AagingBase: a comprehensive database of anti-aging peptides.

The process of aging is an intrinsic and inevitable aspect of life that impacts every living organism. As biotechnological advancements continue to shape our understanding of medicine, peptide therapeutics have emerged as a promising strategy for anti-aging interventions. This is primarily due to their favorable attributes, such as low immunogenicity and cost-effective production. Peptide-based treatments have garnered widespread acceptance and interest in aging research, particularly in the context of age-related therapies. To effectively develop anti-aging treatments, a comprehensive understanding of the physicochemical characteristics of anti-aging peptides is essential. Factors such as amino acid composition, instability index, hydrophobic areas and other relevant properties significantly determine their efficacy as potential therapeutic agents. Consequently, the creation of 'AagingBase', a comprehensive database for anti-aging peptides, aims to facilitate research on aging by leveraging the potential of peptide therapies. AagingBase houses experimentally validated 282 anti-aging peptides collected from 54 research articles and 236 patents. Employing state-of-the-art computational techniques, the acquired sequences have undergone rigorous physicochemical calculations. Furthermore, AagingBase presents users with various informative analyses highlighting atomic compositions, secondary structure fractions, tertiary structure, amino acid compositions and frequencies. The database also offers advanced search and filtering options and similarity search, thereby aiding researchers in understanding their biological functions. Hence, the database enables efficient identification and prioritization of potential peptide candidates in geriatric medicine and holds immense potential for advancing geriatric medicine research and innovations. AagingBase can be accessed without any restriction. Database URL: https://project.iith.ac.in/cgntlab/aagingbase/.

MyeloDB: a multi-omics resource for multiple myeloma.

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/ .

A comprehensive rhythmicity analysis of host proteins and immune factors involved in malaria pathogenesis to decipher the importance of host circadian clock in malaria.

Background: Circadian rhythms broadly impact human health by regulating our daily physiological and metabolic processes. The circadian clocks substantially regulate our immune responses and susceptibility to infections. Malaria parasites have intrinsic molecular oscillations and coordinate their infection cycle with host rhythms. Considering the cyclical nature of malaria, a clear understanding of the circadian regulations in malaria pathogenesis and host responses is of immense importance. Methods: We have thoroughly investigated the transcript level rhythmic patterns in blood proteins altered in falciparum and vivax malaria and malaria-related immune factors in mice, baboons, and humans by analyzing datasets from published literature and comprehensive databases. Using the Metascape and DAVID platforms, we analyzed Gene Ontology terms and physiological pathways associated with the rhythmic malaria-associated host immune factors. Results: We observed that almost 50% of the malaria-associated host immune factors are rhythmic in mice and humans. Overlapping rhythmic genes identified in mice, baboons, and humans, exhibited enrichment (Q < 0.05, fold-enrichment > 5) of multiple physiological pathways essential for host immune and defense response, including cytokine production, leukocyte activation, cellular defense, and response, regulation of kinase activity, B-cell receptor signaling pathway, and cellular response to cytokine stimulus. Conclusions: Our analysis indicates a robust circadian regulation on multiple interconnected host response pathways and immunological networks in malaria, evident from numerous rhythmic genes involved in those pathways. Host immune rhythms play a vital role in the temporal regulation of host-parasite interactions and defense machinery in malaria.

Identification of Novel Peptides as Potential Modulators of Aß42 Amyloidogenesis: An in silico Approach.

AIMS: Alzheimer's disease is a neurodegenerative disease for which no cure is available. The presence of amyloid plaques in the extracellular space of neural cells is the key feature of this fatal disease. BACKGROUND: The proteolysis of Amyloid Precursor Protein by presenilin leads to the formation of Amyloid-beta peptides (Aß 42/40). Deposition of 42 residual Aß peptides forms fibril's structure, disrupting neuron synaptic transmission, inducing neural cell toxicity, and ultimately leading to neuron death. OBJECTIVE: Various novel peptides have been investigated via molecular docking and molecular dynamic simulation studies to investigate their effects on Aß amyloidogenesis. METHODS: The sequence-based peptides were rationally designed and investigated for their interaction with Aß42 monomer and fibril, and their influence on the structural stability of target proteins was studied. RESULTS: Analyzed docking results suggest that the peptide YRIGY (P6) has the highest binding affinity with Aß42 fibril amongst all the synthetic peptides, and the peptide DKAPFF (P12) similarly shows a better binding with the Aß42 monomer. Moreover, simulation results also suggest that the higher the binding affinity, the better the inhibitory action. CONCLUSION: These findings indicate that both the rationally designed peptides can modulate amyloidogenesis, but peptide (P6) has better potential for the disaggregation of the fibrils. In contrast, peptide P12 stabilizes the native structure of the Aß42 monomer more effectively and hence can serve as a potential amyloid inhibitor. Thus, these peptides can be explored as therapeutic agents against Alzheimer's disease. Experimental testing of these peptides for immunogenicity, stability in cellular conditions, toxic effects and membrane permeability can be the future research scope of this study.

Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independent dataset, we achieved an accuracy of 95.90% with MCC of 0.917. To show its broader applicability, we used this classifier to predict the clinical outcome using survival data from two large cohorts of DLBCL patients. In survival analysis, MLP-COO recapitulates the survival probabilities of DLBCL patients based on their COO in both cohorts. We anticipate that MLP-COO model developed in this study will benefit in the accurate COO prediction of DLBCL patients and their clinical outcomes.

Human Serum Albumin Aggregation and its Modulation Using Nanoparticles: A Review.

Amyloid fibrils are highly stable protein fibrillar aggregates believed to be involved in various neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, and prion diseases. Inhibiting the aggregation process is a potential strategy to prevent diseases caused by amyloid formation. In this regard, nanoparticles have emerged as promising candidates owing to their unique physical/chemical properties of small size, large surface area, biocompatibility, biodegradability, non-toxicity, and ease of functionalization. Human Serum Albumin (HSA) is a soluble multidomain monomeric protein that interacts with various ligands and hormones, aiding in their transport, distribution, metabolism in the circulatory system, and also plays a vital role in extracellular fluid volume stabilization. Under certain in vitro conditions, HSA has been reported to undergo conformational changes leading to fibril formation and hence acts as a suitable model for studying amyloidogenesis. In this review, we have explored the effects of various nanoparticles on HSA aggregation and their mechanism of action. The study will throw light on the mechanistic details of nanoparticle-mediated amyloid modulation, which will help in the development of effective therapeutics against amyloidosis.