A Network of Conformational Transitions Revealed by Molecular Dynamics Simulations of the Binary Complex of <i>Escherichia coli</i> 6‑Hydroxymethyl-7,8-dihydropterin Pyrophosphokinase with MgATP

6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) catalyzes the first reaction in the folate biosynthetic pathway. Comparison of its X-ray and nuclear magnetic resonance structures suggests that the enzyme undergoes significant conformational change upon binding to its substrates, especially in three catalytic loops. Experimental research has shown that, in its binary form, even bound by analogues of MgATP, loops 2 and 3 remain rather flexible; this raises questions about the putative large-scale induced-fit conformational change of the HPPK–MgATP binary complex. In this work, long-time all-atomic molecular dynamics simulations were conducted to investigate the loop dynamics in this complex. Our simulations show that, with loop 3 closed, multiple conformations of loop 2, including the open, semiopen, and closed forms, are all accessible to the binary complex. These results provide valuable structural insights into the details of conformational changes upon 6-hydroxymethyl-7,8-dihydropterin (HP) binding and biological activities of HPPK. Conformational network analysis and principal component analysis related to the loops are also discussed

Molecular Dynamics Simulations of the <i>Escherichia coli</i> HPPK Apo-enzyme Reveal a Network of Conformational Transitions

6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) catalyzes the first reaction in the folate biosynthetic pathway. Comparison of its X-ray and nuclear magnetic resonance structures suggests that the enzyme undergoes significant conformational change upon binding to its substrates, especially in three catalytic loops. Experimental research has shown that even when confined by crystal contacts, loops 2 and 3 remain rather flexible when the enzyme is in its apo form, raising questions about the putative large-scale induced-fit conformational change of HPPK. To investigate the loop dynamics in a crystal-free environment, we performed conventional molecular dynamics simulations of the apo-enzyme at two different temperatures (300 and 350 K). Our simulations show that the crystallographic <i>B</i>-factors considerably underestimate the loop dynamics; multiple conformations of loops 2 and 3, including the open, semi-open, and closed conformations that an enzyme must adopt throughout its catalytic cycle, are all accessible to the apo-enzyme. These results revise our previous view of the functional mechanism of conformational change upon MgATP binding and offer valuable structural insights into the workings of HPPK. In this paper, conformational network analysis and principal component analysis related to the loops are discussed to support the presented conclusions

Proteome-Informed Machine Learning Studies of Cocaine Addiction

No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstream and downstream of the dopamine transporter. However, it is difficult to study so many proteins with traditional experiments, highlighting the need for innovative strategies in the field. We propose a proteome-informed machine learning (ML) platform for discovering nearly optimal anti-cocaine addiction lead compounds. We analyze proteomic protein–protein interaction networks for cocaine dependence to identify 141 involved drug targets and build 32 ML models for cross-target analysis of more than 60,000 drug candidates or experimental drugs for side effects and repurposing potentials. We further predict their ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Our platform reveals that essentially all of the existing drug candidates fail in our cross-target and ADMET screenings but identifies several nearly optimal leads for further optimization

Perspectives on SARS-CoV‑2 Main Protease Inhibitors

The main protease (Mpro) plays a crucial role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and is highly conserved, rendering it one of the most attractive therapeutic targets for SARS-CoV-2 inhibition. Currently, although two drug candidates targeting SARS-CoV-2 Mpro designed by Pfizer are under clinical trials, no SARS-CoV-2 medication is approved due to the long period of drug development. Here, we collect a comprehensive list of 817 available SARS-CoV-2 and SARS-CoV Mpro inhibitors from the literature or databases and analyze their molecular mechanisms of action. The structure–activity relationships (SARs) among each series of inhibitors are discussed. Additionally, we broadly examine available antiviral activity, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and animal tests of these inhibitors. We comment on their druggability or drawbacks that prevent them from becoming drugs. This Perspective sheds light on the future development of Mpro inhibitors for SARS-CoV-2 and future coronavirus diseases

Binding Enthalpy Calculations for a Neutral Host–Guest Pair Yield Widely Divergent Salt Effects across Water Models

Dissolved salts are a part of the physiological milieu and can significantly influence the kinetics and thermodynamics of various biomolecular processes, such as binding and catalysis; thus, it is important for molecular simulations to reliably describe their effects. The present study uses a simple, nonionized host–guest model system to study the sensitivity of computed binding enthalpies to the choice of water and salt models. Molecular dynamics simulations of a cucurbit[7]­uril host with a neutral guest molecule show striking differences in the salt dependency of the binding enthalpy across four water models, TIP3P, SPC/E, TIP4P-Ew, and OPC, with additional sensitivity to the choice of parameters for sodium and chloride. In particular, although all of the models predict that binding will be less exothermic with increasing NaCl concentration, the strength of this effect varies by 7 kcal/mol across models. The differences appear to result primarily from differences in the number of sodium ions displaced from the host upon binding the guest rather than from differences in the enthalpy associated with this displacement, and it is the electrostatic energy that contributes most to the changes in enthalpy with increasing salt concentration. That a high sensitivity of salt affecting the choice of water model, as observed for the present host–guest system despite it being nonionized, raises issues regarding the selection and adjustment of water models for use with biological macromolecules, especially as these typically possess multiple ionized groups that can interact relatively strongly with ions in solution

Machine Learning Analysis of Cocaine Addiction Informed by DAT, SERT, and NET-Based Interactome Networks

Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large number of deaths around the world. Despite decades of effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in the interactome. Among them, the dopamine (DAT), serotonin (SERT), and norepinephrine (NET) transporters are three major targets. Each of these targets has a large protein–protein interaction (PPI) network, which must be considered in the anticocaine addiction drug discovery. This work presents DAT, SERT, and NET interactome network-informed machine learning/deep learning (ML/DL) studies of cocaine addiction. We collected and analyzed 61 protein targets out of 460 proteins in the DAT, SERT, and NET PPI networks that have sufficiently large existing inhibitor datasets. Utilizing autoencoder (AE) and other ML/DL algorithms, including gradient boosting decision tree (GBDT) and multitask deep neural network (MT-DNN), we built predictive models for these targets with 115 407 inhibitors to predict drug repurposing potential and possible side effects. We further screened their absorption, distribution, metabolism, and excretion, and toxicity (ADMET) properties to search for leads having potential for developing treatments for cocaine addiction. Our approach offers a new systematic protocol for artificial intelligence (AI)-based anticocaine addiction lead discovery