Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories

M6A-mediated molecular patterns and tumor microenvironment infiltration characterization in nasopharyngeal carcinoma

N6-methyladenosine (m6A) is the most predominant RNA epigenetic regulation in eukaryotic cells. Numerous evidence revealed that m6A modification exerts a crucial role in the regulation of tumor microenvironment (TME) cell infiltration in several tumors. Nevertheless, the potential role and mechanism of m6A modification in nasopharyngeal carcinoma (NPC) remains unknown. mRNA expression data and clinical information from GSE102349, and GSE53819 datasets obtained from Gene Expression Omnibus (GEO) was used for differential gene expression and subsequent analysis. Consensus clustering was used to identify m6A-related molecular patterns of 88 NPC samples based on prognostic m6A regulators using Univariate Cox analysis. The TME cell-infiltrating characteristics of each m6A-related subclass were explored using single-sample gene set enrichment (ssGSEA) algorithm and CIBERSORT algotithm. DEGs between two m6A-related subclasses were screened using edgeR package. The prognostic signature and predicated nomogram were constructed based on the m6A-related DEGs. The cell infiltration and expression of prognostic signature in NPC was determined using immunohistochemistry (IHC) analysis. Chi-square test was used to analysis the significance of difference of the categorical variables. And survival analysis was performed using Kaplan-Meier plots and log-rank tests. The NPC samples were divided into two m6A-related subclasses. The TME cell-infiltrating characteristics analyses indicated that cluster 1 is characterized by immune-related and metabolism pathways activation, better response to anit-PD1 and anti-CTLA4 treatment and chemotherapy. And cluster 2 is characterized by stromal activation, low expression of HLA family and immune checkpoints, and a worse response to anti-PD1 and anti-CTLA4 treatment and chemotherapy. Furthermore, we identified 1558 DEGs between two m6A-related subclasses and constructed prognostic signatures to predicate the progression-free survival (PFS) for NPC patients. Compared to non-tumor samples, REEP2, TMSB15A, DSEL, and ID4 were upregulated in NPC samples. High expression of REEP2 and TMSB15A showed poor survival in NPC patients. The interaction between REEP2, TMSB15A, DSEL, ID4, and m6A regulators was detected. Our finding indicated that m6A modification plays an important role in the regulation of TME heterogeneity and complexity.

Exploring molecular fragments for fraction unbound in human plasma of chemicals: a fragment-based cheminformatics approach

Fraction unbound in plasma (fu,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for fu,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for fu,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the fu,p values. The molecular fragments may help for the assessment of the fu,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Projecting phytochemical bacoside A anti-mucorale agent: An in-silico and in-vitro assessment

Mucormycosis, a life-threatening fungal infection that primarily affects immunocompromised individuals.The protein family commonly observed in the fugus responsible for causing Mucormycosis. The attachment of spores to host cells surface, facilitated by a protein CotH, is a critical step for the invasion and progression of the disease. Therefore, CotH inhibitors have emerged as a promising therapeutic strategy for treating mucormycosis.This study presents a novel therapeutic target and ligand for controlling the growth of Mucorales. First, to identify potential CotH inhibitors, we surveyed a library antifungal compounds elaborated in AYUSearch database. Next, using machine learning-based algorithms we screend 20 potentials ligands, followed by structure-based molecular modelling and molecular trajectory analysis to identify the three most promising chemical constituents. In-vitro tube assays on selected Mucorales determined the minimum inhibitory concentrations (MIC) for screened chemotypes. The MIC assay revealed that Bacoside inhibits the growth and sporulation at 5 mg/ml concentrations, emerging as a probable CotH inhibitor. Further, the compound's toxicity was evaluated by adding it to the feed of C.elegans, and the finding suggests that the bacoside is reasonably safe at the studied concentration. The findings project bacoside A as a potential anti-mucorale lead compound that can be further validated with preclinical and clinical studies.

Decoding the dynamics of BCL9 triazole stapled peptide

BCL9 is a key protein in Wnt signaling pathway. It acts as a transcriptional co-activator to β-catenin, and dysregulation in this pathway leads to tumor growth. Inhibiting such a protein-protein interaction is considered as a therapeutic challenge. The interaction between β-catenin and BCL9 is facilitated by a 23-residue helical domain from BCL9 and a hydrophobic groove of β-catenin. To prevent this interaction, a peptide that mimics the alpha-helical domain of BCL9 can be designed. Stapling is considered a successful strategy in the pursuit of designing such peptides in which amino acids side are stitched together using chemical moieties. Among the various types of cross-linkers, triazole is the most rapid and effective one synthesized via click reaction. However, the underlying interactions behind maintaining the secondary structure of stapled peptides remain less explored. In the current work, we employed the molecular dynamics simulation to study the conformational behavior of the experimentally synthesized single and double triazole stapled BCL9 peptide. Upon the addition of a triazole staple, there is a significant reduction in the conformational space of BCL9. The helical character of the stapled peptide increases with an increase in separation between the triazole cross-linkers. Also, we encompassed the Replica Exchange with Solute Tempering (REST2) simulation to validate the high-temperature response of the stapled peptide. From REST2, the PCA and t-SNE show the reduction in distinct cluster formation on the addition of triazole staple. Our study infers further development of these triazole-stapled BCL9 peptides into effective inhibitors to target the interaction between β-catenin and BCL9.

Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics

The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system─collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the α-helix, we added the α-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics. This simulation led to 10 folding events in a 200 ns simulation with 32 replicas.

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

The functional diversity of fish assemblages in the vicinity of oil and gas pipelines compared to nearby natural reef and soft sediment habitats

As the offshore hydrocarbon industry matures and decommissioning activities are expected to increase, there is a requirement to assess the environmental consequences of different pipeline decommissioning options. Previous research on fish and other ecological components associated with pipelines has focused on examining species richness, abundance and biomass surrounding structures. The extent to which subsea pipelines mimic or alter ecosystem function compared with nearby natural habitats is unknown. We analyse differences in fish assemblage biological trait composition and the functional diversity at exposed shallow-water subsea pipelines, nearby natural reef and soft sediment habitats, using mini stereo-video remotely operated vehicles (ROV). Habitats significantly differed in assemblage trait composition. The pipeline and reef habitats shared a more similar functional composition and had the presence of key functional groups required for the development and maintenance of healthy coral reef systems. The reef habitat had the greatest functional diversity, followed by the pipeline habitat and soft sediment habitat respectively.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Protein Function Analysis through Machine Learning

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein-ligand binding, including allosteric effects, protein-protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

Collective variable discovery in the age of machine learning: reality, hype and everything in between

Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

Carbon Dot Blinking Fingerprint Uncovers Native Membrane Receptor Organizations via Deep Learning

Oligomeric organization of G protein-coupled receptors is proposed to regulate receptor signaling and function, yet rapid and precise identification of the oligomeric status especially for native receptors on a cell membrane remains an outstanding challenge. By using blinking carbon dots (CDs), we now develop a deep learning (DL)-based blinking fingerprint recognition method, named deep-blinking fingerprint recognition (BFR), which allows automatic classification of CD-labeled receptor organizations on a cell membrane. This DL model integrates convolutional layers, long-short-term memory, and fully connected layers to extract time-dependent blinking features of CDs and is trained to a high accuracy (∼95%) for identifying receptor organizations. Using deep blinking fingerprint recognition, we found that CXCR4 mainly exists as 87.3% monomers, 12.4% dimers, and <1% higher-order oligomers on a HeLa cell membrane. We further demonstrate that the heterogeneous organizations can be regulated by various stimuli at different degrees. The receptor-binding ligands, agonist SDF-1α and antagonist AMD3100, can induce the dimerization of CXCR4 to 33.1 and 20.3%, respectively. In addition, cytochalasin D, which inhibits actin polymerization, similarly prompts significant dimerization of CXCR4 to 30.9%. The multi-pathway organization regulation will provide an insight for understanding the oligomerization mechanism of CXCR4 as well as for elucidating their physiological functions.

CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting

Molecular dynamics (MD) simulations are an exceedingly and increasingly potent tool for molecular behavior prediction and analysis. However, the enormous wealth of data generated by these simulations can be difficult to process and render in a human-readable fashion. Cluster analysis is a commonly used way to partition data into structurally distinct states. We present a method that improves on the state of the art by taking advantage of the temporal information of MD trajectories to enable more accurate clustering at a lower memory cost. To date, cluster analysis of MD simulations has generally treated simulation snapshots as a mere collection of independent data points and attempted to separate them into different clusters based on structural similarity. This new method, cluster analysis of trajectories based on segment splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying the method to a synthetic toy model as well as four real-life data sets–trajectories of MD simulations of alanine dipeptide and valine dipeptide as well as two fast-folding proteins–we find CATBOSS to be robust and highly performant, yielding natural-looking cluster boundaries and greatly improving clustering resolution. As the classification of points into segments emphasizes density gaps in the data by grouping them close to the state means, CATBOSS applied to the valine dipeptide system is even able to account for a degree of freedom deliberately omitted from the input data set. We also demonstrate the potential utility of CATBOSS in distinguishing metastable states from transition segments as well as promising application to cases where there is little or no advance knowledge of intrinsic coordinates, making for a highly versatile analysis tool.

Single-Cell Proteomic Profiling Identifies Nanoparticle Enhanced Therapy for Triple Negative Breast Cancer Stem Cells

Breast cancer remains a major cause of cancer-related deaths in women worldwide. Chemotherapy-promoted stemness and enhanced stem cell plasticity in breast cancer is a cause for great concern. The discovery of drugs targeting BCSCs was suggested to be an important advancement in the establishment of therapy that improves the efficacy of chemotherapy. In this work, by using single-cell mass cytometry, we observed that stemness in spheroid-forming cells derived from MDA-MB-231 cells was significantly increased after doxorubicin administration and up-regulated integrin αvβ3 expression was also observed. An RGD-included nanoparticle (CS-V) was designed, and it was found that it could promote doxorubicin’s efficacy against MDA-MB-231 spheroid cells. The above observations suggested that the combination of RGD-included nanoparticles (CS-V) with the chemo-drug doxorubicin could be developed as a potential therapy for breast cancer.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling

Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.

Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments

Pepsin-like aspartic proteases (PAPs) are a class of aspartic proteases which shares tremendous structural similarity with human pepsin. One of the key structural features of PAPs is the presence of a β-hairpin motif otherwise known as flap. The biological function of the PAPs is highly dependent on the conformational dynamics of the flap region. In apo PAPs, the conformational dynamics of the flap is dominated by the rotational degrees of freedom associated with χ1 and χ2 angles of conserved Tyr (or Phe in some cases). However it is plausible that dihedral order parameters associated with several other residues might play crucial roles in the conformational dynamics of apo PAPs. Due to their size, complexities associated with conformational dynamics and clinical significance (drug targets for malaria, Alzheimer's disease etc.), PAPs provide a challenging testing ground for computational and experimental methods focusing on understanding conformational dynamics and molecular recognition in biomolecules. The opening of the flap region is necessary to accommodate substrate/ligand in the active site of the PAPs. The BIG challenge is to gain atomistic details into how reversible ligand binding/unbinding (molecular recognition) affects the conformational dynamics. Recent reports of kinetics (K i, K d) and thermodynamic parameters (ΔH, TΔS, and ΔG) associated with macro-cyclic ligands bound to BACE1 (belongs to PAP family) provide a perfect challenge (how to deal with big ligands with multiple torsional angles and select optimum order parameters to study reversible ligand binding/unbinding) for computational methods to predict binding free energies and kinetics beyond typical test systems e.g. benzamide-trypsin. In this work, i reviewed several order parameters which were proposed to capture the conformational dynamics and molecular recognition in PAPs. I further highlighted how machine learning methods can be used as order parameters in the context of PAPs. I then proposed some open ideas and challenges in the context of molecular simulation and put forward my case on how biophysical experiments e.g. NMR, time-resolved FRET etc. can be used in conjunction with biomolecular simulation to gain complete atomistic insights into the conformational dynamics of PAPs.

Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels

Halohydrin dehalogenases (HHDH) are industrially relevant biocatalysts exhibiting a promiscuous epoxide-ring opening reactivity in the presence of small nucleophiles, thus giving access to novel carbon–carbon, carbon–oxygen, carbon–nitrogen, and carbon–sulfur bonds. Recently, the repertoire of HHDH has been expanded, providing access to some novel HHDH subclasses exhibiting a broader epoxide substrate scope. In this work, we develop a computational approach based on the application of linear and non-linear dimensionality reduction techniques to long time-scale Molecular Dynamics (MD) simulations to study the HHDH conformational landscapes. We couple the analysis of the conformational landscapes to CAVER calculations to assess their impact on the active site tunnels and potential ability towards bulky epoxide ring opening reaction. Our study indicates that the analyzed HHDHs subclasses share a common breathing motion of the halide binding pocket, but present large deviations in the loops adjacent to the active site pocket and N-terminal regions. Such conformational differences affect the available tunnels for epoxide binding to the active site. The superior activity of the HHDH G subclass towards bulkier substrates is explained by the additional structural elements delimiting the active site region, its rich conformational heterogeneity, and the substantially wider and frequently observed active site tunnels. This study therefore provides key information for HHDH promiscuity and engineering.

Protein Conformational States-A First Principles Bayesian Method

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.