcript-PCR (qRT-PCR).

Molecular epidemiological and virological study of dengue virus infections in Guangzhou, China, during 2001-2010.

Background: Dengue virus (DENV) infection is the most prevalent arthropod-borne viral infection in tropical and subtropical regions worldwide. Guangzhou has the ideal environment for DENV transmission and DENV epidemics have been reported in this region for more than 30 years.

Methods: Information for DENV infection cases in Guangzhou from 2001 to 2010 were collected and analyzed.

Antibody dynamics of 2009 influenza A (H1N1) virus in infected patients and vaccinated people in China.

Background: To evaluate the risk of the recurrence and the efficiency of the vaccination, we followed-up antibody responses in patients with the 2009 pandemic H1N1 influenza and persons who received the pandemic H1N1 vaccine in Guangzhou China.

Methods: We collected serum samples from 129 patients and 86 vaccinated persons at day 0, 15, 30, 180 after the disease onset or the vaccination, respectively. Antibody titers in these serum samples were determined by haemagglutination inhibition (HI) assay using a local isolated virus strain A/Guangdong Liwan/SWL1538/2009(H1N1).

Epidemiological investigation of an outbreak of pandemic influenza A (H1N1) 2009 in a boarding school: serological analysis of 1570 cases.

Background: A large number of 2009 pandemic influenza A (H1N1) infections were localized in school populations.

Objectives: To describe the epidemiology, clinical features and risk factors associated with an outbreak that occurred at a vocational boarding school in Guangzhou, P.R.

[Study on the inhibitory effect of RNA interference on replication of dengue virus].

To investigate the inhibitory effect of RNA interference (RNAi) on dengue virus I (DENV-1) replication. Small interfering RNA (siRNA) against the PreM gene of dengue virus was synthesized and transfected into C6/36 cells with liposome, which was then attacked by DENV-1 virus. The antiviral effect of siRNA was evaluated by cytopathic effect (CPE), the cell survival rate measured by MTT, and virus RNA quantified by real-time RT-PCR.

Inhibitory effect of small interfering RNA on dengue virus replication in mosquito cells.

Background: Dengue viruses (DENs) are the wildest transmitted mosquito-borne pathogens throughout tropical and sub-tropical regions worldwide. Infection with DENs can cause severe flu-like illness and potentially fatal hemorrhagic fever. Although RNA interference triggered by long-length dsRNA was considered a potent antiviral pathway in the mosquito, only limited studies of the value of small interfering RNA (siRNA) have been conducted.

[Investigation on a seasonal influenza accompanying with the first locale novel A/H1N1 influenza outbreak in China].

Objective: To timely summarize past experience and to provide more pertinent reference for control and prevention in A/H1N1 cases in influenza season.

Methods: During May 25 to 31, 2009, 2 secondary community cases caused by a influenza A/H1N1 imported case. In the close contacts of 3 A/H1N1 cases, 14 had some aspirator symptoms onset, such as fever (> or = 37.

[Relation between clinical detection rates of the novel influenza virus A/H1N1 RNA in confirmed patients in Guangzhou and the disease course].

Objective: To study the relation of the detection rates of the novel influenza virus A/H1N1 RNA in clinically confirmed patients in the 2009 pandemic with the age distribution of the patients and the disease course.

Methods: A total of 151 clinical patients with H1N1 infection were enrolled in this study, from whom 833 dynamic throat swab samples were obtained for detecting the H1N1 RNA using real-time PCR. A statistical analysis of the age distribution was performed among the patients with different disease courses.

[Risk assessment of H5N1 human infection after an outbreak of avian influenza in water fowl].

Objective: To evaluate the risk of human infection after the outbreak of avian influenza H5N1 in animals, and probe the possibility for virus transmission.

Methods: By means of field epidemiological study, molecular epidemiology, serology and emergency surveillance, persons who had ever closely contacted with sick or dead poultry were observed. While, the RT-PCR and gene sequencing method were used to detect H5 nucleic acid from environmental swabs from 4 epidemic spots, and hemagglutination inhibition assay was also used to detect H5 antibody.

[An analysis on genetic characteristics of hemagglutinin and neuraminidase of type B influenza viruses in Guangzhou in 2006].

To understand the genetic characteristics of hemagglutinin (HA) and neuraminidase (NA) of type B influenza viruses in Guangzhou in 2006, three virus strains from etiology surveillance and seven strains from outbreaks were investigated. Genome RNAs of type B influenza viruses were extracted and reverse-transcripted into cDNAs using random primers. The whole-length DNA of HA and NA were amplified by polymerase chain reaction (PCR), cloned into T-A plasmid and sequenced, and analyzed phylogenetically by DNAstar software.