Deep Learning in Virtual Screening: Recent Applications and Developments.
Ontology highlight
ABSTRACT:
SUBMITTER: Kimber TB
PROVIDER: S-EPMC8123040 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Olefin metathesis is one of the most powerful C-C double-bond-forming reactions. Metathesis reactions have had a tremendous impact in organic synthesis, enabling a variety of applications in polymer chemistry, drug discovery and chemical biology. Although challenging, the possibility to perform aqueous metatheses has become an attractive alternative, not only because water is a more sustainable medium, but also to exploit biocompatible conditions. This review focuses on the progress made in aqueous olefin metatheses and their applications in chemical biology.
| S-EPMC6404410 | biostudies-literature
Project description:Oligonucleotide-based agents have the potential to treat or cure almost any disease, and are one of the key therapeutic drug classes of the future. Bioconjugated oligonucleotides, a subset of this class, are emerging from basic research and being successfully translated to the clinic. In this Review, we first briefly describe two approaches for inhibiting specific genes using oligonucleotides-antisense DNA (ASO) and RNA interference (RNAi)-followed by a discussion on delivery to cells. We then summarize and analyze recent developments in bioconjugated oligonucleotides including those possessing GalNAc, cell penetrating peptides, α-tocopherol, aptamers, antibodies, cholesterol, squalene, fatty acids, or nucleolipids. These novel conjugates provide a means to enhance tissue targeting, cell internalization, endosomal escape, target binding specificity, resistance to nucleases, and more. We next describe those bioconjugated oligonucleotides approved for patient use or in clinical trials. Finally, we summarize the state of the field, describe current limitations, and discuss future prospects. Bioconjugation chemistry is at the centerpiece of this therapeutic oligonucleotide revolution, and significant opportunities exist for development of new modification chemistries, for mechanistic studies at the chemical-biology interface, and for translating such agents to the clinic.
| S-EPMC6766081 | biostudies-literature
Project description:Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.
| S-EPMC4443793 | biostudies-literature
Project description:Microelectromechanical systems (MEMS) are of high interest for recent electronic applications. Their applications range from medicine to measurement technology, from microfluidics to the Internet of Things (IoT). In many cases, MEMS elements serve as sensors or actuators, e.g., in recent mobile phones, but also in future autonomously driving cars. Most MEMS elements are based on silicon, which is not deformed plastically under a load, as opposed to metals. While highly sophisticated solutions were already found for diverse MEMS sensors, actuators, and other elements, MEMS fabrication is less standardized than pure microelectronics, which sometimes blocks new ideas. One of the possibilities to overcome this problem may be the 3D printing approach. While most 3D printing technologies do not offer sufficient resolution for MEMS production, and many of the common 3D printing materials cannot be used for this application, there are still niches in which the 3D printing of MEMS enables producing new structures and thus creating elements for new applications, or the faster and less expensive production of common systems. Here, we give an overview of the most recent developments and applications in 3D printing of MEMS.
| S-EPMC7231376 | biostudies-literature
Project description:All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.
| S-EPMC9323969 | biostudies-literature
Project description:Osteoarthritis (OA) is a common degenerative joint disease that causes disability if left untreated. The treatment of OA currently requires a proper delivery system that avoids the loss of therapeutic ingredients. Hydrogels are widely used in tissue engineering as a platform for carrying drugs and stem cells, and the anatomical environment of the limited joint cavity is suitable for hydrogel therapy. This review begins with a brief introduction to OA and hydrogels and illustrates the effects, including the analgesic effects, of hydrogel viscosupplementation on OA. Then, considering recent studies of hydrogels and OA, three main aspects, including drug delivery systems, mesenchymal stem cell entrapment, and cartilage regeneration, are described. Hydrogel delivery improves drug retention in the joint cavity, making it possible to deliver some drugs that are not suitable for traditional injection; hydrogels with characteristics similar to those of the extracellular matrix facilitate cell loading, proliferation, and migration; hydrogels can promote bone regeneration, depending on their own biochemical properties or on loaded proregenerative factors. These applications are interlinked and are often researched together.
| S-EPMC9024926 | biostudies-literature
Project description:MotivationAntibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in-vivo and in-vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders.ResultsWe demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.AvailabilityThe DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8723137 | biostudies-literature
Project description:The application of nanotechnology in nuclear medicine offers attractive therapeutic opportunities for the treatment of various diseases, including cancer. Indeed, nanoparticles-conjugated targeted alpha-particle therapy (TAT) would be ideal for localized cell killing due to high linear energy transfer and short ranges of alpha emitters. New approaches in radiolabeling are necessary because chemical radiolabeling techniques are rendered sub-optimal due to the presence of recoil energy generated by alpha decay, which causes chemical bonds to break. This review attempts to cover, in a concise fashion, various aspects of physics, radiobiology, and production of alpha emitters, as well as highlight the main problems they present, with possible new approaches to mitigate those problems. Special emphasis is placed on the strategies proposed for managing recoil energy. We will also provide an account of the recent studies in vitro and in vivo preclinical investigations of α-particle therapy delivered by various nanosystems from different materials, including inorganic nanoparticles, liposomes, and polymersomes, and some carbon-based systems are also summarized.
| S-EPMC8398190 | biostudies-literature
Project description:PURPOSE OF REVIEW:Systemic lupus erythematosus (SLE) pathogenesis is complex. Aberrancies of immune function that previously were described but not well understood are now becoming better characterized, in part through recognition of monogenic cases of lupus-like disease. RECENT FINDINGS:We highlight here recent descriptions of metabolic dysfunction, cytokine dysregulation, signaling defects, and DNA damage pathways in SLE. Specifically, we review the effects of signaling abnormalities in mammalian target of rapamycin, Rho kinase, Bruton's tyrosine kinase, and Ras pathways. The importance of DNA damage sensing and repair pathways, and their influence on the overproduction of type I interferon in SLE are also reviewed. SUMMARY:Recent findings in SLE pathogenesis expand on previous understandings of broad immune dysfunction. These findings have clinical applications, as the dysregulated pathways described here can be targeted by existing and preclinical therapies.
| S-EPMC6050980 | biostudies-literature
Project description:The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
| S-EPMC8199773 | biostudies-literature