In silico target network analysis of de novo-discovered, tick saliva-specific microRNAs reveals important combinatorial effects in their interference with vertebrate host physiology

AbstractLong-read de novo genome assembly continues to advance rapidly. However, there is a lack of effective tools to accurately evaluate the assembly results, especially for structural errors. We present Inspector, a reference-free long-read de novo assembly evaluator which faithfully reports types of errors and their precise locations. Notably, Inspector can correct the assembly errors based on consensus sequences derived from raw reads covering erroneous regions. Based on in silico and long-read assembly results from multiple long-read data and assemblers, we demonstrate that in addition to providing generic metrics, Inspector can accurately identify both large-scale and small-scale assembly errors.

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X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis

10.1101/2020.12.23.424259 ◽

2020 ◽

Author(s):

Dongyu Xue ◽

Han Zhang ◽

Dongling Xiao ◽

Yukang Gong ◽

Guohui Chuai ◽

...

Keyword(s):

Molecular Analysis ◽

In Silico ◽

Large Scale ◽

De Novo ◽

Representation Learning ◽

Training Data ◽

Fine Tuning ◽

Model Interpretation ◽

Unlabelled Data ◽

Super Computing

AbstractIn silico modelling and analysis of small molecules substantially accelerates the process of drug development. Representing and understanding molecules is the fundamental step for various in silico molecular analysis tasks. Traditionally, these molecular analysis tasks have been investigated individually and separately. In this study, we presented X-MOL, which applies large-scale pre-training technology on 1.1 billion molecules for molecular understanding and representation, and then, carefully designed fine-tuning was performed to accommodate diverse downstream molecular analysis tasks, including molecular property prediction, chemical reaction analysis, drug-drug interaction prediction, de novo generation of molecules and molecule optimization. As a result, X-MOL was proven to achieve state-of-the-art results on all these molecular analysis tasks with good model interpretation ability. Collectively, taking advantage of super large-scale pre-training data and super-computing power, our study practically demonstrated the utility of the idea of “mass makes miracles” in molecular representation learning and downstream in silico molecular analysis, indicating the great potential of using large-scale unlabelled data with carefully designed pre-training and fine-tuning strategies to unify existing molecular analysis tasks and substantially enhance the performance of each task.

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An In-Silico Approach for Designing a Potential Antagonistic Molecule Targeting β2-adrenoreceptor Having Therapeutic Significance

Letters in Applied NanoBioScience ◽

10.33263/lianbs101.20632069 ◽

2020 ◽

Vol 10 (2) ◽

pp. 2063-2069

Keyword(s):

Drug Discovery ◽

In Silico ◽

Active Sites ◽

De Novo ◽

Cellular Level ◽

Ligand Molecule ◽

Best Fitting ◽

Β2 Adrenergic Receptor ◽

External Stimuli ◽

G Protein Coupled

One of the largest families of membrane proteins, the G protein-coupled receptors (GPCRs) has been a very important target of drug discovery as they are involved in having a regulatory role in a variety of signaling pathways at the cellular level in response to external stimuli. Modern in-silico and crystallographic approaches have further made it easier to peep into their structures. In this study, β2 adrenergic receptor (β2AR) has been targeted, and a new ligand molecule using the de-novo approach has been proposed. Using 1-Amino-3-(2,3-dihydro-1H-indol-4-yloxy)-propan-2-ol, the best fitting binding fragments were established with a significant dissociation constant value of 5-7 nanomolar. The flexibility of specific active sites was also investigated, and it was observed that residues 114 (V), 117 (V), 203 (S), 286 (W), and 289 (F) played a crucial role in accommodating ligand for the best binding. Upon examination of the bioavailability parameters, the ligand var9 exhibited significant inhibitory characteristics having lower toxicity values and high drug likeliness properties. Findings certainly hold significance in terms of targeting GPCRs in getting insight into structure-based drug designing and drug discovery.

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In silico identification of vaccine targets for 2019-nCoV

F1000Research ◽

10.12688/f1000research.22507.1 ◽

2020 ◽

Vol 9 ◽

pp. 145 ◽

Cited By ~ 7

Author(s):

Chloe Hyun-Jung Lee ◽

Hashem Koohy

Keyword(s):

T Cell ◽

In Silico ◽

Vaccine Development ◽

De Novo ◽

Cell Receptor ◽

Binding Potential ◽

Hla Alleles ◽

Immunogenic Peptides ◽

Novel Coronavirus ◽

Positional Weight

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

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De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2018.00406 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 8

Author(s):

Aitana Alonso-Gonzalez ◽

Cristina Rodriguez-Fontenla ◽

Angel Carracedo

Keyword(s):

Autism Spectrum Disorder ◽

Network Analysis ◽

De Novo ◽

Autism Spectrum ◽

Spectrum Disorder ◽

De Novo Mutations

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Ficus benghalensis as Potential Inhibitor of 5α-Reductase for Hair Growth Promotion: In Vitro, In Silico, and In Vivo Evaluation

Frontiers in Pharmacology ◽

10.3389/fphar.2021.774583 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jawaria Iltaf ◽

Sobia Noreen ◽

Muhammad Fayyaz ur Rehman ◽

Shazia Akram Ghumman ◽

Fozia Batool ◽

...

Keyword(s):

In Silico ◽

Hair Growth ◽

De Novo ◽

Growth Promotion ◽

Hair Follicles ◽

Binding Energies ◽

Ficus Benghalensis ◽

Rp Hplc

The screening of hair follicles, dermal papilla cells, and keratinocytes through in vitro, in vivo, and histology has previously been reported to combat alopecia. Ficus benghalensis has been used conventionally to cure skin and hair disorders, although its effect on 5α-reductase II is still unknown. Currently, we aim to analyze the phytotherapeutic impact of F. benghalensis leaf extracts (FBLEs) for promoting hair growth in rabbits along with in vitro inhibition of the steroid isozyme 5α-reductase II. The inhibition of 5α-reductase II by FBLEs was assessed by RP-HPLC, using the NADPH cofactor as the reaction initiator and Minoxin (5%) as a positive control. In silico studies were performed using AutoDock Vina to visualize the interaction between 5α-reductase II and the reported phytoconstituents present in FBLEs. Hair growth in female albino rabbits was investigated by applying an oral dose of the FBLE formulation and control drug to the skin once a day. The skin tissues were examined by histology to see hair follicles. Further, FAAS, FTIR, and antioxidants were performed to check the trace elements and secondary metabolites in the FBLEs. The results of RP-HPLC and the binding energies showed that FBLEs reduced the catalytic activity of 5α-reductase II and improved cell proliferation in rabbits. The statistical analysis (p < 0.05 or 0.01) and percentage inhibition (>70%) suggested that hydroalcoholic FBLE has more potential in increasing hair growth by elongating hair follicle’s anagen phase. FAAS, FTIR, and antioxidant experiments revealed sufficient concentrations of Zn, Cu, K, and Fe, together with the presence of polyphenols and scavenging activity in FBLE. Overall, we found that FBLEs are potent in stimulating hair follicle maturation by reducing the 5α-reductase II action, so they may serve as a principal choice in de novo drug designing to treat hair loss.

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