ChembioAI

🚀 Advancing Viral Annotation in Environmental Samples with Protein Language Models 🌊🦠 Viral sequences are often poorly annotated in environmental samples, creating a significant barrier to understanding how viruses influence microbial ecosystems. Traditional annotation methods, which rely on sequence homology, are limited by the availability of viral sequences and the high divergence in viral proteins. In a groundbreaking study, we demonstrate how protein language models can transcend these limitations. By capturing viral protein functions beyond remote sequence homology, these models enable systematic labeling of protein families and identification of functional roles for biological discovery. 🌍🔬 The findings show that protein language models enhance the annotated fraction of ocean virome viral proteins by 37%, uncovering previously hidden protein families. One of the most exciting discoveries is a novel DNA editing protein family that defines a new mobile element in marine picocyanobacteria! 🧬🌊 This work opens new doors for viral functional annotation and biological discoveries across diverse ecosystems. It highlights the power of AI in advancing our understanding of complex microbial worlds. 🌱💡 Paper: https://lnkd.in/gihNZ-HM  Code: https://lnkd.in/gH3FF-eJ #ViralResearch #ProteinLanguageModels #Bioinformatics #MarineScience #MicrobialEcology #AIinBiology #OceanVirome #Innovation #BiologicalDiscovery #DataScience

PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening - The PepCARES framework leverages PeptideMPNN, a novel model refined from ProteinMPNN using transfer learning. PeptideMPNN demonstrates a 26.26% improvement in sequence recovery and reduces perplexity by 0.536, enhancing its efficacy in peptide design tasks. - PeptideMPNN successfully designs peptides targeting two HLA alleles. Using predictive tools like MHCfovea, it identifies potential peptide candidates, showcasing strong binding affinity and structural fidelity in experimental validation. - AlphaFold2 is employed to confirm structural integrity of the designed peptides, with PeptideMPNN’s designs showing higher confidence scores than baseline models, underscoring the potential for accurate 3D folding and binding stability. - Advanced analysis using PDBePISA calculates Gibbs free energy and surface electrostatic potential, validating PeptideMPNN’s designs for both binding affinity and structural stability. Results reveal 14 candidates with enhanced binding for HLA-A02:01 and seven for HLA-B27:05. - PepCARES provides a robust framework for peptide design, refining and evaluating peptide candidates for therapeutic and vaccine applications with a potential impact on immune response targeting and peptide-based vaccine development. Paper: https://lnkd.in/gtSXUbVK Code: https://lnkd.in/gUFt-ed5 #PeptideDesign #VaccineDevelopment #Bioinformatics #ProteinEngineering #ComputationalBiology

NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics. - NovoBench presents the first unified benchmarking suite specifically for evaluating de novo peptide sequencing models, addressing key challenges in fair comparison across datasets and evaluation metrics in proteomics. - This benchmark integrates multiple state-of-the-art models, including DeepNovo, PointNovo, CasaNovo, InstaNovo, AdaNovo, and π-HelixNovo, enabling a comprehensive and standardized assessment of each model’s performance. - NovoBench introduces novel evaluation metrics beyond standard amino acid-level and peptide-level precision and recall, such as post-translational modification (PTM) detection, efficiency, and robustness to noise, peptide length, and missing fragments. - This study reveals significant variability in model performance under different experimental conditions, highlighting the importance of selecting models based on specific dataset characteristics and application needs. 💻Code: https://lnkd.in/dRUPKzTS 📜Paper: https://lnkd.in/dxYZzKVg #DeNovoSequencing #Proteomics #MachineLearning #DeepLearning #NovoBench

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer. - CancerFoundation is a novel foundation model, exclusively trained on single-cell RNA sequencing (scRNA-seq) data from malignant cells. Despite using only 1 million cells - CancerFoundation’s focus on malignant cells enhances its precision in capturing unique transcriptional states associated with tumor heterogeneity and drug resistance, enabling it to excel in predicting responses for unseen cell lines and drugs. - The model incorporates tissue and technology-aware oversampling, which allows it to perform well on underrepresented cancer types and sequencing technologies, making it versatile across diverse cancer datasets. - CancerFoundation proposes survival prediction as a new downstream task for scRNA-seq models, bridging the gap between single-cell and bulk RNA data and proving useful for patient stratification and understanding cancer progression. 💻Code: https://lnkd.in/dAKyATV4 📜Paper: https://lnkd.in/dbgeZA3V #CancerResearch #SingleCellRNA #DrugResistance #MachineLearning #Bioinformatics #Oncology

ImmunoStruct: Integration of protein sequence, structure, and biochemical properties for immunogenicity prediction and interpretation. - ImmunoStruct is a novel deep-learning model that integrates protein sequence, 3D structure, and biochemical properties for improved prediction of peptide-MHC (pMHC) immunogenicity - The model is trained on a multimodal dataset of ~27,000 pMHC complexes generated using AlphaFold - ImmunoStruct maps complex structural relationships within pMHC complexes, providing interpretable predictions and highlighting key peptide positions influencing T-cell activation - ImmunoStruct demonstrates strong alignment with experimental immunogenicity data, accurately predicting immunogenic responses for SARS-CoV-2 epitopes with an AUC of 0.780, indicating its practical potential in infectious disease response. 📜Paper: https://lnkd.in/dntxH2H6 #Immunogenicity #DeepLearning #CancerNeoepitopes #InfectiousDisease #VaccineDevelopment #Bioinformatics

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Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. - This paper addresses the replicability crisis in ML-based small molecule drug discovery, proposing robust and domain-specific protocols - It underscores the critical need for statistically significant and practically impactful ML models to inform high-stakes drug discovery decisions - Emphasizes the importance of statistical tests (e.g., Tukey HSD) for robust method comparison, advocating for effect size assessment to determine practical, not just statistical, significance. - Proposes a novel MCSim (Multiple Comparisons Similarity) plot to visualize practical and statistical significance, assisting in comprehensive method evaluation. - By aligning performance metrics with practical impacts, the guidelines aim to bridge the gap between ML research and real-world drug discovery needs. 💻Code: https://lnkd.in/gMzpS4TZ 📜Paper: https://lnkd.in/djeQjnfy #MachineLearning #DrugDiscovery #Cheminformatics #MLBenchmarks #ReplicabilityCrisis

Research Orientation Your choice highlights the diverse paths advancing scientific discovery. Vote now! 🗳️ #ResearchPoll #InnovationInScience

Artificial intelligence for microbiology and microbiome research - This comprehensive review explores the transformative impact of AI in microbiology and microbiome research, detailing how machine learning and deep learning techniques are advancing our understanding of microbial communities. - Key AI applications include taxonomic profiling, functional annotation, and gene prediction, enhancing accuracy in identifying and categorizing microbial species from metagenomic data. - AI-driven models facilitate complex microbe-X interactions, such as microbe-host and microbe-drug associations, providing insights into microbial influence on human health, drug efficacy, and disease susceptibility. - The review emphasizes challenges like balancing model interpretability with complexity and the need for standardized benchmarks, crucial for developing reliable, generalizable AI applications in microbiology. 📜Paper: https://lnkd.in/dEAdgpkY #AI #Microbiology #Microbiome #MachineLearning #Bioinformatics

Make a Mess to Find Success! 🔬💥 Just because something doesn’t do what you planned doesn’t mean it’s useless. – Thomas Edison Edison knew that mistakes are essential for breakthroughs. AI experiments are no different—let’s embrace every failure and make it a stepping stone!