Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Dronabinol as an answer to flavivirus infections: an in-silico investigation

Flavivirus infections are common in several parts of the world. Two major types of flaviviruses are dengue and zika viruses. Both these two viral infections have caused many fatalities around the world. There is an absence of a vaccine and an effective medication against these viruses. In this study, we analyzed the ability of dronabinol to act as a potential cure against these viral infections. We performed the docking of dronabinol with several viral proteins followed by molecular dynamics simulation, MM/PBSA and PCA analysis. We checked the ability of the polyphenol dronabinol to interfere with the binding of viral helicases to their cellular targets. We performed 2 D-QSAR studies, drug likeliness, ADMET and target prediction studies. From our study, we observed that dronabinol had the best docking ability against the helicase proteins of dengue and zika. Molecular dynamics simulation and MM/PBSA investigation confirmed the stability of the binding while PCA investigation showed a lowering of molecular motions in response to dronabinol docking to the helicases. Dronabinol interfered in the binding of the helicases to RNA. 2 D QSAR studies revealed a low IC50 value for dronabinol. Dronabinol showed favorable drug-likeness, ADMET properties and target prediction results. Thus we propose dronabinol be further investigated in-vitro as a cure against dengue and zika virus infections.Communicated by Ramaswamy H. Sarma.

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

The unprecedented performance of Deepmind's Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold's ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

What have molecular simulations contributed to understanding of Gram-negative bacterial cell envelopes?

Bacterial cell envelopes are compositionally complex and crowded and while highly dynamic in some areas, their molecular motion is very limited, to the point of being almost static in others. Therefore, it is no real surprise that studying them at high resolution across a range of temporal and spatial scales requires a number of different techniques. Details at atomistic to molecular scales for up to tens of microseconds are now within range for molecular dynamics simulations. Here we review how such simulations have contributed to our current understanding of the cell envelopes of Gram-negative bacteria.