Large-scale synapse-level neuronal wiring diagrams in silico and in vitro

Loss-of-function long QT (LQT) mutations inducing LQT1 and LQT2 syndromes have been successfully translated to human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) used as disease-specific models. However, their in vitro investigation mainly relies on experiments using small numbers of cells. This is especially critical when working with cells as heterogeneous as hiPSC-CMs. We aim (i) to investigate in silico the ionic mechanisms underlying LQT1 and LQT2 hiPSC-CM phenotypic variability, and (ii) to enable massive in silico drug tests on mutant hiPSC-CMs. We combined (i) data of control and mutant slow and rapid delayed rectifying K+ currents, IKr and IKs respectively, (ii) a recent in silico hiPSC-CM model, and (iii) the population of models paradigm to generate control and mutant populations for LQT1 and LQT2 cardiomyocytes. Our four populations contain from 1008 to 3584 models. In line with the experimental in vitro data, mutant in silico hiPSC-CMs showed prolonged action potential (AP) duration (LQT1: +14%, LQT2: +39%) and large electrophysiological variability. Finally, the mutant populations were split into normal-like hiPSC-CMs (with action potential duration similar to control) and at risk hiPSC-CMs (with clearly prolonged action potential duration). At risk mutant hiPSC-CMs carried higher expression of L-type Ca2+, lower expression of IKr and increased sensitivity to quinidine as compared to mutant normal-like hiPSC-CMs, resulting in AP abnormalities. In conclusion, we were able to reproduce the two most common LQT syndromes with large-scale simulations, which enable investigating biophysical mechanisms difficult to assess in vitro, e.g., how variations of ion current expressions in a physiological range can impact on AP properties of mutant hiPSC-CMs.

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Flexibility and mobility of SARS-CoV-2-related protein structures

Scientific Reports ◽

10.1038/s41598-021-82849-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rudolf A. Römer ◽

Navodya S. Römer ◽

A. Katrine Wallis

Keyword(s):

In Silico ◽

Drug Targets ◽

Large Scale ◽

Structural Information ◽

Protein Structures ◽

Antiviral Drug ◽

Docking Studies ◽

Related Protein

AbstractThe worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of possible SARS-CoV-2 drug targets, as deposited on the Protein Databank (PDB), and ascertain their dynamics, flexibility and rigidity. For example, for the SARS-CoV-2 spike protein—using its complete homo-trimer configuration with 2905 residues—our method identifies a large-scale opening and closing of the S1 subunit through movement of the S$${}^\text{B}$$ B domain. We compute the full structural information of this process, allowing for docking studies with possible drug structures. In a dedicated database, we present similarly detailed results for the further, nearly 300, thus far resolved SARS-CoV-2-related protein structures in the PDB.

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Leishmania spp Epitopes in Humans Naturally Resistant to the Disease: Working Toward a Synthetic Vaccine

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.631019 ◽

2021 ◽

Vol 11 ◽

Author(s):

Magda Melissa Flórez ◽

Rocío Rodríguez ◽

José Antonio Cabrera ◽

Sara M. Robledo ◽

Gabriela Delgado

Keyword(s):

Immune Response ◽

In Silico ◽

Large Scale ◽

Mononuclear Cells ◽

Clinical Symptoms ◽

Epitope Prediction ◽

Effective Vaccine ◽

Peripheral Blood Mononuclear

Vaccines are one of the most effective strategies to fight infectious diseases. Reverse vaccinology strategies provide tools to perform in silico screening and a rational selection of potential candidates on a large scale before reaching in vitro and in vivo evaluations. Leishmania infection in humans produces clinical symptoms in some individuals, while another part of the population is naturally resistant (asymptomatic course) to the disease, and therefore their immune response controls parasite replication. By the identification of epitopes directly in humans, especially in those resistant to the disease, the probabilities of designing an effective vaccine are higher. The aim of this work was the identification of Leishmania epitopes in resistant humans. To achieve that, 11 peptide sequences (from Leishmania antigenic proteins) were selected using epitope prediction tools, and then, peripheral blood mononuclear cells (PBMCs) were isolated from human volunteers who were previously divided into four clinical groups: susceptible, resistant, exposed and not exposed to the parasite. The induction of inflammatory cytokines and lymphoproliferation was assessed using monocyte-derived dendritic cells (moDCs) as antigen-presenting cells (APCs). The response was evaluated after exposing volunteers’ cells to each peptide. As a result, we learned that STI41 and STI46 peptides induced IL-8 and IL-12 in moDCs and lymphoproliferation and low levels of IL-10 in lymphocytes differentially in resistant volunteers, similar behavior to that observed in those individuals to L. panamensis lysate antigens. We conclude that, in silico analysis allowed for the identification of natural Leishmania epitopes in humans, and also STI41 and STI46 peptides could be epitopes that lead to a cellular immune response directed at parasite control.

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Data processing algorithms for the in silico SARS-CoV-2 epitope prediction and vaccine development

Artificial Intelligence ◽

10.15407/jai2021.02.008 ◽

2021 ◽

Vol 26 (jai2021.26(2)) ◽

pp. 08-13

Author(s):

Sprindzuk M ◽

◽

Vladyko A ◽

Titov L ◽

◽

...

Keyword(s):

Data Processing ◽

In Silico ◽

Large Scale ◽

Vaccine Development ◽

Vaccine Adjuvants ◽

Prevention Measures ◽

Research Experience ◽

Processing Algorithms ◽

Data Processing Algorithms

Based on literature analysis and own bioinformatics and virology research experience, authors propose multistep data processing algorithms, designed for the objectives of assisting the SARS-CoV-2 epitope vaccine production. Epitope vaccines are expected to provoke a weaker but safer response of the vaccinated person. Methodologies of reverse bioengineering, vaccinology and synthetic peptide manufacturing have a promising future to combat COVID-19 brutal disease. The significant mutational variability and evolution of the SARS-CoV-2, which is more typical for natural animal-borne viruses, are the hurdle for the effective and robust vaccine application and therefore require multidisciplinary research and prevention measures on the international level of cooperation. However, we can expect that other viruses with different nature and content may be labelled as SARS-CoV-2. In this case metagenomics is an important discipline for COVID-19 discovery. High quality reliable virus detection is still an unresolved question for improvement and optimization. It is of upmost importance to develop the in silico and in vitro methods for the vaccine recipient reaction prediction and monitoring as techniques of the so-called modern personalized medicine. Many questions can`t be solved applying exclusively in silico techniques and only can be discovered in vitro and in vivo, demanding significant time and money investments. Future experiments also should be directed at the discovery of optimal vaccine adjuvants, vectors and epitope ensembles, as well as the personal characteristics of citizens of a certain region. This research would require several more years of meticulous large-scale laboratory and clinical work in various centers of biomedical institutions worldwide

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Computational drug repositioning of bortezomib to reverse metastatic effect ofGALNT14in lung cancer

10.1101/394163 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ok-Seon Kwon ◽

Haeseung Lee ◽

Hyeon-Joon Kong ◽

Ji Eun Park ◽

Wooin Lee ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

In Silico ◽

Large Scale ◽

Cancer Metastasis ◽

Drug Repositioning ◽

Expression Patterns ◽

Lung Cancer Metastasis

AbstractAlthough many molecular targets for cancer therapy have been discovered, they often show poor druggability, which is a major obstacle to develop targeted drugs. As an alternative route to drug discovery, we adopted anin silicodrug repositioning (in silicoDR) approach based on large-scale gene expression signatures, with the goal of identifying inhibitors of lung cancer metastasis. Our analysis of clinicogenomic data identified GALNT14, an enzyme involved in O-linked N-acetyl galactosamine glycosylation, as a putative driver of lung cancer metastasis leading to poor survival. To overcome the poor druggability of GALNT14, we leveraged Connectivity Map approach, anin silicoscreening for drugs that are likely to revert the metastatic expression patterns. It leads to identification of bortezomib (BTZ) as a potent metastatic inhibitor, bypassing direct inhibition of poorly druggable target, GALNT14. The anti-metastatic effect of BTZ was verifiedin vitroandin vivo. Notably, both BTZ treatment andGALNT14knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes, suggesting that BTZ acts by modulating TGFβ signalingTaken together, these results demonstrate that ourin silicoDR approach is a viable strategy to identify a candidate drug for undruggable targets, and to uncover its underlying mechanisms.

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Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

10.1101/2020.11.23.389676 ◽

2020 ◽

Author(s):

Chayaporn Suphavilai ◽

Shumei Chia ◽

Ankur Sharma ◽

Lorna Tu ◽

Rafael Peres Da Silva ◽

...

Keyword(s):

Single Cell ◽

Recommender System ◽

In Silico ◽

Large Scale ◽

Drug Response ◽

Response Prediction ◽

Scale Analysis ◽

Rna Seq ◽

Large Scale Analysis

SummaryWhile understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies.Graphical abstractHighlightsLarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomesCalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc)Prediction of drug response in patient-derived cell lines with transcriptomic heterogeneityIn silico identification of drug combinations that work based on clonal vulnerabilities

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Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit

Biomolecules ◽

10.3390/biom10111570 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1570

Author(s):

Ghita Ghislat ◽

Taufiq Rahman ◽

Pedro J. Ballester

Keyword(s):

Carbonic Anhydrase ◽

In Silico ◽

Large Scale ◽

Target Prediction ◽

Cost Effective ◽

Orphan Drugs ◽

Orphan Receptor ◽

Carbonic Anhydrase Ii ◽

Fundamental Aspect

Background and purpose: Identifying the macromolecular targets of drug molecules is a fundamental aspect of drug discovery and pharmacology. Several drugs remain without known targets (orphan) despite large-scale in silico and in vitro target prediction efforts. Ligand-centric chemical-similarity-based methods for in silico target prediction have been found to be particularly powerful, but the question remains of whether they are able to discover targets for target-orphan drugs. Experimental Approach: We used one of these in silico methods to carry out a target prediction analysis for two orphan drugs: actarit and malotilate. The top target predicted for each drug was carbonic anhydrase II (CAII). Each drug was therefore quantitatively evaluated for CAII inhibition to validate these two prospective predictions. Key Results: Actarit showed in vitro concentration-dependent inhibition of CAII activity with submicromolar potency (IC50 = 422 nM) whilst no consistent inhibition was observed for malotilate. Among the other 25 targets predicted for actarit, RORγ (RAR-related orphan receptor-gamma) is promising in that it is strongly related to actarit’s indication, rheumatoid arthritis (RA). Conclusion and Implications: This study is a proof-of-concept of the utility of MolTarPred for the fast and cost-effective identification of targets of orphan drugs. Furthermore, the mechanism of action of actarit as an anti-RA agent can now be re-examined from a CAII-inhibitor perspective, given existing relationships between this target and RA. Moreover, the confirmed CAII-actarit association supports investigating the repositioning of actarit on other CAII-linked indications (e.g., hypertension, epilepsy, migraine, anemia and bone, eye and cardiac disorders).

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Modelling the impact of clot fragmentation on the microcirculation after thrombectomy

10.1101/2020.11.30.403808 ◽

2020 ◽

Author(s):

Wahbi K. El-Bouri ◽

Andrew MacGowan ◽

Tamás I. Józsa ◽

Matthew J. Gounis ◽

Stephen J. Payne

Keyword(s):

Blood Flow ◽

Ischaemic Stroke ◽

Computational Model ◽

In Silico ◽

Large Scale ◽

In Vitro Experiments ◽

Mechanical Removal ◽

The Impact ◽

The Brain

1AbstractMany ischaemic stroke patients who have a mechanical removal of their clot (thrombectomy) do not get reperfusion of tissue despite the thrombus being removed. One hypothesis for this ‘no-reperfusion’ phenomenon is micro-emboli fragmenting off the large clot during thrombectomy and occluding smaller blood vessels downstream of the clot location. This is impossible to observe in-vivo and so we here develop an in-silico model based on in-vitro experiments to model the effect of micro-emboli on brain tissue. Through in-vitro experiments we obtain, under a variety of clot consistencies and thrombectomy techniques, micro-emboli distributions post-thrombectomy. Blood flow through the microcirculation is modelled for statistically accurate voxels of brain microvasculature including penetrating arterioles and capillary beds. A novel micro-emboli algorithm, informed by the experimental data, is used to simulate the impact of micro-emboli successively entering the penetrating arterioles and the capillary bed. Scaled-up blood flow parameters – permeability and coupling coefficients – are calculated under various conditions. We find that capillary beds are more susceptible to occlusions than the penetrating arterioles with a 4x greater drop in permeability per volume of vessel occluded. Individual microvascular geometries determine robustness to micro-emboli. Hard clot fragmentation leads to larger micro-emboli and larger drops in blood flow for a given number of micro-emboli. Thrombectomy technique has a large impact on clot fragmentation and hence occlusions in the microvasculature. As such, in-silico modelling of mechanical thrombectomy predicts that clot specific factors, interventional technique, and microvascular geometry strongly influence reperfusion of the brain. Micro-emboli are likely contributory to the phenomenon of no-reperfusion following successful removal of a major clot.2Author summaryAfter an ischaemic stroke - one where a clot blocks a major artery in the brain - patients can undergo a procedure where the clot is removed mechanically with a stent - a thrombectomy. This reopens the blocked vessel, yet some patients don’t achieve blood flow returning to their tissue downstream. One hypothesis for this phenomenon is that the clot fragments into smaller clots (called micro-emboli) which block smaller vessels downstream. However, this can’t be measured in patients due to the inability of clinical imaging resolving the micro-scale. We therefore develop a computational model here, based on experimental thrombectomy data, to quantify the impact of micro-emboli on blood flow in the brain after the removal of a clot. With this model, we found that micro-emboli are a likely contributor to the no-reflow phenomenon after a thrombectomy. Individual blood vessel geometries, clot composition, and thrombectomy technique all impacted the effect of micro-emboli on blood flow and should be taken into consideration to minimise the impact of micro-emboli in the brain. Furthermore, the computational model developed here allows us to now build large-scale models of blood flow in the brain, and hence simulate stroke and the impact of micro-emboli on the entire brain.

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Flexibility and mobility of SARS-CoV-2-related protein structures

10.1101/2020.07.12.199364 ◽

2020 ◽

Author(s):

Rudolf A. Römer ◽

Navodya S. Römer ◽

A. Katrine Wallis

Keyword(s):

In Silico ◽

Drug Targets ◽

Large Scale ◽

Structural Information ◽

Protein Structures ◽

Antiviral Drug ◽

Docking Studies ◽

Related Protein

ABSTRACTThe worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of SARS-CoV-2 drug targets, i.e. SARS-CoV-2 protein structures as deposited on the Protein Databank (PDB). We study their flexibility, rigidity and mobility, an important first step in trying to ascertain their dynamics for further drug-related docking studies. We are using a recent protein flexibility modelling approach, combining protein structural rigidity with possible motion consistent with chemical bonds and sterics. For example, for the SARS-CoV-2 spike protein in the open configuration, our method identifies a possible further opening and closing of the S1 subunit through movement of SB domain. With full structural information of this process available, docking studies with possible drug structures are then possible in silico. In our study, we present full results for the more than 200 thus far published SARS-CoV-2-related protein structures in the PDB.

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Purification of the Antihemophilic Factor by Gel Filtration on Agarose

Thrombosis and Haemostasis ◽

10.1055/s-0038-1651394 ◽

1969 ◽

Vol 22 (03) ◽

pp. 577-583 ◽

Cited By ~ 3

Author(s):

M.M.P Paulssen ◽

A.C.M.G.B Wouterlood ◽

H.L.M.A Scheffers

Keyword(s):

Factor Viii ◽

Plasma Proteins ◽

Large Scale ◽

Gel Filtration ◽

Large Scale Preparation ◽

Scale Preparation ◽

Antihemophilic Factor

SummaryFactor VIII can be isolated from plasma proteins, including fibrinogen by chromatography on agarose. The best results were obtained with Sepharose 6B. Large scale preparation is also possible when cryoprecipitate is separated by chromatography. In most fractions containing factor VIII a turbidity is observed which may be due to the presence of chylomicrons.The purified factor VIII was active in vivo as well as in vitro.

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