Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network

Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.

Molecular Dynamics and Machine Learning reveal distinguishing mechanisms of Competitive Ligands to perturb α,β-Tubulin

The mechanisms of action of ligands competing for the Colchicine Binding Site (CBS) of the α,β-Tubulin are non-standard compared to the commonly witnessed ligand-induced inhibition of proteins. This is because their potencies are not solely judged by the binding affinity itself, but also by their capacity to bias the conformational states of the dimer. Regarding the latter requirement, it is observed that ligands competing for the same pocket that binds colchicine exhibit divergence in potential clinical outcomes. Molecular dynamics-based ∼5.2 µs sampling of α,β-Tubulin complexed with four different ligands has revealed that each ligand has its customized way of influencing the complex. Primarily, it is the proportion of twisting and/or bending characteristic of modes of the intrinsic dynamics which is revealed to be 'fundamental' to tune this variation in the mechanism. The milder influence of 'bending' makes a ligand (TUB092), better classifiable under the group of vascular disrupting agents (VDAs), which are phenotypically effective on cytoskeletons; whereas a stronger impact of 'bending' makes the classical ligand Colchicine (COL) a better Anti-Mitotic agent (AMA). Two other ligands BAL27862 (2RR) and Nocodazole (NZO) fall in the intermediate zone as they fail to explicitly induce bending modes. Random Forest Classification method and K-means Clustering is applied to reveal the efficiency of Machine Learning methods in classifying the Tubulin conformations according to their ligand-specific perturbations and to highlight the significance of specific amino acid residues, mostly positioned in the α-β and β-β interfaces involved in the mechanism. These key residues responsible to yield discriminative actions of the ligands are likely to be highly useful in future endeavours to design more precise inhibitors.

High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.

UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study

Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free-energy landscapes. However, the inherently high dimensionality of a protein free-energy landscape poses a challenge to deciphering how proteins perform their functions. For this reason, dimensionality reduction is an active field of research for molecular biologists. The uniform manifold approximation and projection (UMAP) is a dimensionality reduction method based on a fuzzy topological analysis of data. In the present study, the performance of UMAP is compared with that of other popular dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), and time-structure independent components analysis (tICA) in the context of analyzing molecular dynamics simulations of the circadian clock protein VIVID. A good dimensionality reduction method should accurately represent the data structure on the projected components. The comparison of the raw high-dimensional data with the projections obtained using different dimensionality reduction methods based on various metrics showed that UMAP has superior performance when compared with linear reduction methods (PCA and tICA) and has competitive performance and scalable computational cost.

Theoretical Study on the Mechanism of the Acylate Reaction of β-Lactamase

Using density functional theory and a cluster approach, we study the reaction potential surface and compute Gibbs free energies for the acylate reaction of β-lactamase with penicillin G, where the solvent effect is important and taken into consideration. Two reaction paths are investigated: one is a multi-step process with a rate-limit energy barrier of 19.1 kcal/mol, which is relatively small, and the reaction can easily occur; the other is a one-step process with a barrier of 45.0 kcal/mol, which is large and thus makes the reaction hard to occur. The reason why the two paths have different barriers is explained.

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Deciphering the Allosteric Process of the Phaeodactylum tricornutum Aureochrome 1a LOV Domain

The conformational-driven allosteric protein diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a) differs from other light-oxygen-voltage (LOV) proteins for its uncommon structural topology. The mechanism of signaling transduction in the PtAu1a LOV domain (AuLOV) including flanking helices remains unclear because of this dissimilarity, which hinders the study of PtAu1a as an optogenetic tool. To clarify this mechanism, we employed a combination of tree-based machine learning models, Markov state models, machine-learning-based community analysis, and transition path theory to quantitatively analyze the allosteric process. Our results are in good agreement with the reported experimental findings and reveal a previously overlooked Cα helix and protein linkers as important in promoting the protein conformational changes. This integrated approach can be considered as a general workflow and applied on other allosteric proteins to provide detailed information about their allosteric mechanisms.

ivis Dimensionality Reduction Framework for Biomacromolecular Simulations

Molecular dynamics (MD) simulations have been widely applied to study macromolecules including proteins. However, the high dimensionality of the data sets produced by simulations makes thorough analysis difficult and further hinders a deeper understanding of biomacromolecules. To gain more insights into the protein structure-function relations, appropriate dimensionality reduction methods are needed to project simulations onto low-dimensional spaces. Linear dimensionality reduction methods, such as principal component analysis (PCA) and time-structure-based independent component analysis (t-ICA), could not preserve sufficient structural information. Though better than linear methods, nonlinear methods, such as t-distributed stochastic neighbor embedding (t-SNE), still suffer from the limitations in avoiding system noise and keeping inter-cluster relations. ivis is a novel deep learning-based dimensionality reduction method originally developed for single-cell data sets. Here, we applied this framework for the study of light, oxygen, and voltage (LOV) domains of diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a). Compared with other methods, ivis is shown to be superior in constructing a Markov state model (MSM), preserving information of both local and global distances, and maintaining similarity between high and low dimensions with the least information loss. Moreover, the ivis framework is capable of providing new perspectives for deciphering residue-level protein allostery through the feature weights in the neural network. Overall, ivis is a promising member of the analysis toolbox for proteins.

Deciphering the protein motion of S1 subunit in SARS-CoV-2 spike glycoprotein through integrated computational methods

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major worldwide public health emergency that has infected over 8 million people. Spike glycoprotein, especially the partially open state of S1 subunit, in SARS-CoV-2 is considered vital for its infection with human host cell. However, the mechanism elucidating the transition from the closed state to the partially open state still remains unclear. In this study, we applied a series of computational methods, including Markov state model, transition path theory and random forest to analyze the S1 motion. Our results showed a promising complete conformational movement of the receptor-binding domain, from buried, partially open, to detached states. We also estimated the transition probability among these states. Based on the asymmetry in both the dynamics behavior and the accumulated alpha carbon (Cα) importance, we further suggested a relation among chains in the trimer spike protein, which leads to a deeper understanding on protein motions of the S1 subunit.

Communicated by Ramaswamy H. Sarma

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Dynamical Behavior of β-Lactamases and Penicillin- Binding Proteins in Different Functional States and Its Potential Role in Evolution

β-Lactamases are enzymes produced by bacteria to hydrolyze β-lactam-based antibiotics, and pose serious threat to public health through related antibiotic resistance. Class A β-lactamases are structurally and functionally related to penicillin-binding proteins (PBPs). Despite the extensive studies of the structures, catalytic mechanisms and dynamics of both β-lactamases and PBPs, the potentially different dynamical behaviors of these proteins in different functional states still remain elusive in general. In this study, four evolutionarily related proteins, including TEM-1 and TOHO-1 as class A β-lactamases, PBP-A and DD-transpeptidase as two PBPs, are subjected to molecular dynamics simulations and various analyses to characterize their dynamical behaviors in different functional states. Penicillin G and its ring opening product serve as common ligands for these four proteins of interest. The dynamic analyses of overall structures, the active sites with penicillin G, and three catalytically important residues commonly shared by all four proteins reveal unexpected cross similarities between Class A β-lactamases and PBPs. These findings shed light on both the hidden relations among dynamical behaviors of these proteins and the functional and evolutionary relations among class A β-lactamases and PBPs.