8 research outputs found
Assessing the stability of free-energy perturbation calculations by performing variations in the method
Electrostatic Interactions between Complement Regulator CD46(SCR1-2) and Adenovirus Ad11/Ad21 Fiber Protein Knob
Adenoviruses bind to a variety of human cells to cause infection. Both the B2 adenovirus 11 and B1 adenovirus 21 use protein knobs to bind to complement regulator CD46(SCR1-2) in order to gain entry into host cells. In each complex, the two proteins are highly negatively charged but bind to each other at an interface with oppositely charged surface patches. We computationally generated single-alanine mutants of charged residues in the complexes CD46(SCR1-2)-Ad11k and CD46(SCR1-2)-Ad21k. We used electrostatic clustering and Poisson-Boltzmann free energy calculations to propose a hypothesis on the role of electrostatics in association. Our results delineate specific interfacial electrostatic interactions that are critical for association in both CD46(SCR1-2)-Ad11k and CD46(SCR1-2)-Ad21k. These results will serve as a predictive tool in the selection of mutants with desired binding affinity in experimental mutagenesis studies. This study will also serve as a foundation for the design of inhibitors to treat adenovirus infections
Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model
Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study
Repurposing natural compounds as potent inhibitors of β-catenin: an integrative in-silico approaches
Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking
Multiscale methods in drug design bridge chemical and biological complexity in the search for cures
Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.</p