Structure-based drug discovery (SBDD) is a standard method for identifying prospective medications for a target by leveraging its structural information. AlphaFold, a technique for predicting protein structure, has been regarded as a helpful resource for the discovery of therapeutics for new targets with low or no structural knowledge. In this study, the scientists utilized AlphaFold…
Scientists from Edinburgh have solved the mystery behind the formation and variation in fingerprint patterns. Fingerprints are complex patterns, unique to every individual, and are determined before birth. The researchers from the University of Edinburgh unravel the molecular and cellular mechanisms underlying fingerprint ridge formation and their intricate arrangements. Fingerprint ridges are found to be…
Researchers from Skoltech, MIPT, the National Academy of Sciences of Belarus’ Institute of Bioorganic Chemistry (IBOCH NAS), and the Russian Academy of Sciences’ Institute of Biomedical Chemistry have studied the structures of ferredoxins from Mycobacterium tuberculosis and their complexes with interacting proteins. The study aims to discover new targets for anti-tuberculosis drug development by closely examining the…
Scientists from China have recently performed pan-3D genome analysis for soybean genomes to investigate the interplay among 3D genomic variations, structural variations (SV) as well as gene expression. Researchers applied Hi-C techniques to 27 soybean genome accessions to provide a pan-3D genome landscape of soybean and shed light on the evolutionary dynamics of 3D genomes…
Scientists at Jinhua Advanced Research Institute and Harbin University of Science and Technology have developed a deep-learning algorithm that can identify depression through speech. This algorithm was trained to recognize emotions in spoken language by looking at various pertinent traits. Millions of people all over the world go through depression, which is a common mental…
All organisms depend on protein-protein interactions (PPIs) for cellular and biological activities. This paper introduces a novel method for building peptide binders for “undruggable” protein-protein interactions. A machine learning approach generates different candidate peptides from natural binding regions from protein partners. Molecular docking and in-vitro microchip-based binding assays filter the peptides. The research confirms and…
Scientists from Barcelona have discovered a new hexameric protein structure for the RepB protein, which is involved in catalyzing replication initiation in Streptococcal plasmid pMV158, conferring antibiotic resistance to tetracycline. The new hexameric structure, obtained using biochemical methods and X-ray crystallography, is found to render flexibility, which attributes to the dual functionality of the protein,…
The POPDx model does not require extensive patient datasets, which makes it potentially useful for patients with rare diseases. The POPDx framework was created to effectively identify a wide variety of phenotypes among participants in the UK Biobank study. To provide a thorough analysis, it makes use of a wide variety of patient data, including…
The digestive system is home to trillions of bacteria, which are very different in each person based on their gender, age, race, lifestyle, and health. For precision medicine to include microbial metabolism, computer models would have to be expandable and able to resolve strains and molecules. This study introduces us to AGORA2 – assembly of…
Researchers from the Jun Qu lab at SUNY Buffalo, New York, have developed a revolutionary method for in-depth mapping of protein localization in whole tissue. The method implements the cutting-edge technique of micro-scaffold-assisted spatial proteomics (MASP). The daunting task of accurate, in-depth mapping of protein localizations at the whole-tissue level has been achieved by using…
Cancer is one of the prominent causes of death globally, and discovering new methods to prevent and cure it is important for public health. Understanding the particular nutrients that cancer cells require is one of the strategies researchers are investigating to fight the disease. Arginine is one of the important amino acids produced by our…
Scientists from UCSD have devised an efficient AI-based pipeline, RESP, for identifying high-affinity antibodies. Typically, researchers would employ directed evolution to enable iterative mutagenesis and selection to identify and assess high-affinity antibodies. Existing methods employing deep learning techniques fail to provide statistical estimates for assessing reliable predictions. RESP, as a pipeline, implements an autoencoder model…
According to Levinthal’s paradox, each protein may adopt around 10300 distinct structures. We now know 3-D structures for about 98% of the human proteome thanks to DeepMind’s AI system, AlphaFold. However, AlphaFold has limitations, such as its inability to generate coordinates for tiny molecules and ligands that are critical to the protein’s activity. Researchers from…
Scientists from Shaanxi Normal University, China, developed a novel Drug-Drug-Interactions (DDI) prediction tool that implements a dual graph neural network architecture to incorporate chemical substructure information as well as substructure interaction information of drugs. This never-before-implemented approach has the potential to identify synergistic drug combinations for COVID-19. Why do we need to predict DDI? Drug-Drug…
Most SARS-CoV-2 patients experience anosmia, or a loss of smell, which can linger for months after recovery. According to olfactory epithelium samples taken from 24 biopsies, T cell-mediated inflammation continues in the olfactory epithelium even after SARS-CoV-2 has been removed from the tissue, indicating a mechanism for long-term post-COVID-19 smell loss. The severe acute respiratory…
Controlling gene expression by engineering Cys2His2 zinc finger (ZF) domains to bind particular target sequences in the genome has many therapeutic applications. However, due to their intricate structure, designing these domains to interact with DNA is challenging. To address the problem, scientists from the University of Toronto and New York University have developed a deep…
Accurately identifying somatic mutations and differentiating them from germline variations is critical for precision oncology and precise tumor-mutational burden (TMB) computation. For this purpose, three trained machine-learning models, TabNet, XGBoost, and LightGBM, were utilized, and state-of-the-art performance was achieved, with concordance between matched-normal and tumor-only TMB improving from R2=0.006 to R2=0.73. The racial biases in…
Scientists from the University of California, San Francisco, have created an inventive AI system that can create entirely new enzymes from scratch. Even though the amino acid sequences of these synthetic enzymes differ significantly from those of any naturally occurring protein, they have demonstrated remarkable efficacy in laboratory experiments. In other words, using state-of-the-art technology,…
There has been an increase in research in numerous areas of biology and medicine due to the development of mass spectrometry lipidomics. As a result, massive amounts of raw data have been produced, which calls for cutting-edge techniques to handle automated data processing. Several software tools that meet particular needs have been developed to address…
Chemical language models (CLMs) can be used to construct molecules with desired features. Textual representations of novel chemical structures, such as simplified molecular input line entry system (SMILES) strings, are generated using CLMs. The ability of generative models to learn a training distribution of molecules determines their usefulness for designing new functional substances. Through the…
Scientists from University College London, UK, study the Machine Learning (ML) approaches that have successfully closed the ever-widening gap between large amounts of protein sequences produced by state-of-the-art sequencing methods and annotations of the structures and functions of proteins. One of the best methods for structure prediction, AlphaFold2 (AF2), has resulted in a 1,000-fold increase…
Scientists from the University of Cambridge have introduced a cutting-edge DNA sequencing method that can precisely pinpoint the location and mechanism of small molecule drugs interacting with the targeted genome. The novel technique has the potential to provide valuable insights into the drug development process and increase our understanding of the complex interactions between drugs…
Aston University scientists have made a groundbreaking discovery in the field of virology. They were able to create a 3D model of the virus with its entire genome after successfully reconstructing and validating an entire virus model using mixed-resolution cryo-electron microscopy density. The findings of the research published in Faraday Discussions provide significant implications for understanding how…
The Children’s Hospital of Philadelphia (CHOP) scientists have created ESPRESSO, a new computational tool for analyzing RNA-seq data. ESPRESSO aims to improve the detection and quantification of transcript isoforms, which are variations of a gene that can serve different functions. ESPRESSO is particularly useful when dealing with long-read RNA-seq data, which can be prone to…
Owkin, an AI biotech company, has successfully used federated learning (FL) for the first time to train deep learning models on histopathology data from multiple hospitals without the need for the data to be transferred outside of hospital security systems. This groundbreaking achievement was reported in a study published in Nature Medicine. The unveiling of this…
According to a recent study from Karl Landsteiner University of Health Sciences (KL Krems), using radiomics and deep learning algorithms can quickly and accurately distinguish between glioblastoma (primary tumors) and brain metastases. The study, which was published in Metabolites, discovered that magnetic resonance-based radiological data of tumor oxygen metabolism provide a solid foundation for discrimination via…
Scientists from Case Western Reserve University, Cleveland, USA, developed a self-supervised image representation learning framework, “ImageMol,” for predicting molecular properties and drug targets with high accuracy, making it a valuable tool for computational drug discovery. Imagine we belong to the incredibly lazy bunch that uses a pre-made pizza crust that would probably make every Italian…
BioNTech, a leading biopharmaceutical company, has announced plans to fully acquire InstaDeep, an artificial intelligence company specializing in drug discovery, design, and development. This acquisition will allow BioNTech to cement its position as a leader in AI-powered drug development. The two companies hope to drive innovation and improve patient care by combining BioNTech’s expertise in…
Researchers from MIT have created a comprehensive map of over 300 protein kinases and their targets, with the goal of potentially discovering new cancer treatments. Protein kinases are a vital class of enzymes that play a crucial role in the human body. These signaling molecules control a wide range of cellular activities, including growth, cell…
Lung cancer is one of the most common and deadly types of cancer worldwide, and the ability of cancer cells to evade the body’s immune system poses a significant challenge in treating it. A team of Weill Cornell Medicine scientists recently made a significant breakthrough in understanding this process. They discovered a genetic signature that can predict patient survival and identified a key mechanism lung cancer cells use to avoid immune attacks. This groundbreaking discovery could hasten the development of treatments that can circumvent tumor defense mechanisms, increasing the chances of survival for lung cancer patients. The signaling pathway involving IRE1 and XBP1 is becoming increasingly recognized as a key player in cancer progression and immune system suppression in various types of cancer. The study, which was published in the journal Nature Communications, involved the analysis of human lung cancer datasets as well as experiments on preclinical models, and it…
Scientists from Carnegie Mellon University developed SPICEMIX, a machine learning method that allows researchers to better understand the contribution of various spatial patterns to gene expression in complex tissues such as the brain. The study reporting the SPICEMIX method was featured as the main story in the latest edition of Nature Genetics. Genes that are activated…
A recent study utilized digital twin technology to identify new markers for atherosclerotic coronary artery disease (ASCAD). The study was conducted by G3 Therapeutics, a specialist in multi-omics, and Aitia, an AI company with Gemini Digital Twin technology. The study indicates that triglyceride-rich LDL particles may be a unique biomarker for ASCAD and may also…
When it comes to developing new drugs, one of the most important considerations is how well a candidate drug will bind to its target protein. This binding ability, also known as affinity, plays a critical role in determining the efficacy of a drug. In the past, determining the binding affinity of a drug candidate was…
The ability to predict cancer has advanced with the recent release of a new database that includes information on cancer proteins and was developed using artificial intelligence and machine learning techniques. Professor Mathias Uhlén from the KTH Royal Institute of Technology revealed the launch of the Disease Blood Atlas, which offers a pioneering map displaying…
Scientists at the University of Toronto advanced drug formulation development by utilizing machine learning techniques to predict and guide the design of long-acting injectable drugs. Through the use of these algorithms, the scientists were able to predict the release of drugs from advanced delivery systems and use this information to inform the design of new…
Finding cancer subtypes is a crucial first step in creating individualized care. In particular, RNA splicing change-based sub-typing has been encouraged by a number of recent studies. An unsupervised technique called CHESSBOARD was developed by scientists in the USA specifically for RNA splicing data. It identifies “tiles” in the data that are identified by a…
The mechanical properties of DNA are affected by various processes, including local sequence and epigenetic modifications. It has been challenging to understand the relationship between these modifications and DNA’s mechanical properties because of a lack of efficient experimental methods. Durham University researchers, in collaboration with researchers from Johns Hopkins University, Barcelona Institute of Science and…
The relationship between Body Mass Index (BMI) and several health issues that have an influence on morbidity and mortality has been the focus of research by epidemiologists and scientists with roots in China and the USA. The possibility to further our understanding of the breadth and depth of illnesses connected to obesity exists in the…
The Bioinformatics and Genomics Group at the University of Barcelona has developed “RGT” (Regulatory Genomics Toolbox), a powerful tool for the integrative analysis of high throughput regulatory genomics data. RGT is a comprehensive and user-friendly software package that allows researchers to analyze and interpret complex data sets. The integration and analysis of high-throughput regulatory genomics…
DNA polymerase θ is an enzyme that plays a crucial role in the process of repairing broken strands of DNA. In a recent study, researchers at Rice University and St. Jude Children’s Research Hospital in Memphis, Tennessee, analyzed the structure of this enzyme in order to understand its function better and how it might be…
Biomolecules are molecules produced by living organisms and are essential for the proper functioning of an organism’s cells and tissues. They include molecules such as carbohydrates, lipids, nucleic acids, and proteins. What are the Types of Biomolecules? There are four main types of biomolecules: carbohydrates, lipids, nucleic acids, and proteins. Present like a Pro! ExpertSlides…
Multiple omics technologies may be used in biological cohorts to identify patient-level disease features and unique treatment responses. The size and heterogeneity of the multi-modal data, however, constitute integrating and inference a challenging challenge. Multi-omics variational autoencoders (MOVE), a deep-learning-based framework created by researchers from the University of Copenhagen and Copenhagen Research Center, was used…
Scientists from the University of California San Diego School of Medicine and Rady Children’s Institute for Genomic Medicine have developed a method “DeepMosaic” for identifying mosaic mutations using deep learning. The process involves training a model to analyze large amounts of genomic data and recognize patterns associated with mosaic mutations. The researchers hope that this…
Saitama University, Japan, researchers developed a powerful method for observing the time-resolved behavior of biomolecules through high-speed atomic force microscopy (HS-AFM), thus improving the approach for estimating structural information from AFM data. Deciphering dynamics of biomolecules The surface geometry of a sample molecule is the only structural information present in HS-AFM pictures, though. It is…
The OpenGenomeBrowser, developed by researchers from the University of Bern, is a web platform that allows researchers to manage and analyze microbial genome data, including DNA sequences, annotations, and variation data. It is versatile, able to handle a range of data types, dataset-independent, able to work with any genome data regardless of source or format,…
Researchers at the School of Informatics, Xiamen University, China, and Computational Health Informatics Program, Boston Children’s Hospital, USA developed the GCNCPR-ACPs method to effectively capture different levels of node attributes for amino acid node representation learning. GCNCPR-ACPs (a Graph Convolution Neural Network Method is a new machine learning-based method based on collapse pooling and residual…
RNA (ribonucleic acid) is a molecule that plays a variety of important roles in the body. It is a single-stranded nucleic acid that is similar to DNA, but has a few key differences. While DNA is the genetic material that carries information from one generation to the next, RNA is involved in the translation of…
Scientists from the University of Oslo, Norway, presented a brand-new Python module called BioNumPy that builds a layer on top of NumPy to make it possible to program intuitively with arrays on biological datasets. The goal of BioNumPy is to make it simple to read popular bioinformatics file formats into NumPy-like data structures for quick operations…
Researchers at Drexel University’s School of Biomedical Engineering, Science, and Health Systems have found that ChatGPT, an AI-powered chatbot program, may be able to detect early signs of Alzheimer’s disease. In a study, the program analyzed conversations between individuals with Alzheimer’s disease and healthy controls and was able to accurately identify those with the disease…
To understand how living systems operate and develop new medications, it is essential to identify protein functions accurately. Researchers from the USA propose a new model, ATGO, for predicting Gene Ontology (GO) attributes of proteins using a triplet neural network that is enhanced with unsupervised language models trained on protein sequences. The integration of the…
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