MDAnalysis 2.0 and beyond: fast and interoperable, community driven simulation analysis

Naughton, Fiona B.; Alibay, Irfan; Barnoud, Jonathan; Barreto-Ojeda, Estefania; Beckstein, Oliver; Bouysset, Cédric; Cohen, Orion; Gowers, Richard; MacDermott-Opeskin, Hugo; Matta, Micaela; Melo, Manuel N.; Reddy, Tyler; Wang, Lily; Zhuang, Yinghong

doi:10.1016/j.bpj.2021.11.1368

Cited by 19 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the tradeoff between convergence and simulation cost, all single asphaltene model simulations were performed with 40, 100, and 200 asphaltene molecules, maintaining a concentration of 7 wt%. The aggregation state of asphaltenic systems is monitored consistently with previous works: the series of observables defined by Headen et al 34 have been implemented in a homemade Python script using the package MDAnalysis version 2.0 46 and are defined in section 4.3 . The aggregation number, g n , which corresponds to the number of molecules per aggregate, also referred to as clusters, permits monitoring the equilibrium state of the system while estimating the size of a cluster of asphaltenes.…”

Section: Resultsmentioning

confidence: 99%

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Pétuya¹,

Punase

Bosoni³

et al. 2023

ACS Omega

View full text Add to dashboard Cite

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin.

show abstract

Section: Resultsmentioning

confidence: 99%

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Pétuya¹,

Punase

Bosoni³

et al. 2023

ACS Omega

View full text Add to dashboard Cite

show abstract

“…The state of W2 is similar to P2, P3, and P4 states. The conformation ensemble similarity among these two systems was calculated by the “harmonic ensemble similarity” method ,, based on Kullback–Leibler divergence. W0 is similar to P1, W1 is similar to P0, and W2 corresponds to P4 (Figure , and S12).…”

Section: Discussionmentioning

confidence: 99%

Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model

Hong

Song

Rahman

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Leukocyte adhesion deficiency-1 (LAD-1) disorder is a severe immunodeficiency syndrome caused by deficiency or mutation of β2 integrin. The phosphorylation on threonine 758 of β2 integrin acts as a molecular switch inhibiting the binding of filamin. However, the switch mechanism of site-specific phosphorylation at the atom level is still poorly understood. To resolve the regulation mechanism, all-atom molecular dynamics simulation and Markov state model were used to study the dynamic regulation pathway of phosphorylation. Wild type system possessed lower binding free energy and fewer number of states than the phosphorylated system. Both systems underwent local disorder-to-order conformation conversion when achieving steady states. To reach steady states, wild type adopted less number of transition paths/shortest path according to the transition path theory than the phosphorylated system. The underlying phosphorylated regulation pathway was from P1 to P0 and then P4 state, and the main driving force should be hydrogen bond and hydrophobic interaction disturbing the secondary structure of phosphorylated states. These studies will shed light on the pathogenesis of LAD-1 disease and lay a foundation for drug development.

show abstract

“…Here, we used the AlphaFold2 protein structure database to get the 3D structures for each of the proteins used in the KIBA and Davis data sets. We downloaded the 3D structures in PDB format and used MDAnalysis version 2.0.0 and NetworkX version 2.8.4 to compute the contact map from the 3D structure. The pairwise C α distances were calculated for each given structure, and two residues were said to be in contact if their distance was less than 8 Å. , …”

Section: Methodsmentioning

confidence: 99%

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Gorantla,

Kubincová,

Weiße

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Accurate in silico prediction of protein−ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.

show abstract

MDAnalysis 2.0 and beyond: fast and interoperable, community driven simulation analysis

Cited by 19 publications

References 0 publications

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Contact Info

Product

Resources

About