BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity for drug design. After binding to a biologically relevant target system, covalent compounds form a reversible or irreversible covalent bond with a nucleophilic amino acid. Due to the inherently large binding energy of a covalent bond, covalent binders exhibit higher potencies and thus allow potentially lower drug dosages. However, a proper balancing of compound reactivity is key for the design of covalent binders, to achieve high levels of target inhibition while minimizing promiscuous covalent binding to nontarget proteins. In this work, we demonstrated the possibility to apply the electrophilicity index concept to estimate covalent compound reactivity. We tested this approach on acrylamides, one of the most prominent classes of covalent warheads. Our study clearly demonstrated that, for compounds with molecular weight (MW) below 250 Da, the electrophilicity index can be directly used to estimate compound reactivity. On the other hand, for leadlike molecules (MW > 250 Da) we implemented a new truncation algorithm that has to be applied before reactivity calculations. This algorithm can ensure the localization of HOMO/LUMO orbitals on the compound warhead and thus a correct estimation of its reactivity. Our results also indicate that caution should be used when employing the electrophilicity index to estimate the reactivity of nonterminal acrylamides. The nonparametric nature of this method and its reasonable computational cost make it a suitable tool to support covalent drug design.

Conformational Entropy as Collective Variable for Proteins

Many enhanced sampling methods rely on the identification of appropriate collective variables. For proteins, even small ones, finding appropriate descriptors has proven challenging. Here we suggest that the NMR S² order parameter can be used to this effect. We trace the validity of this statement to the suggested relation between S² and conformational entropy. Using the S² order parameter and a surrogate for the protein enthalpy in conjunction with metadynamics or variationally enhanced sampling, we are able to reversibly fold and unfold a small protein and draw its free energy at a fraction of the time that is needed in unbiased simulations. We also use S² in combination with the free energy flooding method to compute the unfolding rate of this peptide. We repeat this calculation at different temperatures to obtain the unfolding activation energy.

The impact of methylphenidate and its enantiomers on dopamine synthesis and metabolism in vitro

Methylphenidate (MPH), a psychostimulant, is an effective first-line treatment for the symptoms associated with Attention-Deficit/Hyperactivity Disorder (ADHD). Although most MPH formulations are composed of the racemic 1:1 mixture of the two enantiomers (d- and l-threo), converging lines of evidence indicate that d-threo MPH seems to be superior to the l-isomer. We aimed to investigate whether MPH racemic mixture or pure enantiomers influence the enzyme activity of tyrosine hydroxylase (TH), monoamine oxidase B (MAO-B), catechol-O-methyltransferase (COMT), and aldehyde dehydrogenase (ALDH) in vitro in homogenates of rat PC12 cells incubated with racemic, d- and l-threo MPH (1nM up to 100μM), or a vehicle for control. We could observe dose dependent enhancement of TH activity with d-threo MPH, probably due to its higher affinity to the enzyme, which we could confirm for d-threo versus l-threo MPH via docking and molecular dynamic simulations analysis. MAO-B enzyme activity was found to be enhanced when incubated with both d- and l-isomers but not with the racemic mixture. This conflicting result was hypothesized to be due to possible aggregation of the two enantiomers or other molecular conformations. Such a possible interaction was observed indirectly, when TH was incubated with constant d-threo MPH while increasing l-isomer (increasing total MPH concentrations). Hence, TH activity was slightly decreased with increased l-isomer. In conclusion, the current in vitro investigation points to the stereoselectivity of the investigated enzymes and pharmacological effects of MPH enantiomers.

Correction: Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles

Correction for 'Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles' by Alan E. Enciso, et al., Nanoscale, 2017, 9, 3128-3132.

Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles

Upon reduction with sodium borohydride, diazonium tetrachloroaurate salts of triazine dendrons yield dendron-coated gold nanoparticles connected by a gold-carbon bond. These robust nanoparticles are stable in water and toluene solutions for longer than one year and present surface groups that can be reacted to change surface chemistry and manipulate solubility. Molecular modeling was used to provide insight on the hydration of the nanoparticles and their observed solubilties.

Focus on PNA Flexibility and RNA Binding using Molecular Dynamics and Metadynamics

Peptide Nucleic Acids (PNAs) can efficiently target DNA or RNA acting as chemical tools for gene regulation. Their backbone modification and functionalization is often used to increase the affinity for a particular sequence improving selectivity. The understanding of the trading forces that lead the single strand PNA to bind the DNA or RNA sequence is preparatory for any further rational design, but a clear and unique description of this process is still not complete. In this paper we report further insights into this subject, by a computational investigation aiming at the characterization of the conformations of a single strand PNA and how these can be correlated to its capability in binding DNA/RNA. Employing Metadynamics we were able to better define conformational pre-organizations of the single strand PNA and γ-modified PNA otherwise unrevealed through classical molecular dynamics. Our simulations driven on backbone modified PNAs lead to the conclusion that this γ-functionalization affects the single strand preorganization and targeting properties to the DNA/RNA, in agreement with circular dichroism (CD) spectra obtained for this class of compounds. MD simulations on PNA:RNA dissociation and association mechanisms allowed to reveal the critical role of central bases and preorganization in the binding process.

The allosteric communication pathways in KIX domain of CBP

Allosteric regulation plays an important role in a myriad of biomacromolecular processes. Specifically, in a protein, the process of allostery refers to the transmission of a local perturbation, such as ligand binding, to a distant site. Decades after the discovery of this phenomenon, models built on static images of proteins are being reconsidered with the knowledge that protein dynamics plays an important role in its function. Molecular dynamics simulations are a valuable tool for studying complex biomolecular systems, providing an atomistic description of their structure and dynamics. Unfortunately, their predictive power has been limited by the complexity of the biomolecule free-energy surface and by the length of the allosteric timescale (in the order of milliseconds). In this work, we are able to probe the origins of the allosteric changes that transcription factor mixed lineage leukemia (MLL) causes to the interactions of KIX domain of CREB-binding protein (CBP) with phosphorylated kinase inducible domain (pKID), by combing all-atom molecular dynamics with enhanced sampling methods recently developed in our group. We discuss our results in relation to previous NMR studies. We also develop a general simulations protocol to study allosteric phenomena and many other biological processes that occur in the micro/milliseconds timescale.