Effects of pH and aggregation in the human prion conversion into scrapie form: a study using molecular dynamics with excited normal modes.

There are two different prion conformations: (1) the cellular natural (PrPC) and (2) the scrapie (PrPSc), an infectious form that tends to aggregate under specific conditions. PrPC and PrPSc are widely different regarding secondary and tertiary structures. PrPSc contains more and longer ß-strands compared to PrPC. The lack of solved PrPSc structures precludes a proper understanding of the mechanisms related to the transition between cellular and scrapie forms, as well as the aggregation process. In order to investigate the conformational transition between PrPC and PrPSc, we applied MDeNM (molecular dynamics with excited normal modes), an enhanced sampling simulation technique that has been recently developed to probe large structural changes. These simulations yielded new structural rearrangements of the cellular prion that would have been difficult to obtain with standard MD simulations. We observed an increase in ß-sheet formation under low pH (≤ 4) and upon oligomerization, whose relevance was discussed on the basis of the energy landscape theory for protein folding. The characterization of intermediate structures corresponding to transition states allowed us to propose a conversion model from the cellular to the scrapie prion, which possibly ignites the fibril formation. This model can assist the design of new drugs to prevent neurological disorders related to the prion aggregation mechanism.

Binding of phenothiazines into allosteric hydrophobic pocket of human thioredoxin 1.

Thioredoxins are multifunctional oxidoreductase proteins implicated in the antioxidant cellular apparatus and oxidative stress. They are involved in several pathologies and are promising anticancer targets. Identification of noncatalytic binding sites is of great interest for designing new allosteric inhibitors of thioredoxin. In a recent work, we predicted normal mode motions of human thioredoxin 1 and identified two major putative hydrophobic binding sites. In this work we investigated noncovalent interactions of human thioredoxin 1 with three phenotiazinic drugs acting as prooxidant compounds by using molecular docking and circular dichroism spectrometry to probe ligand binding into the previously predicted allosteric hydrophobic pockets. Our in silico and CD spectrometry experiments suggested one preferred allosteric binding site involving helix 3 and adopting the best druggable conformation identified by NMA. The CD spectra showed binding of thioridazine into thioredoxin 1 and suggested partial helix unfolding, which most probably concerns helix 3. Taken together, these data support the strategy to design thioredoxin inhibitors targeting a druggable allosteric binding site.

Binding sites and hydrophobic pockets in Human Thioredoxin 1 determined by normal mode analysis.

The Thioredoxin (Trx) system plays important roles in several diseases (e.g. cancer, viral infections, cardiovascular and neurodegenerative diseases). Therefore, there is a therapeutic interest in the design of modulators of this system. In this work, we used normal mode analysis to identify putative binding site regions for Human Trx1 that arise from global motions. We identified three possible inhibitor's binding regions that corroborate previous experimental findings. We show that intrinsic motions of the protein are related to the exposure of hydrophobic regions and non-active site cysteines that could constitute new binding sites for inhibitors.

Use of machine learning approaches for novel drug discovery.

INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. AREAS COVERED: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. EXPERT OPINION: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Molecular cloning and in silico characterization of knottin peptide, U2-SCRTX-Lit2, from brown spider (Loxosceles intermedia) venom glands.

Inhibitor cystine knots (ICKs) are a family of structural peptides with a large number of cysteine residues that form intramolecular disulfide bonds, resulting in a knot. These peptides are involved in a variety of biological functions including predation and defense, and are found in various species, such as spiders, scorpions, sea anemones, and plants. The Loxosceles intermedia venom gland transcriptome identified five groups of ICK peptides that represent more than 50 % of toxin-coding transcripts. Here, we describe the molecular cloning of U2-Sicaritoxin-Lit2 (U2-SCRTX-Lit2), bioinformatic characterization, structure prediction, and molecular dynamic analysis. The sequence of U2-SCRTX-Lit2 obtained from the transcriptome is similar to that of µ-Hexatoxin-Mg2, a peptide that inhibits the insect Nav channel. Bioinformatic analysis of sequences classified as ICK family members also showed a conservation of cysteine residues among ICKs from different spiders, with the three dimensional molecular model of U2-SCRTX-Lit2 similar in structure to the hexatoxin from µ-hexatoxin-Mg2a. Molecular docking experiments showed the interaction of U2-SCRTX-Lit2 to its predictable target-the Spodoptera litura voltage-gated sodium channel (SlNaVSC). After 200 ns of molecular dynamic simulation, the final structure of the complex showed stability in agreement with the experimental data. The above analysis corroborates the existence of a peptide toxin with insecticidal activity from a novel ICK family in L. intermedia venom and demonstrates that this peptide targets Nav channels.