Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility–activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form

Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility–activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form

Three-dimensional structure representation of Homing endonuclease I-crei complex (1g9y), using display features of the Lithium software package 75

<p><b>Copyright information:</b></p><p>Taken from "Energetics of the protein-DNA-water interaction"</p><p>BMC Structural Biology 2007;7():4-4.</p><p>Published online 10 Jan 2007</p><p>PMCID:PMC1781455.</p><p></p> Overall view of the complex where the protein is displayed in ribbon/tube style and the DNA is represented in color-coded ribbons: red for adenine (A), blue for cytosine (C), green for guanine (G), and yellow for thymine (T). Water molecule hydrating a negatively-charged amino acid side-chain. Water molecule hydrating a DNA phosphate group. Water molecule screening the repulsive interaction between an Asp side-chain and a DNA phosphate. Water molecule located at the complex interface mediating specific amino acid-base interactions

Structure-Based Virtual Screening for the Discovery of Novel Inhibitors of New Delhi Metallo-β-lactamase‑1

Bacterial resistance has become a worldwide concern after the emergence of metallo-β-lactamases (MBLs). They represent one of the major mechanisms of bacterial resistance against beta-lactam antibiotics. Among MBLs, New Delhi metallo-β-lactamase-1 NDM-1, the most prevalent type, is extremely efficient in inactivating nearly all-available antibiotics including last resort carbapenems. No inhibitors for NDM-1 are currently available in therapy, making the spread of NDM-1 producing bacterial strains a serious menace. With this perspective, we performed a structure-based <i>in silico</i> screening of a commercially available library using FLAPdock and identified several, non-β-lactam derivatives as promising candidates active against NDM-1. The binding affinities of the highest scoring hits were measured <i>in vitro</i> revealing, for some of them, low micromolar affinity toward NDM-1. For the best inhibitors, efficacy against resistant bacterial strains overexpressing NDM-1 was validated, confirming their favorable synergistic effect in combination with the carbapenem Meropenem

Human dopamine transporter: the first implementation of a combined <i>in silico/in vitro</i> approach revealing the substrate and inhibitor specificities

<p>Parkinson’s disease (PD) is characterized by the loss of dopamine-generating neurons in the <i>substantia nigra</i> and <i>corpus striatum</i>. Current treatments alleviate PD symptoms rather than exerting neuroprotective effect on dopaminergic neurons. New drugs targeting the dopaminergic neurons by specific uptake through the human dopamine transporter (hDAT) could represent a viable strategy for establishing selective neuroprotection. Molecules able to increase the bioactive amount of extracellular dopamine, thereby enhancing and compensating a loss of dopaminergic neurotransmission, and to exert neuroprotective response because of their accumulation in the cytoplasm, are required. By means of homology modeling, molecular docking, and molecular dynamics simulations, we have generated 3D structure models of hDAT in complex with substrate and inhibitors. Our results clearly reveal differences in binding affinity of these compounds to the hDAT in the open and closed conformations, critical for future drug design. The established <i>in silico</i> approach allowed the identification of promising substrate compounds that were subsequently analyzed for their efficiency in inhibiting hDAT-dependent fluorescent substrate uptake, through <i>in vitro</i> live cell imaging experiments. Taken together, our work presents the first implementation of a combined <i>in silico/in vitro</i> approach enabling the selection of promising dopaminergic neuron-specific substrates.</p

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack. Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (<i>h</i>AOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information

From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack. Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (<i>h</i>AOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information

CO rebinding kinetics.

<p>(A) Comparison between the CO rebinding kinetics to wt Cygb (circles) and HE7Q Cygb* (solid lines) solutions at 40°C, equilibrated with 1 atm CO (black) and 0.1 atm CO (red). (B) Lifetime distributions associated with the rebinding kinetics in panel A. (C) Comparison between the CO rebinding kinetics to wt Cygb solutions (blue circles), wt COCygb gels (black circles) and wt Cygb+CO gels (red circles). T = 40°C, 1 atm CO. (D) Lifetime distributions associated with the rebinding kinetics in panel C.</p

Positional fluctuations of residues.

<p>Representation of the root-mean square fluctuations (Å) of residues for the five simulated systems: <i>Cygb_h</i> (from 1UT0), <i>Cygb_p</i> (from HE7Q mutant and 3AG0), and O₂Cygb (from HE7Q mutant and 3AG0). The positional fluctuations determined for the residue atoms of uniquely the backbone are represented in red and black, respectively.</p

Self- and cross-similarity indexes determined for the active space of essential motions derived for the different coordinated species of Cygb.

<p>Self- similarity indexes were determined by considering the essential motions derived from the snapshots sampled in time windows 20–60 and 60–100 ns in a single trajectory. Cross-similarity indexes were determined by averaging the values obtained from the comparison of the different time windows in two trajectories. The active space comprised 30 eigenvectors, which explain around 85% of the structural variance.</p