Alejandro Jimenez-Sanchez的动态

PLMSearch Approach: A method for homologous protein search that uses deep representations from a pre-trained protein language model. 1. Improved Sensitivity: Achieves over three times the sensitivity of traditional sequence search tools like MMseqs2. 2. Speed: Capable of searching millions of protein pairs within seconds. 3. Remote Homology Detection: Effectively recalls homologous proteins with dissimilar sequences but similar structures, rivaling structure-based search methods. 4. Real Structure Training Data: Trains similarity models using large datasets of real structural similarity. webserver: https://lnkd.in/gkTzinXg https://lnkd.in/g7mAyZWa

This paper introduces PLTNUM, an #AI model for predicting #protein half-lives. It leverages protein language models ( #SaProt which is based on #FoldSeek ) and structural data to achieve superior accuracy compared to existing methods. By integrating Shapley Additive Explanations (SHAP), PLTNUM identifies factors like cysteine-containing domains and intrinsically disordered regions as contributors to shorter protein half-lives. While PLTNUM focuses on sequence-dependent factors, the study emphasizes the importance of considering biological context for further improving protein half-life predictions #structurabiology #ProteinStability #Bioinformatics #ComputationalBiology

How is this for Collapsing Time –?Take approximately 3.2 billion years of evolutionary biology that has successfully produced encoded amino acids into functioning, cooperating proteins, and life on earth. Here is a demonstration of a large language model (LLM) trained at scale on evolutionary data, that can generate recipes for functional proteins and completely novel proteins as well.? Collapsing Time: +3 Billion Years to ~60 Seconds. #Science #LLM #Simulation #Proteins #Biology #SyntheticBiology #CollapseTime URL:

Do you have proteomics data in need of differential expression analysis? In this video, we'll walk you through an entire pipeline for proteomics differential expression analysis that covers data preparation, normalization, imputation, finding significant proteins, and comprehensive data visualization. In this video, you'll learn how to: Prepare Your Data: Filter for contamination and missing values. Normalize data and handle imputation. Ensure unique protein and gene names. Customize the pipeline to use your dataset. Maintain a similar structure to the sample data (e.g., protein IDs, gene names, LFQ intensities). Read experimental design from a local file with necessary columns (Label, Condition, Replicate). Perform Differential Enrichment Analysis: Define significant proteins using thresholds for adjusted p-value and log2 fold change. Visualize Your Data: Create PCA plots, correlation heat maps, volcano plots, and bar plots. Understand the significance of within-sample and between-sample variances. Generate clustered heat maps for comprehensive data visualization. Export Results: Generate result tables. Save and export your data for further analysis. Link to CompuFlair Pipeline Search Engine & AI Assistant: www.compu-flair.com Don't forget to like, comment, and subscribe for more bioinformatics tutorials! #Bioinformatics #DataAnalysis #RProgramming #DifferentialEnrichment #DataVisualization #Tutorial

IndustryARC? updated the market research study on “???????????????????????????? ????????????” Forecast (2024-2032) ?????? ???????? ???????????? https://lnkd.in/geH_PURf ???????? ?????? ???????? ?????? ???????????????? ???????? ?????? ???????????????????????????? ???????????? The recording, annotation, storage, analysis, and retrieval of nucleic acid sequence, protein sequence, and structural information are all covered by bioinformatics. Proteomics is the study of proteomes on a vast scale. A proteome is a collection of proteins made by a living creature, system, or biological milieu. Single nucleotide polymorphisms (SNPs) is a useful tool for determining the genetic basis of disease. In genome-wide association studies and fine-scale genetic mapping initiatives, these variants can be utilized as markers. Bioinformatics applications include molecular therapeutics, metabolomics, and proteomics, to name a few. It's also used in genetics and genomics research. The combination of biology and information technology is referred to as "bioinformatics." Computer software tools are used in bioinformatics to create, administer, and develop databases. Data warehousing, data mining, and communication networking all use it. ???????????????? ???????????????? ?????????????????https://lnkd.in/gCnt5iCV ?????? ??????????????: Illumina, Thermo Fisher Scientific, Agilent Technologies, QIAGEN, PerkinElmer, Bruker, IBM, Biomatters2u, Genedata, Gene Codes Corporation ? #Bioinformatics #Genomics #ComputationalBiology #BigData #DataScience #NextGenSequencing #Proteomics #GeneticResearch #MolecularBiology #PrecisionMedicine

?? Since we ran into technical difficulties with Dr. Gelman's Cross Roads last month, we are trying it again ?? this Wednesday, June 19 @ 9AM JST ?? Be sure to join us to see Dr. Sam G.'s new machine learning model METL that predicts protein function based on its amino acid sequence, while also leveraging protein biophysics and molecular mechanisms ???? ?? Join us on YouTube Live here: https://lnkd.in/gC4DdAEC ---------------------------- Just as words combine to form sentences that convey meaning in human languages, the specific arrangement of amino acids in proteins can be viewed as an information-rich language describing molecular structure and behavior. Protein language models harness advances in natural language processing to decode intricate patterns and relationships within protein sequences. These models learn meaningful, low-dimensional representations that capture the semantic organization of protein space and have broad utility in protein engineering. However, while protein language models are powerful, they do not take advantage of the extensive knowledge of protein biophysics and molecular mechanisms acquired over the last century. Thus, they are largely unaware of the underlying physical principles governing protein function. We introduce Mutational Effect Transfer Learning (METL), a specialized protein language model that bridges the gap between traditional biophysics-based and machine learning approaches by incorporating synthetic data from molecular simulations. We pretrain a transformer on millions of molecular simulations to capture the relationship between protein sequence, structure, energetics, and stability. We then finetune the neural network to harness these fundamental biophysical signals and apply them when predicting protein functional scores from experimental assays. METL excels in protein engineering tasks like generalizing from small training sets and extrapolating to new sequence positions. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 experimental examples. About the Speaker → https://samgelman.com Visit Cross Labs website → https://www.crosslabs.org Sponsored by → https://lnkd.in/gtQTqYVy #biophysics #proteinengineering #proteins #phds #careers #webinar #science

?? Unlocking Biological Insights with STRING ?? In the rapidly evolving field of bioinformatics, tools that enable us to visualize and analyze complex biological data are invaluable. One such powerful resource is STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). ?? What is STRING? STRING is a comprehensive database and web-based tool that provides insights into protein-protein interactions (PPIs). It integrates known and predicted interactions from various sources, including experimental data, computational predictions, and public databases. ?? Why Use STRING? Visualization: STRING allows researchers to visualize interactions in an intuitive network format, making it easier to identify key proteins and their roles in biological processes. Functional Enrichment: By analyzing networks, we can uncover functional associations and pathways, enhancing our understanding of cellular mechanisms. Cross-Species Comparison: STRING supports comparisons across different organisms, facilitating evolutionary studies and the identification of conserved interactions. ?? Applications in Research: Disease Mechanisms: Investigating how protein interactions contribute to diseases like cancer or neurodegenerative disorders. Drug Discovery: Identifying potential drug targets by understanding interaction networks. Genomic Studies: Linking gene expression data to protein functions and interactions. ?? Getting Started: To utilize STRING, simply input your gene or protein of interest, and explore the visualized network of interactions. You can also download data for further analysis in your preferred bioinformatics tools. As we continue to unravel the complexities of biology, tools like STRING empower us to make meaningful discoveries and drive innovation in research. ?? Check out STRING here: STRING Database #Bioinformatics #STRING #Proteomics #DataVisualization #ResearchInnovation #ScienceCommunication#Mohamed_Alemam

???Today we explore one promising solution to the current key challenge in protein structure modeling: the incorporation of non-protein molecules. ? This capability is crucial for fields like lipid studies and drug design, where modeling protein-molecule interactions is essential but remains a gap in current models. Different approaches have been developed to address this issue, but today we dive into a study from EPFL who developed a novel deep learning approach. ? Check out our blog for a detailed exploration of this study and a comprehensive look at the current challenges in computational protein design. https://lnkd.in/dsDZTMjr

Scientists from Google DeepMind Introduced #ProtEx: A new retrieval-augmented approach that maps protein sequences to their biological functions with unprecedented accuracy. A major advance for understanding protein behavior! ?? ?? What makes ProtEx special? It learns from exemplars in existing databases & uses a novel multi-sequence pretraining approach. This helps it accurately predict EC numbers, GO terms & Pfam families - even for protein classes it hasn't seen before! ?? Most exciting: ProtEx shows remarkable performance with rare & underrepresented protein sequences less well represented in the training data. This could accelerate research across multiple areas of biology, from drug discovery to enzyme engineering. Quick Read: https://lnkd.in/gA9fFjUX #Bioinfrmatics #MachineLearning #AI #Proteomics #Biology #ScienceNews

Make sure to read this comprehensive article by Izabela Ninu that combines photosynthesis and gene editing in our Biology blog below!