In silico design of HDAC6 inhibitors with neuroprotective effects.

HDAC6 has emerged as a molecular target to treat neurodegenerative disorders, due to its participation in protein aggregate degradation, oxidative stress process, mitochondrial transport, and axonal transport. Thus, in this work we have designed a set of 485 compounds with hydroxamic and bulky-hydrophobic moieties that may function as HDAC6 inhibitors with a neuroprotective effect. These compounds were filtered by their predicted ADMET properties and their affinity to HDAC6 demonstrated by molecular docking and molecular dynamics simulations. The combination of in silico with in vitro neuroprotective results allowed the identification of a lead compound (FH-27) which shows neuroprotective effect that could be due to HDAC6 inhibition. Further, FH-27 chemical moiety was used to design a second series of compounds improving the neuroprotective effect from 2- to 10-fold higher (YSL-99, YSL-109, YSL-112, YSL-116 and YSL-121; 1.25 ± 0.67, 1.82 ± 1.06, 7.52 ± 1.78, 5.59 and 5.62 ± 0.31 µM, respectively). In addition, the R enantiomer of FH-27 (YSL-106) was synthesized, showing a better neuroprotective effect (1.27 ± 0.60 µM). In conclusion, we accomplish the in silico design, synthesis, and biological evaluation of hydroxamic acid derivatives with neuroprotective effect as suggested by an in vitro model. Communicated by Ramaswamy H. Sarma.

Exploring the inhibitory activity of valproic acid against the HDAC family using an MMGBSA approach.

Valproic acid (VPA) is a compound currently used in clinical practice for the treatment of epilepsy as well as bipolar and mood disorders. VPA targets histone deacetylases (HDACs), which participate in the removal of acetyl groups from lysine in several proteins, regulating a wide variety of functions within the organism. An imbalance or malfunction of these enzymes is associated with the development and progression of several diseases, such as cancer and neurodegenerative diseases. HDACs are divided into four classes, but VPA only targets Class I (HDAC1-3 and 8) and Class IIa (HDAC4-5, 7 and 9) HDACs; however, structural and energetic information regarding the manner by which VPA inhibits these HDACs is lacking. Here, the structural and energetic features that determine this recognition were studied using molecular docking and molecular dynamics (MD) simulation. It was found that VPA reaches the catalytic site in HDAC1-3 and 7, whereas in HDAC6, VPA only reaches the catalytic tunnel. In HDAC4, VPA was bound adjacent to L1 and L2, a zone that participates in corepressor binding, and in HDAC8, VPA was bound to the hydrophobic active site channel (HASC), in line with previous reports.

Structural and energetic basis for the inhibitory selectivity of both catalytic domains of dimeric HDAC6.

HDAC6 is a protein involved in cancer, neurodegenerative disease and inflammatory disorders. To date, the full three-dimensional (3D) structure of human HDAC6 has not been elucidated; however, there are some experimental 3D structural homologs to HDAC6 that can be used as templates. In this work, we utilized molecular modeling procedures to model both of the catalytic domains of HDAC6 connected by the linker region where DMB region is placed. Once the 3D structure of human HDAC6 was obtained, it was structurally evaluated and submitted to docking and molecular dynamic (MD) simulations along with Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method to explore the stability and the binding free energy properties of the HDAC6-ligand complexes. In addition, its structural and energetic behavior was explored with each one of the catalytic domains in the molecular recognition of six selective HDAC6 inhibitors, HPOB, CAY10603, Nexturastat, Rocilinostat, Tubacin and Tubastatin A for DD2, and with the so-called 9-peptide which is DD1-HDAC6 selective substrate. The use of the whole system (DD1-DMB-DD2) showed a tendency toward the ligand affinity of DD2, CAY10603> Tubacin > Rocilinostat > Nexturastat > HPOB > Tubastatin > 9-peptide, which is in line with experimental reports. However, 9-peptide showed a higher affinity for DD1, which agrees with experimental reports elsewhere. Principal component analysis provided important information about the structural changes linked to the molecular recognition process, whereas per-residue decomposition analysis revealed the energetic contribution of the key residues in the molecular binding and structural characteristics that could assist in drug design.