Wet-lab Experiments Research Articles

Understanding the Role of Layered Minerals in the Emergence and Preservation of Proto-Proteins and Detection of Traces of Early Life.

ConspectusThe origin of life remains one of the most profound mysteries in science. Over millennia, theories have evolved, yet the question persists: How did life emerge from inanimate matter? At its core, the study of life's origin offers insights into our place in the universe and the nature of life itself. By delving into the chemical and geological processes that led to life's emergence, scientists gain a deeper understanding of the fundamental principles that govern living systems. This knowledge not only expands our scientific understanding but also has profound implications for fields ranging from astrobiology to synthetic biology.This research employs a multidisciplinary approach, combining a diverse array of techniques, from space missions to wet laboratory experiments to theoretical modeling. Investigations into the formation of the first proto-biomolecules are tailored to explore both the complex molecular processes that underpin life and the geological contexts in which these processes may have occurred. While laboratory experiments are aimed at mimicking the processes of early planets, not every process and sample is attainable. To this end, we demonstrate the use of molecular modeling techniques to complement experimental efforts and extraterrestrial missions. The simulations enable researchers to test hypotheses and explore scenarios that are difficult or impossible to replicate in the laboratory, bridging gaps in our understanding of prebiotic processes across vast time and space scales.Minerals, particularly layered structures like clays and hydrotalcites, play diverse and pivotal roles in the origin of life. They concentrate organic species, catalyze polymerization reactions (such as peptide formation), and provide protective environments for the molecules. Minerals have also been suggested to have acted as primitive genetic materials. Nevertheless, they may lack the ability for long-term information replication. Instead, we suggest that minerals may act as transcribers of information encoded in environmental cyclic phenomena, such as tidal or seasonal changes. We argue that extensive protection of the produced polymer will immobilize it, making it inactive for any further function. Therefore, in order to generate a functional polymer, it is essential that it remains mobile and chemically active. Furthermore, we suggest a route to the identification of pseudobiosignatures, a polymer that was polymerized on the same mineral surface and consequently retained through overprotection.This Account presents a comprehensive evaluation of the current understanding of the role of layered mineral surfaces on life's origin and biosignature preservation. It highlights the complexity of mineral-organic interactions and proposes pathways for proto-biomolecule emergence and methods for identifying and interpreting potential biosignatures. Ultimately, the quest to uncover the origin of life continues to drive scientific exploration and innovation, offering profound insights into the fundamental nature of existence and our place in the universe.

Positive-Unlabelled learning for identifying new candidate Dietary Restriction-related genes among ageing-related genes

Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, the existing ML approach naively labels genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence. This hinders the reliability of the negative examples (non-DR-related genes) and the method’s ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms (p<0.05) the existing state-of-the-art approach in three predictive accuracy metrics with up to ∼40% lower computational cost in the best case, and we identify 4 new promising DR-related genes (PRKAB1, PRKAB2, IRS2, PRKAG1), all with evidence from the existing literature supporting their potential DR-related role.

Discovery of potential antidiabetic peptides using deep learning

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.

A Systematic Study on Mirror Repeats in CTLA4 and KCNJ11 Genes of Diabetes

All cellular functions are controlled by the regulatory material which is constituted majorly by variety type of repeat elements. Among that types a unique pattern of bases form a Mirror repeat (MR) which thought to plays variety of roles at the genomic level that include regulation of gene expression, genomic integrity, triplex structure formation etc. The present research focused on identification of Mirror repeats in different genes types involved in both diabetes types. Two different genes CTLA4 (T1D) &amp; KCNJ11 (T2D) were targeted to identify these sequence type. A manual method refers as FPCB is utilized to extract out MR sequences. The targeted gene sequences were divided into short regions so that it can be handled out easily while bioinformatics based analysis. Among the both types it was reported that both genes have different pattern of distribution of MR sequences. CTLA4 have 346 MR sequences in total in which MR235 have highest length of 50bps. In case of KCNJ11 a total no of 235 MR sequences were reported in which MR105 having highest length of 58bps. The present study will be helpful to elucidate the roles of MR sequences in diseases development &amp; progression. It will be helpful for sequence based protein finding studies using bioinformatics tools as well as wet lab experiments.

Multiscale Computational Study of Cellulose Acetate-Water Miscibility: Insights from Molecularly Informed Field-Theoretic Modeling.

Cellulose acetate (CA), a prominent water-soluble derivative of cellulose, is a promising biodegradable ingredient that has applications in films, membranes, fibers, drug delivery, and more. In this work, we present a molecularly informed field-theoretic model for CA to explore its phase behavior in aqueous solutions. By integrating atomistic details into large-scale field-theoretic simulations via the relative entropy coarse-graining framework, our approach enables efficient calculations of CA's miscibility window as a function of the degree of substitution (DS) of cellulose hydroxyl groups with acetate side chains. This allows us to capture the intricate phase behavior of CA, particularly its unique miscibility at intermediate substitution, without relying on experimental input. Additionally, the model directly probes CA solution behavior specific to the relative DS at C2, C3, and C6 alcohol sites, providing insights for the rational design of water-soluble CA for diverse applications. This work demonstrates a promising integration of molecularly informed field theories, complementing wet-lab experimentation, for engineering the next-generation polymeric materials with precisely tailored properties.

Discerning computational, in vitro and in vivo investigations of self-assembling empagliflozin polymeric micelles in type-2 diabetes.

Empagliflozin (EMPA) is an SGLT2 inhibitor, a new class of anti-diabetic medication, indicated for treating type-2 diabetes. Its low permeability, poor solubility and bioavailability limits its use in management of diabetes. The study was aimed to formulate EMPA loaded polymeric micelles (PMs) to overcome these obstacles in oral absorption. In silico studies-molecular docking, molecular dynamic simulation (MDS), and quantum chemical calculation were employed to study the interaction of EMPA with different polymers. EMPA loaded TPGS polymeric micelles (EMPA-TPGS-PMs) were formulated by direct dissolution method and characterized in terms of surface morphology, entrapment, particle size, in vitro drug release, and in vitro cytotoxicity (HEK293 cells). In vivo pharmacokinetic and pharmacodynamic studies were also performed. The results suggested a good interaction between TPGS and EMPA with lowest binding energy compared to other polymers. Further MDS results and DFT calculations validated the stable binding of the complex hence TPGS was selected for further wet lab experiments. The EMPA-TPGS complex displayed lower value of Total energy (T.E.) than its individual components, indicating the overall stability of the complex while, the energy band gap (∆E) value lied between the two individual molecules, signifying the better electron transfer between HOMO and LUMO of the complex. Based on the solubility, entrapment and cytotoxicity studies, 5% TPGS was selected for formulating drug loaded micelles. EMPA-TPGS5-PMs presented a size of 9.008 ± 1.25nm, Polydispersity index (PDI) of 0.254 ± 0.100, a controlled release behaviour upto 24h. SEM and AFM images of the nanoformulation suggested spherical particles whereas, DSC, and PXRD studies confirmed the loss of crystallinity of EMPA. A 3.12-folds higher AUC and a greater reduction in blood glucose levels was exhibited by EMPA-TPGS5-PMs in comparison to EMPA-SUSP in mice model. EMPA-TPGS-PMs has exhibited better bio absorption and therapeutic effectiveness in diabetes treatment. This improved performance would open the possibility of dose reduction, reduced dosing frequency & dose-related side effects, improving pharmaco-economics and thereby improved overall compliance to the patient. However, this translation from bench to bedside would necessitate studies in higher animals and human volunteers.

Hybrid Predictive Machine Learning Model for the Prediction of Immunodominant Peptides of Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST-BST-XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model's reliability and an average accuracy of 97.21% was recorded for the ChST-BST-XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development.

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks

In this paper, we aim to build a platform that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real dataset from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the MultiovizR package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at https://multioviz.ccv.brown.edu/.

Identifying circRNA-disease association based on relational graph attention network and hypergraph attention network

In recent years, with the in-depth study of circRNA, scholars have begun to discover a synergistic relationship between circRNA and microorganisms. Traditional wet lab experiments in biology require expensive financial, material, and human resources to investigate the relationship between circRNA and diseases. Therefore, we propose a new predictive model for inferring the association between circRNA and diseases, called HAGACDA. Specifically, we first aggregate the unique features of circRNA and diseases themselves through singular value decomposition, Pearson similarity, and the biological information characteristics of circRNA and diseases. Utilizing the competitive relationships between miRNA and other microorganisms, we construct a circRNA-miRNA-disease multi-source heterogeneous network. Subsequently, we use a relational graph attention network to aggregate features based on the structural connections between different nodes. To address the inherent limitations in capturing high-order patterns in edge sets, we integrate a hypergraph attention network to extract features of circRNA and diseases. Finally, association prediction scores for node pairs are obtained through a multilayer perceptron. We conducted a comprehensive analysis of the model, including comparative experiments and case studies. Experimental results demonstrate that our model accurately predicts the association between circRNA and diseases.

MMCL-CPI: A multi-modal compound-protein interaction prediction model incorporating contrastive learning pre-training

MotivationCompound-protein interaction (CPI) prediction plays a crucial role in drug discovery and drug repositioning. Early researchers relied on time-consuming and labor-intensive wet laboratory experiments. However, the advent of deep learning has significantly accelerated this progress. Most existing deep learning methods utilize deep neural networks to extract compound features from sequences and graphs, either separately or in combination. Our team’s previous research has demonstrated that compound images contain valuable information that can be leveraged for CPI task. However, there is a scarcity of multimodal methods that effectively combine sequence and image representations of compounds in CPI. Currently, the use of text-image pairs for contrastive language-image pre-training is a popular approach in the multimodal field. Further research is needed to explore how the integration of sequence and image representations can enhance the accuracy of CPI task. ResultsThis paper presents a novel method called MMCL-CPI, which encompasses two key highlights: 1) Firstly, we propose extracting compound features from two modalities: one-dimensional SMILES and two-dimensional images. This approach enables us to capture both sequence and spatial features, enhancing the prediction accuracy for CPI. Based on this, we design a novel multimodal model. 2) Secondly, we introduce a multimodal pre-training strategy that leverages comparative learning on a large-scale unlabeled dataset to establish the correspondence between SMILES string and compound’s image. This pre-training approach significantly improves compound feature representations for downstream CPI task. Our method has shown competitive results on multiple datasets.

PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation

Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

Efficient Deep Model Ensemble Framework for Drug-Target Interaction Prediction.

Accurate prediction of Drug-Target Interactions (DTI) is crucial for drug development. Current state-of-the-art deep learning methods have significantly advanced the field; however, these methods exhibit limitations in predictive performance and the propensity for false negatives. Therefore, we propose EADTN, a simple and efficient ensemble model. We have designed an innovative feature adaptation technique to automatically extract local weights of drugs and targets, and we utilize clustering-enhanced parameter fine-tuning to overcome the issue of false negatives, thereby enhancing its reliability in drug discovery. Based on EADTN, we also propose a Shapley value-based method for identifying key drug substructures, effectively enhancing the model's interpretability. Additionally, we utilized EADTN to reveal potential interactions between NQO1 targets and the drugs SIRT-IN-1 and LY2183240, which were subsequently validated through wet-lab experiments. Experimental evidence demonstrates that EADTN consistently outperforms existing best-performing models across various data sets, promising significant benefits in fields such as drug repositioning.

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks.

Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.

Towards an enhanced understanding of osteoanabolic effects of PTH-induced microRNAs on osteoblasts using a bioinformatic approach.

In this study, we used a bioinformatic approach to construct a miRNA-target gene interaction network potentially involved in the anabolic effect of parathyroid hormone analogue teriparatide [PTH (1-34)] on osteoblasts. We extracted a dataset of 26 microRNAs (miRNAs) from previously published studies and predicted miRNA target interactions (MTIs) using four software tools: DIANA, miRWalk, miRDB, and TargetScan. By constructing an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated signaling pathways regulating pluripotency of stem cells, the Hippo signaling pathway, and the TGF-beta signaling pathway as the most significant pathways in the effects of PTH on osteoblasts. Furthermore, we constructed intersection of MTI networks for these three pathways and added validated interactions. There are 8 genes present in all three selected pathways and a set of 18 miRNAs are predicted to target these genes, according to literature data. The most important genes in all three pathways were BMPR1A, BMPR2 and SMAD2 having the most interactions with miRNAs. Among these miRNAs, only miR-146a-5p and miR-346 have validated interactions in these pathways and were shown to be important regulators of these pathways. In addition, we also propose miR-551b-5p and miR-338-5p for further experimental validation, as they have been predicted to target important genes in these pathways but none of their target interactions have yet been verified. Our wet-lab experiment on miRNAs differentially expressed between PTH (1-34) treated and untreated mesenchymal stem cells supports miR-186-5p from the literature obtained data as another prominent miRNA. The meticulous selection of miRNAs outlined will significantly support and guide future research aimed at discovering and understanding the crucial pathways of osteoanabolic PTH-epigenetic effects on osteoblasts. Additionally, they hold potential for the discovery of new PTH target genes, innovative biomarkers for the effectiveness and safety of osteoporosis-affected treatment, as well as novel therapeutic targets.

Laboratory success 101: how to excel in your first lab project

The majority of biochemistry research is based on wet laboratory experiments, yet undergraduate degrees strongly focus on the theoretical aspects of this discipline. The first lab project can be a significant learning curve for students, involving challenges in laboratory techniques, research reproducibility, consistency, resilience, and practical skills. Drawing from my experience in two research internships, a master’s project, and completing three rotations in a PhD program, I have outlined here valuable tips and advice to help undergraduates navigate this transition smoothly, enabling them to excel in their first lab project.

Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.

Synthesis and Semi-Synthesis of Alpha-Synuclein: Insight into the Chemical Complexity of Synucleinopathies.

The chemical rules governing protein folding have intrigued generations of researchers for decades. With the advent of artificial intelligence (AI), prediction of protein structure has improved tremendously. However, there is still a level of analysis that is only possible through wet laboratory experiments, especially in respect to the investigation of the pathological effect of mutations and posttranslational modifications (PTMs) on proteins of interest. This requires the availability of pure peptides and proteins in sufficient quantities for biophysical, biochemical, and functional studies. In this context, chemical protein synthesis and semi-synthesis are powerful tools in protein research, which help to enlighten the role of protein modification in the physiology and pathology of proteins. A protein of high interest in the field of biomedicine is alpha-synuclein (aSyn), a protein deeply associated with several devastating neurodegenerative disorders such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), or multiple systems atrophy (MSA). Here, we describe several methods and pathways to synthesize native or modified aSyn, and discuss how these approaches enable us to address pathological mechanisms that may open novel perspectives for therapeutic intervention.

ResFungi: A Novel Protein Database of Antifungal Drug Resistance Genes Using a Hidden Markov Model Profile.

Fungal infections vary from superficial to invasive and can be life-threatening in immunocompromised and healthy individuals. Antifungal resistance is one of the main reasons for an increasing concern about fungal infections as they become more complex and harder to treat. The fungal "omics" databases help us find drug resistance genes, which is of great importance and extremely necessary. With that in mind, we built a new platform for drug resistance genes. We added seven drug classes of resistance genes to our database: azoles (without specifying which drug), fluconazole, voriconazole, itraconazole, flucytosine, micafungin, and caspofungin. Species with known resistance genes were used to validate the results from our database. This study describes a list of 261 candidate genes related to antifungal resistance, with several genes displaying transport functions involved in azole resistance. Over 65% of the candidate genes found were related to at least one type of azole. Overall, the candidate genes found have functional annotations consistent with genes or enzymes that have been linked to antifungal resistance in previous studies. Also, candidate antifungal resistance genes found exhibit functional annotations consistent with previously described resistance mechanisms. The existence of an HMM profile focusing on antifungal resistance genes allows in silico searches for candidate genes, helping future wet lab experiments, and hence, reducing costs when studying candidate antifungal genes without prior knowledge of the species or genes. Finally, ResFungi has proven to be a powerful tool to narrow down candidate antifungal-related genes and unravel mechanisms related to resistance to help in the design of experiments focusing on the genetic basis of antifungal resistance.