Template-based protein–protein docking exploiting pairwise interfacial residue restraints

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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Proteomic Analysis of Medicinal Plant Calotropis Gigantea by insilico Peptide Mass Fingerprinting

Current Computer - Aided Drug Design ◽

10.2174/1573409916666200219114531 ◽

2020 ◽

Vol 16 ◽

Author(s):

Saad Ur Rehman ◽

Muhammad Rizwan ◽

Sajid Khan ◽

Azhar Mehmood ◽

Anum Munir

Keyword(s):

Medicinal Plant ◽

Structure Prediction ◽

3D Structure ◽

Peptide Mass Fingerprinting ◽

Protein Docking ◽

Receptor Protein ◽

Human Leukemia ◽

Protein Database ◽

Calotropis Gigantea ◽

Peptide Mass

: Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. Background: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in a database and identified. Method: The calculation of insilico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from MASCOT online server. Anticancer probability is calculated from the iACP server, docking of active peptides is done by CABS-dock the server. Objective: The purpose of the study is to identify the peptides having anti-cancerous properties by in-silico peptide mass fingerprinting. Results : The anti-cancerous peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the Active chain, the human leukemia protein docking with peptides shows the accurate binding. Conclusion : These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

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FPDock: Protein-Protein Docking Using Flower Pollination Algorithm

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2021.107518 ◽

2021 ◽

pp. 107518

Author(s):

Sharon Sunny ◽

Jayaraj P.B.

Keyword(s):

Protein Docking ◽

Flower Pollination Algorithm ◽

Flower Pollination

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The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

Bioinformatics and Biology Insights ◽

10.1177/11779322211002168 ◽

2021 ◽

Vol 15 ◽

pp. 117793222110021

Author(s):

Onyeka S Chukwudozie ◽

Vincent C Duru ◽

Charlotte C Ndiribe ◽

Abdullahi T Aborode ◽

Victor O Oyebanji ◽

...

Keyword(s):

Viral Genome ◽

Drug Repurposing ◽

Protein Docking ◽

Vaccine Research ◽

Vaccine Candidates ◽

Genome Data ◽

Disease Agent ◽

Time Critical ◽

Antigen Antibody ◽

Antigenic Epitopes

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

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GNINA 1.0: molecular docking with deep learning

Journal of Cheminformatics ◽

10.1186/s13321-021-00522-2 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Andrew T. McNutt ◽

Paul Francoeur ◽

Rishal Aggarwal ◽

Tomohide Masuda ◽

Rocco Meli ◽

...

Keyword(s):

Molecular Docking ◽

Root Mean Square ◽

Root Mean Square Deviation ◽

Computational Cost ◽

Scoring Function ◽

Binding Pocket ◽

Protein Docking ◽

Mean Square ◽

Mean Square Deviation ◽

Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

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