Agel amyloidosis (also known as Finnish-type) is a rare hereditary disease caused by the abnormal aggregation and accumulation of fragments of the gelsolin protein. The fragments form fibrillar aggregates and affect the cornea, facial nerves, skin, and kidneys. A number of mutations in the G2 domain of the protein have been so far identified as disease-causing. While several mutated forms of G2 have been crystallized in the past, providing insights on the molecular etiology of fibrillation and possible therapeutic pathways, the D187N has long remained elusive, probably due to a shifted order-disorder equilibrium.
alejandroNanobody interaction unveils structure, dynamics and proteotoxicity of the Finnish-type amyloidogenic gelsolin variant
Our current understanding of drug design is fundamentally structure based. The process works as follows: once the structure of the target protein is known, and some interesting pockets have been identified on it, medicinal chemists can study these spaces and suggest small molecules which can create strong interactions with that protein environment, hopefully leading to a conformational change in the protein which will modify its behavior.
alejandroLigVoxel: A deep learning pharmacophore-field predictor
Researchers have been looking beyond traditional Molecular Dynamics (MD) for a long time now. While many have been using many forms of biased MD (for example, metadynamics), we have alternatively proposed Adaptive Molecular Dynamics 1,2 as a way to unbiasedly enhance the exploration of the phase space through on-the-fly learning of the explored space.
alejandroAdaptive Molecular Dynamics on the Cloud with HTMD and AceCloud
“A command-line interface designed to perform complex workflows with few inputs”
By Alberto Cuzzolin
Nowadays, Molecular Dynamics (MD) simulation is known to be an important tool in drug discovery process. Although its potential is clearly proved, it is still not trivial to handle all the steps necessary to retrieve results. To handle all these aspects, we developed HTMD, a python framework that manages all these aspects and allows an easily scale up to an high-throughput manner.
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Molecular dynamics has matured to the point where users can simulate multiple protein systems over timescales as large as milliseconds (see reference of HTMD and ACEMD at https://www.acellera.com/science/).
However, the preparation of the protein systems remains a complex step.
Tools already exist which allow the preparation of an MD system using visual GUIs or webservers.
However few, like HTMD, are built allowing scriptable system preparation for multiple hundreds of such systems.
Adapted with permission from J. Chem. Theory Comput., 2017, 13 (9), pp 4003–4011. Copyright 2017 American Chemical Society.
alejandroAutomated Preparation and Simulation of Membrane Proteins with HTMD
One step of the simulation workflow has typically remained relatively under addressed, namely, the preparation steps before building a molecular system. These preparation steps aim to make a protein structure, usually extracted from the PDB database, ready for system building. In particular, there’s two main points that need to be addressed: (a) titration of the residues and (b) optimization of hydrogen bond (H-bond) network. Resolved protein structures usually don’t contain hydrogen atoms and therefore need to be added by the researcher before running a molecular dynamics (MD) simulation. While most hydrogens can be easily guessed, some protein residues prove to be more challenging as they co-exist in different protonation states. While in a constant-pH simulation the residues would be free to switch among the different protonation states, in a classic MD simulation protonation states are fixed and therefore must be decided beforehand. These protonation states greatly depend on the local environment of the residue and the simulation pH.
alejandroPreparing a Molecular System for MD with PlayMolecule
Proteins are essential in the regulation of most of the processes in a cell. They do so by interacting with other molecules, including other proteins.
Protein-protein interactions have been the subject of study for a long time using both experimental and computational methods, however they have eluded the atomic-level analysis and accuracy that unbiased molecular dynamics simulations can provide.
Franck ChevalierComplete protein–protein kinetics by molecular dynamics
ACEMD was introduced in the field of Molecular Dynamics (MD) back in 2008. Its release, together with other MD engines, revolutionized the simulation throughput by leveraging the intrinsic parallelism present in a specific computer processor termed GPU (Graphic Processor Unit). Using a cluster of GPUs or a distributed network like GPUGRID one is able to reach performances of HPC-class computers using commodity software, yielding a significant (x10) reduction in the Euro/simulated time cost. Furthermore, the evolution of GPUs performance, which is mainly driven by a market demanding more graphic processing power (e.g. in videogames), has doubled approximately every 3 years and with it, since 2008, ACEMD performance has increased 2.1x. To illustrate this fact, we have compiled the maximum simulated time per year published by prof. Gianni de Fabritiis using ACEMD (which yields the oldest historic data) and we extrapolate that the MD field will reach the second aggregated time timescale by 2022.