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Residence Times for Femtomolar and Picomolar Inhibitors of MTANs.

ABSTRACT: 5'-Methylthioadenosine nucleosidases (MTANs) catalyze the hydrolysis of 5'-substituted adenosines to form adenine and 5-substituted ribose. Escherichia coli MTAN (EcMTAN) and Helicobacter pylori MTAN (HpMTAN) form late and early transition states, respectively. Transition state analogues designed for the late transition state bind with fM to pM affinity to both classes of MTANs. Here, we compare the residence times (off-rates) with the equilibrium dissociation constants for HpMTAN and EcMTAN, using five 5'-substituted DADMe-ImmA transition state analogues. The inhibitors dissociate orders of magnitude slower from EcMTAN than from HpMTAN. For example, the slowest release rate was observed for the EcMTAN-HTDIA complex (t_1/2 = 56 h), compared to a release rate of t_1/2 = 0.3 h for the same complex with HpMTAN, despite similar structures and catalytic sites for these enzymes. Other inhibitors also reveal disconnects between residence times and equilibrium dissociation constants. Residence time is correlated with pharmacological efficacy; thus, experimental analyses of dissociation rates are useful to guide physiological function of tight-binding inhibitors. Steered molecular dynamics simulations for the dissociation of an inhibitor from both EcMTAN and HpMTAN provide atomic level mechanistic insight for the differences in dissociation kinetics and inhibitor residence times for these enzymes.

SUBMITTER: Brown M

PROVIDER: S-EPMC10311515 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Residence Times for Femtomolar and Picomolar Inhibitors of MTANs.

Brown Morais M Tyler Peter C PC Schramm Vern L VL

Biochemistry 20230519 11

5'-Methylthioadenosine nucleosidases (MTANs) catalyze the hydrolysis of 5'-substituted adenosines to form adenine and 5-substituted ribose. Escherichia coli MTAN (EcMTAN) and Helicobacter pylori MTAN (HpMTAN) form late and early transition states, respectively. Transition state analogues designed for the late transition state bind with fM to pM affinity to both classes of MTANs. Here, we compare the residence times (off-rates) with the equilibrium dissociation constan ...[more]

PMID: 37204861

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Project description:Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Dataset Information

Residence Times for Femtomolar and Picomolar Inhibitors of MTANs.

Publications

Residence Times for Femtomolar and Picomolar Inhibitors of MTANs.

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