Abstract:Protein-substrate recognition is highly dynamic and complex process in nature. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of substrate in its act of binding to its designated protein cavity. Towards this end, microsecond long atomistic molecular dynamics (MD) simulation has recently emerged as a popular method of choice, due its ability to record these events at high spatial and temporal resolution. However, success in this approach comes at… Show more
“…S1 and Ref ( Capelli et al., 2019 ).). Two additional pathways, CF and EJ, were observed with low populations in several enhanced sampling simulations ( Capelli et al., 2019 ; Dandekar and Mondal, 2020 ; Nunes-Alves et al., 2018 ; Rydzewski and Valsson, 2019 ) but were not observed here for benzene although the CF pathway was observed for indole (see Fig. S5 ).…”
Section: Resultsmentioning
confidence: 57%
“…Remarkably, no less than thirteen computational studies have been published since 2018 by different research groups in which methods based on MD simulation were used to identify paths from a buried cavity to the T4L exterior and to try to characterize the ligand binding and unbinding processes energetically and kinetically (see Fig. 1 , review Nunes-Alves et al., 2020 and recent papers of Capelli et al., 2019 ; Dandekar and Mondal, 2020 ; Feher et al., 2019 ; Lamim Ribeiro and Tiwary, 2019 ; Lotz and Dickson, 2020 ; Mondal et al., 2018 ; Niitsu et al., 2019 ; Nunes-Alves et al., 2018 ; Rydzewski, 2020 ; Rydzewski and Valsson, 2019 ; Souza et al., 2020 ; Wang et al, 2018 , 2019 ). Fig.…”
Section: Introductionmentioning
confidence: 99%
“…(B) Ligand binding and unbinding routes observed in recent computational studies are indicated by circles colored by simulation type. Methods: MetaD_1 ( Capelli et al., 2019 ) and MetaD_2 ( Wang et al., 2016 ) - metadynamics, Maze ( Rydzewski and Valsson, 2019 ) , CG_1 ( Dandekar and Mondal, 2020 ) and CG_2 ( Souza et al., 2020 ) - coarse-grained, WE MD ( Nunes-Alves et al., 2018 ) – weighted ensemble MD, aMD ( Feher et al., 2019 ) - accelerated MD, RAVE ( Lamim Ribeiro and Tiwary, 2019 ) - Reweighted autoencoded variational Bayes for enhanced sampling; PFIb ( Wang et al., 2019 ) - Past-future information bottleneck, MD_MSM ( Mondal et al., 2018 ) – conventional MD and Markov State Model, gREST ( Niitsu et al., 2019 ) - generalized replica exchange with solute tempering, GaMD ( Miao et al., 2015 ) – Gaussian accelerated MD. …”
The protein-ligand residence time, τ, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict τ, computational methods must overcome the problem that τ often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the τ-Random Acceleration Molecular Dynamics (τRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with small, engineered binding cavities. τRAMD yields relative ligand dissociation rates in good accordance with experiments across this diverse set of complexes that differ with regard to measurement temperature, ligand identity, protein mutation and binding cavity. τRAMD thereby allows a comprehensive characterization of the ligand egress routes and determinants of τ. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of τ. We also find that the presence of a greater number of metastable states along egress pathways leads to slower protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.
“…S1 and Ref ( Capelli et al., 2019 ).). Two additional pathways, CF and EJ, were observed with low populations in several enhanced sampling simulations ( Capelli et al., 2019 ; Dandekar and Mondal, 2020 ; Nunes-Alves et al., 2018 ; Rydzewski and Valsson, 2019 ) but were not observed here for benzene although the CF pathway was observed for indole (see Fig. S5 ).…”
Section: Resultsmentioning
confidence: 57%
“…Remarkably, no less than thirteen computational studies have been published since 2018 by different research groups in which methods based on MD simulation were used to identify paths from a buried cavity to the T4L exterior and to try to characterize the ligand binding and unbinding processes energetically and kinetically (see Fig. 1 , review Nunes-Alves et al., 2020 and recent papers of Capelli et al., 2019 ; Dandekar and Mondal, 2020 ; Feher et al., 2019 ; Lamim Ribeiro and Tiwary, 2019 ; Lotz and Dickson, 2020 ; Mondal et al., 2018 ; Niitsu et al., 2019 ; Nunes-Alves et al., 2018 ; Rydzewski, 2020 ; Rydzewski and Valsson, 2019 ; Souza et al., 2020 ; Wang et al, 2018 , 2019 ). Fig.…”
Section: Introductionmentioning
confidence: 99%
“…(B) Ligand binding and unbinding routes observed in recent computational studies are indicated by circles colored by simulation type. Methods: MetaD_1 ( Capelli et al., 2019 ) and MetaD_2 ( Wang et al., 2016 ) - metadynamics, Maze ( Rydzewski and Valsson, 2019 ) , CG_1 ( Dandekar and Mondal, 2020 ) and CG_2 ( Souza et al., 2020 ) - coarse-grained, WE MD ( Nunes-Alves et al., 2018 ) – weighted ensemble MD, aMD ( Feher et al., 2019 ) - accelerated MD, RAVE ( Lamim Ribeiro and Tiwary, 2019 ) - Reweighted autoencoded variational Bayes for enhanced sampling; PFIb ( Wang et al., 2019 ) - Past-future information bottleneck, MD_MSM ( Mondal et al., 2018 ) – conventional MD and Markov State Model, gREST ( Niitsu et al., 2019 ) - generalized replica exchange with solute tempering, GaMD ( Miao et al., 2015 ) – Gaussian accelerated MD. …”
The protein-ligand residence time, τ, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict τ, computational methods must overcome the problem that τ often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the τ-Random Acceleration Molecular Dynamics (τRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with small, engineered binding cavities. τRAMD yields relative ligand dissociation rates in good accordance with experiments across this diverse set of complexes that differ with regard to measurement temperature, ligand identity, protein mutation and binding cavity. τRAMD thereby allows a comprehensive characterization of the ligand egress routes and determinants of τ. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of τ. We also find that the presence of a greater number of metastable states along egress pathways leads to slower protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.
“…3 Due to the periodic upgrades in computer hardwares 4,5 and GPUs 6 ligand-recognition in complex solvent-inaccessible cavities of multiple proteins are now regularly being simulated with success. 7–10 These ligand-recognition simulation trajectories, in combination with the framework of Markov state model (MSM), 11,12 have served as key resources for an atomic-level characterization of ligand-recognition pathways 9,10,13,14 and discovery of crucial non-native metastable 13,15 ligand-bound protein conformations.…”
A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. In particular, we consider multi-microsecond long Molecular dynamics simulation trajectories of ligand recognition process in solvent-inaccessible cavity of two archtypal systems: L99A mutant of T4 Lysozyme and Cytochrome P450. We first show that if the substrate-recognition occurs via long-lived intermediate, the protein conformations can be automatically classified into substrate-bound and unbound state through an unsupervised dimensionality reduction technique. On the contrary, if the recognition process is mediated by selection of transient protein conformation by the ligand, a clear correspondence between protein conformation and binding-competent macrostates can only be established via a combination of supervised machine learning (ML) and unsupervised dimension reduction approach.
In such scenario, we demonstrate that a priori random forest based supervised classification of the simulated trajectories recognition process would help characterize key amino-acid residue-pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction via time-lagged independent component analysis of the selected residue-pairs would delineate a conformational landscape of protein which is able to demarcate ligand-bound pose from the unbound ones. As a key breakthrough, the ML-based protocol would identify distal protein locations which would be allosterically important for ligand binding and characterise their roles in recognition pathways.
“…Although Martini-based CG MD simulations have been used to study a wide range of biomolecular processes, examples of protein-ligand binding are still scarce (Negami et al, 2014(Negami et al, , 2020Delort et al, 2017;Ferré et al, 2019;Jiang and Zhang, 2019;Dandekar and Mondal, 2020). Studies of protein-protein interactions are more common, although usually restricted to membrane environments (Baaden and Marrink, 2013;Castillo et al, 2013;Lelimousin et al, 2016;Sun et al, 2020).…”
Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.
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