GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network.

Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph attention network (GAT) for compounds and a long short-term memory neural network (LSTM) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, CPGL (CPI with GAT and LSTM) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. CPGL demonstrated an excellent predictive performance and outperforms recently reported deep learning models. Based on 3 public CPI datasets, C.elegans, Human and BindingDB, CPGL represented 1 - 5% improvement compared to existing deep-learning models. Our method also achieves excellent results on datasets with imbalanced positive and negative proportions constructed based on the C.elegans and Human datasets. More importantly, using 2 label reversal datasets, GPCR and Kinase, CPGL showed superior performance compared to other existing deep learning models. The AUC were substantially improved by 20% on the Kinase dataset, indicative of the robustness and generalizability of CPGL.

Alzheimer's disease progression model using disability assessment for dementia scores from bapineuzumab trials.

OBJECTIVE: Disability assessment for dementia (DAD) measurements from two phase-3 studies of bapineuzumab in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients were integrated to develop a disease progression model. METHODS: We evaluated longitudinal changes in DAD scores, baseline factors affecting disease progression, and bapineuzumab effect on disease progression. RESULTS: A beta regression model best described DAD disease progression. The estimated treatment effect of bapineuzumab was not significant, consistent with lack of clinical efficacy observed in the primary analysis. The model suggested that progression of DAD tended to decrease with increase in bapineuzumab exposure. The exposure-response relationship was similar regardless of APOE ε4 status but more pronounced in patients with mild AD. Baseline disease status, age, memantine use, and years since onset (YSO) had significant effects on baseline DAD scores. AD concomitant medication use, baseline disease status, and YSO had significant effects on disease progression rate, measured by DAD score. CONCLUSIONS: The beta regression model is a sensible modeling approach to characterize functional decline in AD patients. This analysis suggested a possible effect of bapineuzumab exposure on DAD progression. Further evaluation may be warranted in future studies. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00575055 and NCT00574132.

Alzheimer's disease assessment scale-cognitive 11-item progression model in mild-to-moderate Alzheimer's disease trials of bapineuzumab.

INTRODUCTION: The objective of this study was to estimate longitudinal changes in disease progression (measured by Alzheimer's disease assessment scale-cognitive 11-item [ADAS-cog/11] scale) after bapineuzumab treatment and to identify covariates (demographics or baseline characteristics) contributing to the variability in disease progression rate and baseline disease status. METHODS: A population-based disease progression model was developed using pooled placebo and bapineuzumab data from two phase-3 studies in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients. RESULTS: A beta regression model with the Richard's function as the structural component best described ADAS-cog/11 disease progression for mild-to-moderate AD population. This analysis confirmed no effect of bapineuzumab exposure on ADAS-cog/11 progression rate, consistent with the lack of clinical efficacy observed in the statistical analysis of ADAS-cog/11 data in both studies. Assessment of covariates affecting baseline severity revealed that men had a 6% lower baseline ADAS-cog/11 score than women; patients who took two AD concomitant medications had a 19% higher (worse) baseline score; APOE ε4 noncarriers had a 5% lower baseline score; and patients who had AD for a longer duration had a higher baseline score. Furthermore, shorter AD duration, younger age, APOE ε4 carrier status, and use of two AD concomitant medications were associated with faster disease progression rates. Patients who had an ADAS-cog/11 score progression rate that was not statistically significantly different from 0 typically took no AD concomitant medications. DISCUSSION: The beta regression model is a sensible modeling approach to characterize cognitive decline in AD patients. The influence of bapineuzumab exposure on disease progression measured by ADAS-cog/11 was not significant. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00575055 and NCT00574132.

A New Map Location of Gene Stb3 for Resistance to Septoria Tritici Blotch in Wheat.

Septoria tritici blotch (STB), caused by Mycosphaerella graminicola (synonym: Zymoseptoria tritici; asexual stage: Septoria tritici), is an important disease of wheat worldwide. Management of the disease usually is by host resistance or fungicides. However, M. graminicola has developed insensitivity to most commonly applied fungicides so there is a continuing need for well-characterized sources of host resistance to accelerate the development of improved wheat cultivars. Gene Stb3 has been a useful source of major resistance, but its mapping location has not been well characterized. Based on linkage to a single marker, a previous study assigned Stb3 to a location on the short arm of chromosome 6D. However, the results from the present study show that this reported location is incorrect. Instead, linkage analysis revealed that Stb3 is located on the short arm of wheat chromosome 7A, completely linked to microsatellite (SSR) locus Xwmc83 and flanked by loci Xcfa2028 (12.4 cM distal) and Xbarc222 (2.1 cM proximal). Linkage between Stb3 and Xwmc83 was validated in BC1F3 progeny of other crosses, and analyses of the flanking markers with deletion stocks showed that the gene is located on 7AS between fraction lengths 0.73 and 0.83. This revised location of Stb3 is different from those for other STB resistance genes previously mapped in hexaploid wheat but is approximately 20 cM proximal to an STB resistance gene mapped on the short arm of chromosome 7Am in Triticum monococcum. The markers described in this study are useful for accelerating the deployment of Stb3 in wheat breeding programs.