1,758 research outputs found
De novo drug design through artificial intelligence: an introduction
Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery
The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes
The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved.
The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity.
This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted.
The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer
Potential Alphavirus Inhibitors From Phytocompounds – Molecular Docking and Dynamics Based Approach
Background. Alphaviral diseases are an economic burden all over the world due to their chronicity and distribution worldwide. The glycoproteins E1 and E2 are important for binding to the surface of the host cell by interacting with the receptors and non-structural proteins named nsP2 and nsP4 are important for the replication of virus, so can be an important drug discovery target.
Objective. We are aimed to explore the in silico interaction between plant-based compounds (phytocompounds) and specific protein targets, such as nonstructural protein nsP4 and glycoprotein E2 of Sindbis virus (SINV), nsP2 and E2 of Chikungunya virus (CHIKV), and glycoproteins E1 and E2 of Ross River virus (RRV).
Methods. A library of phytochemicals from Indian medicinal plants was prepared using databases and converted to 3D structures. Protein structures (nsP2, nsp4, E1, E2) were obtained and refined, followed by molecular docking with AutoDock Vina. Promising ligands were evaluated for properties, cytotoxicity, and mutagenicity, considering drug-likeness and potential issues. Molecular Dynamics simulations assessed complex stability.
Results. We analyzed 375 phytocompounds against these targets using molecular docking, modeling, and molecular dynamics for SINV, CHIKV, and Ross River (RRV) virus proteins. Granatin A has been found to successfully bind to the target sites of SINV nsP4, CHIKV E2, and CHIKV nsP2 with binding affinity values of -16.2, -20.6, and -18.6 Kcal/mol respectively. Further, stability of CHIKV E2 – Granatin A complex was done by performing molecular dynamic simulation and the complex was stable at 60ps.
Conclusions. This research provides valuable insights into the development of effective antiviral drugs against alphaviruses, emphasizing the importance of natural compounds and their interactions with viral proteins. This study might pave the way for further exploration of these small molecules as effective anti-alphaviral therapeutic agents
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning
Protein-DNA interaction is critical for life activities such as replication,
transcription, and splicing. Identifying protein-DNA binding residues is
essential for modeling their interaction and downstream studies. However,
developing accurate and efficient computational methods for this task remains
challenging. Improvements in this area have the potential to drive novel
applications in biotechnology and drug design. In this study, we propose a
novel approach called CLAPE, which combines a pre-trained protein language
model and the contrastive learning method to predict DNA binding residues. We
trained the CLAPE-DB model on the protein-DNA binding sites dataset and
evaluated the model performance and generalization ability through various
experiments. The results showed that the AUC values of the CLAPE-DB model on
the two benchmark datasets reached 0.871 and 0.881, respectively, indicating
superior performance compared to other existing models. CLAPE-DB showed better
generalization ability and was specific to DNA-binding sites. In addition, we
trained CLAPE on different protein-ligand binding sites datasets, demonstrating
that CLAPE is a general framework for binding sites prediction. To facilitate
the scientific community, the benchmark datasets and codes are freely available
at https://github.com/YAndrewL/clape
The Photosensitizer Temoporfin (mTHPC) – Chemical, Pre‐clinical and Clinical Developments in the Last Decade†‡
This review follows the research, development and clinical applications of the photosensitizer 5,10,15,20‐tetra(m‐hydroxyphenyl)chlorin (mTHPC, temoporfin) in photodynamic (cancer) therapy (PDT) and other medical applications. Temoporfin is the active substance in the medicinal product Foscan® authorized in the EU for the palliative treatment of head and neck cancer. Chemistry, biochemistry and pharmacology, as well as clinical and other applications of temoporfin are addressed, including the extensive work that has been done on formulation development including liposomal formulations. The literature has been covered from 2009 to early 2022, thereby connecting it to the previous extensive review on this photosensitizer published in this journal [Senge, M. O. and J. C. Brandt (2011) Photochem. Photobiol. 87, 1240–1296] which followed its way from initial development to approval and clinical application
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Synuclein plasticity: the Achilles’ heel of nerve function linked to the onset of Parkinson’s disease
Lewy bodies – the hallmarks of Parkinson’s disease – are majorly constituted of aggregates of the presynaptic protein alpha-synuclein. The molecular mechanism of alpha-synuclein aggregation through which it changes dramatically from a soluble disordered monomer to insoluble structured fibrils remains unknown. As an intrinsically disordered protein, alpha-synuclein does not have a specific three-dimensional structure, but rather behaves mostly as a meta-stable ensemble of highly dynamic conformers, and as such undergoes rapid kinetics, making it almost impossible to measure its conformational changes with most techniques. Millisecond amide hydrogen exchange can provide valuable insights on the dynamic behaviour of proteins, especially at flexible regions. Thus, the work in this thesis reports on the development of methods and tools for hydrogen/deuterium-exchange mass spectrometry (HDX-MS) and the application of these for the study of aSyn under physiological conditions. In the first part of this thesis, high resolution on the alpha-synuclein monomer was achieved over two dimensions: time and space. Using a novel in-house rapid- mixing quench-flow instrument, hydrogen/deuterium-exchange mass spectrometry data on alpha-synuclein on the millisecond timescale was attained. Furthermore, using a ‘soft’ gas-phase mass spectrometry fragmentation technique called Electron Transfer Dissociation, structural resolution in the protein increased. The second part of this work focuses on the development of a software, HDfleX, in an effort to primarily automate the HDX-MS workflow and allow the merging of HDX-MS data at different levels: bottom-up, middle-down and top-down. The rest of the thesis uses the tools and methods developed earlier on to explore the effects of different solution conditions (cellular compartments and salt cations) on the monomeric conformations of aSyn, and how these correlate to the different stages of aggregation and the ensuing fibril polymorphs. Altogether, the achievements in this work will allow us to better understand the plasticity of the alpha-synuclein monomer as it cycles through different local environments
Multi-Fidelity Bayesian Optimization for Efficient Materials Design
Materials design is a process of identifying compositions and structures to achieve
desirable properties. Usually, costly experiments or simulations are required to evaluate
the objective function for a design solution. Therefore, one of the major challenges is how
to reduce the cost associated with sampling and evaluating the objective. Bayesian
optimization is a new global optimization method which can increase the sampling
efficiency with the guidance of the surrogate of the objective. In this work, a new
acquisition function, called consequential improvement, is proposed for simultaneous
selection of the solution and fidelity level of sampling. With the new acquisition function,
the subsequent iteration is considered for potential selections at low-fidelity levels, because
evaluations at the highest fidelity level are usually required to provide reliable objective
values. To reduce the number of samples required to train the surrogate for molecular
design, a new recursive hierarchical similarity metric is proposed. The new similarity
metric quantifies the differences between molecules at multiple levels of hierarchy
simultaneously based on the connections between multiscale descriptions of the structures.
The new methodologies are demonstrated with simulation-based design of materials and
structures based on fully atomistic and coarse-grained molecular dynamics simulations,
and finite-element analysis. The new similarity metric is demonstrated in the design of
tactile sensors and biodegradable oligomers. The multi-fidelity Bayesian optimization
method is also illustrated with the multiscale design of a piezoelectric transducer by
concurrently optimizing the atomic composition of the aluminum titanium nitride ceramic
and the device’s porous microstructure at the micrometer scale.Ph.D
Quantitative approaches for decoding the specificity of the human T cell repertoire
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method’s mathematical approach, predictive performance, and limitations
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