Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

Computational methods and theory for ion channel research

Ion channels are fundamental biological devices that act as gates in order to ensure selective ion transport across cellular membranes; their operation constitutes the molecular mechanism through which basic biological functions, such as nerve signal transmission and muscle contraction, are carried out. Here, we review recent results in the field of computational research on ion channels, covering theoretical advances, state-of-the-art simulation approaches, and frontline modeling techniques. We also report on few selected applications of continuum and atomistic methods to characterize the mechanisms of permeation, selectivity, and gating in biological and model channels.

Fully Flexible Ligand Docking for the P2X7 Receptor Using ROSIE

The availability of P2X7 receptor structures with allosteric antagonists bound enables us to predict specific interactions between receptor and antagonists at atomistic detail. In this chapter we outline how modern ligand docking techniques can be employed by the nonexpert to predict putative binding modes for known or hypothetical allosteric P2X7 antagonists.

Probing Interplays between Human XBP1u Translational Arrest Peptide and 80S Ribosome

The ribosome stalling mechanism is a crucial biological process, yet its atomistic underpinning is still elusive. In this framework, the human XBP1u translational arrest peptide (AP) plays a central role in regulating the unfolded protein response (UPR) in eukaryotic cells. Here, we report multimicrosecond all-atom molecular dynamics simulations designed to probe the interactions between the XBP1u AP and the mammalian ribosome exit tunnel, both for the wild type AP and for four mutant variants of different arrest potencies. Enhanced sampling simulations allow investigating the AP release process of the different variants, shedding light on this complex mechanism. The present outcomes are in qualitative/quantitative agreement with available experimental data. In conclusion, we provide an unprecedented atomistic picture of this biological process and clear-cut insights into the key AP–ribosome interactions.

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Phenylethanoid glycosides as a possible COVID-19 protease inhibitor: a virtual screening approach

From the beginning of pandemic, more than 240 million people have been infected with a death rate higher than 2%. Indeed, the current exit strategy involving the spreading of vaccines must be combined with progress in effective treatment development. This scenario is sadly supported by the vaccine's immune activation time and the inequalities in the global immunization schedule. Bringing the crises under control means providing the world population with accessible and impactful new therapeutics. We screened a natural product library that contains a unique collection of 2370 natural products into the binding site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). According to the docking score and to the interaction at the active site, three phenylethanoid glycosides (forsythiaside A, isoacteoside, and verbascoside) were selected. In order to provide better insight into the atomistic interaction and test the impact of the three selected compounds at the binding site, we resorted to a half microsecond-long molecular dynamics simulation. As a result, we are showing that forsythiaside A is the most stable molecule and it is likely to possess the highest inhibitory effect against SARS-CoV-2 Mpro. Phenylethanoid glycosides also have been reported to have both protease and kinase activity. This kinase inhibitory activity is very beneficial in fighting viruses inside the body as kinases are required for viral entry, metabolism, and/or reproduction. The dual activity (kinase/protease) of phenylethanoid glycosides makes them very promising anit-COVID-19 agents.

Exploring the binding mechanism of positive allosteric modulators in human metabotropic glutamate receptor 2 using molecular dynamics simulations

Positive allosteric modulators (PAMs) of human metabotropic glutamate receptor 2 (hmGlu₂) are well-known in the treatment of psychiatric disorders for their higher selectivity and lower tolerance risk. A variety of PAMs have been reported over the last decade and two compounds were in Phase II clinical trials for schizophrenia and anxiety. These trials were discontinued on account of the unsatisfactory therapeutic efficacy, but PAMs were explored as novel treatments for addiction and epilepsy. Thus, it is still important to explore novel hmGlu₂ PAMs in the near future. Nowadays, the challenges in optimizing drug potency and improving scaffold diversity for PAMs are the noncomprehensive character analyses of multiple scaffolds; the exploration of the binding modes of PAMs in the allosteric binding site have been proposed to reduce this difficulty. However, there has been no comprehensive research about the binding profiles of PAMs in the hmGlu₂ receptor. To address this issue, this work explores the binding characters of eight PAMs representing five chemical series by multiple computational methods. As a result, the shared binding modes of the eight studied PAMs interacting with 15 residues in the allosteric binding site were defined. In addition, the reduced hydrophobicity with low electronegativity of R₁, increased hydrophobicity with low negative electron density of R₂ and the electronegativity of the linker were identified as indicators that regulate the affinity of PAMs. This finding agrees well with the physicochemical properties of reported multiple series PAMs. This comprehensive work sheds additional light on the binding mechanism and physicochemical regularity underlining PAMs affinity and could be further utilized as a structural and energetic blueprint for discovering and assessing novel PAMs for hmGlu₂.

Principles of Small-Molecule Transport through Synthetic Nanopores

Synthetic nanopores made from DNA replicate the key biological processes of transporting molecular cargo across lipid bilayers. Understanding transport across the confined lumen of the nanopores is of fundamental interest and of relevance to their rational design for biotechnological applications. Here we reveal the transport principles of organic molecules through DNA nanopores by synergistically combining experiments and computer simulations. Using a highly parallel nanostructured platform, we synchronously measure the kinetic flux across hundreds of individual pores to obtain rate constants. The single-channel transport kinetics are close to the theoretical maximum, while selectivity is determined by the interplay of cargo charge and size, the pores' sterics and electrostatics, and the composition of the surrounding lipid bilayer. The narrow distribution of transport rates implies a high structural homogeneity of DNA nanopores. The molecular passageway through the nanopore is elucidated via coarse-grained constant-velocity steered molecular dynamics simulations. The ensemble simulations pinpoint with high resolution and statistical validity the selectivity filter within the channel lumen and determine the energetic factors governing transport. Our findings on these synthetic pores' structure-function relationship will serve to guide their rational engineering to tailor transport selectivity for cell biological research, sensing, and drug delivery.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation

Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein-ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient's genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

Uncertainty quantification in classical molecular dynamics

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand-protein binding free energy estimation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Computational study on the allosteric mechanism of Leishmania major IF4E-1 by 4E-interacting protein-1: Unravelling the determinants of m7GTP cap recognition

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Atomistic details on perturbations induced by Lm4E-IP1 binding are described.

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The modulation of LmIF4E-1 affinity for the cap is confirmed by energetic analyses.

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Signaling paths between the allosteric and othosteric sites of LmIF4E-1 are predicted.

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Lm4E-IP1 binding increases the side-chain entropy of W83 and R172 of LmIF4E-1.

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A mechanism of dynamic allostery is proposed for the regulation mediated by Lm4E-IP1.

During their life cycle, Leishmania parasites display a fine-tuned regulation of the mRNA translation through the differential expression of isoforms of eukaryotic translation initiation factor 4E (LeishIF4Es). The interaction between allosteric modulators such as 4E-interacting proteins (4E-IPs) and LeishIF4E affects the affinity of this initiation factor for the mRNA cap. Here, several computational approaches were employed to elucidate the molecular bases of the previously-reported allosteric modulation in L. major exerted by 4E-IP1 (Lm4E-IP1) on eukaryotic translation initiation factor 4E 1 (LmIF4E-1). Molecular dynamics (MD) simulations and accurate binding free energy calculations (ΔG_bind) were combined with network-based modeling of residue-residue correlations. We also describe the differences in internal motions of LmIF4E-1 apo form, cap-bound, and Lm4E-IP1-bound systems. Through community network calculations, the differences in the allosteric pathways of allosterically-inhibited and active forms of LmIF4E-1 were revealed. The ΔG_bind values show significant differences between the active and inhibited systems, which are in agreement with the available experimental data. Our study thoroughly describes the dynamical perturbations of LmIF4E-1 cap-binding site triggered by Lm4E-IP1. These findings are not only essential for the understanding of a critical process of trypanosomatids’ gene expression but also for gaining insight into the allostery of eukaryotic IF4Es, which could be useful for structure-based design of drugs against this protein family.

TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing

The TIES (Thermodynamic Integration with Enhanced Sampling) protocol is a formally exact alchemical approach in computational chemistry to the calculation of relative binding free energies. The validity of TIES relies on the correctness of matching atoms across compared pairs of ligands, laying the foundation for the transformation along an alchemical pathway. We implement a flexible topology superimposition algorithm which uses an exhaustive joint-traversal for computing the largest common component(s). The algorithm is employed to enable matching and morphing of partial rings in the TIES protocol along with a validation study using 55 transformations and five different proteins from our previous work. We find that TIES 20 with the RESP charge system, using the new superimposition algorithm, reproduces the previous results with mean unsigned error of 0.75 kcal/mol with respect to the experimental data. Enabling the morphing of partial rings decreases the size of the alchemical region in the dual-topology transformations resulting in a significant improvement in the prediction precision. We find that increasing the ensemble size from 5 to 20 replicas per λ window only has a minimal impact on the accuracy. However, the non-normal nature of the relative free energy distributions underscores the importance of ensemble simulation. We further compare the results with the AM1-BCC charge system and show that it improves agreement with the experimental data by slightly over 10%. This improvement is partly due to AM1-BCC affecting only the charges of the atoms local to the mutation, which translates to even fewer morphed atoms, consequently reducing issues with sampling and therefore ensemble averaging. TIES 20, in conjunction with the enablement of ring morphing, reduces the size of the alchemical region and significantly improves the precision of the predicted free energies.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

Large-scale binding affinity calculations on commodity compute clouds

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods.