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Computer vision helps find binding sites in drug targets

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Scientists from the iMolecule group at Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) developed BiteNet, a machine learning (ML) algorithm that helps find drug binding sites, i.e. potential drug targets, in proteins. BiteNet can analyze 1,000 protein structures in 1.5 minutes and find optimal spots for drug molecules to attach. The research was published in Communications Biology. Proteins, the molecules that control most biological processes, are typically the common targets for drugs. To produce a therapeutic effect, drugs should attach to proteins at specific sites called binding sites.


Novel machine learning algorithm helps find drug binding sites – IAM Network

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Reviewed by Emily Henderson, B.Sc.Oct 27 2020 Scientists from the iMolecule group at Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) developed BiteNet, a machine learning (ML) algorithm that helps find drug binding sites, i.e. potential drug targets, in proteins. BiteNet can analyze 1,000 protein structures in 1.5 minutes and find optimal spots for drug molecules to attach. The research was published in the Communications Biology journal. Proteins, the molecules that control most biological processes, are typically the common targets for drugs. To produce a therapeutic effect, drugs should attach to proteins at specific sites called binding sites.


Neural Network Detects Protein-Peptide Binding Sites for Faster Drug Development

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A pair of researchers from Skolkovo Institute of Science and Technology (Skoltech) has developed an efficient neural network model trained to find protein-peptide binding sites, which could significantly advance peptide-based drug discovery. The new neural network, called BiteNetPp, uses known data from the structure of proteins to predict which parts interact with peptides, biological molecules made of short amino acid chains. Peptides have been used for drugs that make up and affect protein to protein interactions in various cells that, in turn, regulate a wide range of cellular processes. Proteins are considered the basic machinery of the cells, moving around, connecting and interacting with each other, and affecting various bodily operations. The interactions between these structures have been of significant interest to the field of pharmaceuticals. However, it was not as straightforward as it seems, especially since the larger therapeutic molecules, called biologics, could not penetrate deep into the cell to directly interact with proteins, as noted in a 2020 Mini Review Article on the Molecular Biology Reports.


AI reveals nature of RNA-protein interactions

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A new computational tool developed by KAUST scientists uses artificial intelligence (AI) to infer the RNA-binding properties of proteins. The software, called NucleicNet, outperforms other algorithmic models of its kind and provides additional biological insights that could aid in drug design and development. "RNA binding is a fundamental feature of many proteins," says Jordy Homing Lam, a former research associate at KAUST and co-first author of the study. "Our structure-based computational framework can reveal the detailed RNA-binding properties of these proteins, which is important for characterizing the pathology of many diseases." Proteins routinely interface with RNA molecules as a way to control the processing and transporting of gene transcripts--and when these interactions go awry, information flow inside the cell is disrupted and disorders can arise, including cancer and neurodegenerative disease.


APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design

arXiv.org Artificial Intelligence

A drawback of these methods that perform important tasks related to methods however is that, they tend not to generalise well drug design such as receptor binding site detection, to data that does not resemble the data distribution used for small molecule docking and binding affinity training. The viability of such models therefore depend on prediction. However, these methods are usually well curated training data that translates well into real world trained on only ligand bound (or holo) conformations applications. of the protein and therefore are not guaranteed to perform well when the protein structure Deep Learning models pertaining to SBDD workflows are is in its native unbound conformation (or apo), usually trained on datasets containing 3D structures of which is usually the conformation available for protein-ligand complexes (Batool et al., 2019). PDBbind a newly identified receptor. A primary reason (Wang et al., 2005) is a predominantly used dataset that provides for this is that the local structure of the binding experimental binding affinity values for protein-ligand site usually changes upon ligand binding. To facilitate co-crystal structures present in the Protein Data Bank (PDB) solutions for this problem, we propose a (Berman et al., 2000). Deep learning architectures usually dataset called APObind that aims to provide apo use voxelized (Jiménez et al., 2018) or graph like representations conformations of proteins present in the PDBbind (Son & Kim, 2021) of the 3D structures present in dataset, a popular dataset used in drug design. Furthermore, PDBbind for computation to get benchmark performances.