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Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

The purpose of this study is to investigate the molecular interactions and potential therapeutic uses of Eltrombopag (EPAG), a small molecule that activates the cMPL receptor. EPAG has been found to be effective in increasing platelet levels and alleviating thrombocytopenia. We utilized computational techniques to predict and confirm the complex formed by the ligand (EPAG) and the Thrombopoietin receptor (TPO-R) cMPL, elucidating the role of RAS, JAK-2, STAT-3, and other essential elements for downstream signaling. Molecular dynamics (MD) simulations were employed to evaluate the stability of the ligand across specific proteins, showing favorable characteristics. For the first time, we examined the presence of TPO-R in human umbilical cord mesenchymal stem cells (hUCMSC) and human gingival mesenchymal stem cells (hGMSC) proliferation. Furthermore, treatment with EPAG demonstrated angiogenesis and vasculature formation of endothelial lineage derived from both MSCs. It also indicated the activation of critical factors such as RUNX-1, GFI-1b, VEGF-A, MYB, GOF-1, and FLI-1. Additional experiments confirmed that EPAG could be an ideal molecule for protecting against UVB radiation damage, as gene expression (JAK-2, ERK-2, MCL-1, NFkB, and STAT-3) and protein CD90/cMPL analysis showed TPO-R activation in both hUCMSC and hGMSC. Overall, EPAG exhibits significant potential in treating radiation damage and mitigating the side effects of radiotherapy, warranting further clinical exploration. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is one of the deadliest subtypes of breast cancer (BC) for its high aggressiveness, heterogeneity, and hypoxic nature. Based on biological and clinical observations the TNBC related mortality is very high worldwide. Emerging studies have clearly demonstrated that hypoxia regulates the critical metabolic, developmental, and survival pathways in TNBC, which include glycolysis and angiogenesis. Alterations to these pathways accelerate the cancer stem cells (CSCs) enrichment and immune escape, which further lead to tumor invasion, migration, and metastasis. Beside this, hypoxia also manipulates the epigenetic plasticity and DNA damage response (DDR) to syndicate TNBC survival and its progression. Hypoxia fundamentally creates the low oxygen condition responsible for the alteration in Hypoxia-Inducible Factor-1alpha (HIF-1a) signaling within the tumor microenvironment, allowing tumors to survive and making them resistant to various therapies. Therefore, there is an urgent need for society to establish target-based therapies that overcome the resistance and limitations of the current treatment plan for TNBC. In this review article, we have thoroughly discussed the plausible significance of HIF-1a as a target in various therapeutic regimens such as chemotherapy, radiotherapy, immunotherapy, anti-angiogenic therapy, adjuvant therapy photodynamic therapy, adoptive cell therapy, combination therapies, antibody drug conjugates and cancer vaccines. Further, we also reviewed here the intrinsic mechanism and existing issues in targeting HIF-1a while improvising the current therapeutic strategies. This review highlights and discusses the future perspectives and the major alternatives to overcome TNBC resistance by targeting hypoxia-induced signaling. [ABSTRACT FROM AUTHOR]

Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/). [ABSTRACT FROM AUTHOR]

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity. [ABSTRACT FROM AUTHOR]