Supraspinal control of motoneurons after paralysis enabled by spinal cord stimulation.

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

SUPRASPINAL CONTROL OF MOTONEURONS AFTER PARALYSIS ENABLED BY SPINAL CORD STIMULATION.

Spinal cord stimulation (SCS) restores motor control after spinal cord injury (SCI) and stroke. This evidence led to the hypothesis that SCS facilitates residual supraspinal inputs to spinal motoneurons. Instead, here we show that SCS does not facilitate residual supraspinal inputs but directly triggers motoneurons action potentials. However, supraspinal inputs can shape SCS-mediated activity, mimicking volitional control of motoneuron firing. Specifically, by combining simulations, intraspinal electrophysiology in monkeys and single motor unit recordings in humans with motor paralysis, we found that residual supraspinal inputs transform subthreshold SCS-induced excitatory postsynaptic potentials into suprathreshold events. We then demonstrated that only a restricted set of stimulation parameters enables volitional control of motoneuron firing and that lesion severity further restricts the set of effective parameters. Our results explain the facilitation of voluntary motor control during SCS while predicting the limitations of this neurotechnology in cases of severe loss of supraspinal axons.

Which 2D Material is Better for DNA Detection: Graphene, MoS2, or MXene?

Despite much research on characterizing 2D materials for DNA detection with nanopore technology, a thorough comparison between the performance of different 2D materials is currently lacking. In this work, using extensive molecular dynamics simulations, we compare nanoporous graphene, MoS2 and titanium carbide MXene (Ti3C2) for their DNA detection performance and sensitivity. The ionic current and residence time of DNA are characterized in each nanoporous materials by performing hundreds of simulations. We devised two statistical measures including the Kolmogorov-Smirnov test and the absolute pairwise difference to compare the performance of nanopores. We found that graphene nanopore is the most sensitive membrane for distinguishing DNA bases. The MoS2 is capable of distinguishing the A and T bases from the C and G bases better than graphene and MXene. Physisorption and the orientation of DNA in nanopores are further investigated to provide molecular insight into the performance characteristics of different nanopores.

Engineering rotating apical-out airway organoid for assessing respiratory cilia motility.

Motile cilia project from the airway apical surface and directly interface with inhaled external environment. Owing to cilia's nanoscale dimension and high beating frequency, quantitative assessment of their motility remains a sophisticated task. Here we described a robust approach for reproducible engineering of apical-out airway organoid (AOAO) from a defined number of cells. Propelled by exterior-facing cilia beating, the mature AOAO exhibited stable rotational motion when surrounded by Matrigel. We developed a computational framework leveraging computer vision algorithms to quantify AOAO rotation and correlated it with the direct measurement of cilia motility. We further established the feasibility of using AOAO rotation to recapitulate and measure defective cilia motility caused by chemotherapy-induced toxicity and by CCDC39 mutations in cells from patients with primary ciliary dyskinesia. We expect our rotating AOAO model and the associated computational pipeline to offer a generalizable framework to expedite the modeling of and therapeutic development for genetic and environmental ciliopathies.

Prediction of GPCR activity using machine learning.

GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively describe the important structural features in the GPCR and correlate them to the activation state of GPCR. In this study, we developed 3 ML approaches to predict the conformation state of GPCR proteins. Additionally, we predict the activity level of GPCRs based on their structure. We leverage the unique advantages of each of the 3 ML approaches, interpretability of XGBoost, minimal feature engineering for 3D convolutional neural network, and graph representation of protein structure for graph neural network. By using these ML approaches, we are able to predict the activation state of GPCRs with high accuracy (91%-95%) and also predict the activation state of GPCRs with low error (MAE of 7.15-10.58). Furthermore, the interpretation of the ML approaches allows us to determine the importance of each of the features in distinguishing between the GPCRs conformations.

Graph neural networks accelerated molecular dynamics.

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale simulations with femtosecond integration is very expensive. In each MD step, numerous iterative computations are performed to calculate energy based on different types of interaction and their corresponding spatial gradients. These repetitive computations can be learned and surrogated by a deep learning model, such as a Graph Neural Network (GNN). In this work, we developed a GNN Accelerated MD (GAMD) model that directly predicts forces, given the state of the system (atom positions, atom types), bypassing the evaluation of potential energy. By training the GNN on a variety of data sources (simulation data derived from classical MD and density functional theory), we show that GAMD can predict the dynamics of two typical molecular systems, Lennard-Jones system and water system, in the NVT ensemble with velocities regulated by a thermostat. We further show that GAMD's learning and inference are agnostic to the scale, where it can scale to much larger systems at test time. We also perform a comprehensive benchmark test comparing our implementation of GAMD to production-level MD software, showing GAMD's competitive performance on the large-scale simulation.

Activation Pathways of Neurotensin Receptor 1 Elucidated Using Statistical Machine Learning.

Neurotensin receptor 1 (NTSR1) is a G-protein coupled receptor (GPCR) that mediates many biological processes through its interaction with the neurotensin (NTS) peptide. The NTSR1 protein is a clinically significant target as it is involved in the proliferation of cancer cells. Understanding the activation mechanism of NTSR1 is an important prerequisite for exploring the therapeutic potential of targeting NTSR1 and the development of drug molecules specific to NTSR1. Previous studies have been aimed at elucidating the structure of NTSR1 in the active and inactive conformations; however, the intermediate molecular pathway for NTSR1 activation dynamics is largely unknown. In this study, we performed extensive molecular dynamics (MD) simulations of the NTSR1 protein and analyzed its kinetic conformational changes to determine the microswitches that drive NTSR1 activation. To biophysically interpret the high-dimensional simulation trajectories, we used Markov state models and machine learning to elucidate the important and detailed conformational changes in NTSR1. Through the analysis of identified microswitches, we propose a mechanistic pathway for NTSR1 activation.

Potential neutralizing antibodies discovered for novel corona virus using machine learning.

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

DNA Detection with Single-Layer Ti3C2 MXene Nanopore.

Nanopore based sequencing is an exciting alternative to the conventional sequencing methods as it allows for high-throughput sequencing with lower reagent costs and time requirements. Biological nanopores, such as α-hemolysin, are subject to breakdown under thermal, electrical, and mechanical stress after being used millions of times. On the contrary, two-dimensional (2D) nanomaterials have been explored as a solid-state platform for the sequencing of DNA. Their subnanometer thickness and outstanding mechanical properties have made possible the high-resolution and high-signal-to-noise ratio detection of DNA, but such a performance is dependent on the type of nanomaterial selected. Solid-state nanopores of graphene, Si3N4, and MoS2 have been studied as potential candidates for DNA detection. However, it is important to understand the sensitivity and characterization of these solid-state materials for nanopore based detection. Recent developments in the synthesis of MXene have inspired our interest in its application as a nanopore based DNA detection membrane. Here, we simulate the metal carbide, MXene (Ti3C2), with single stranded DNA to understand its interactions and the efficiency of MXene as a putative material for the development of a nanopore based detection platform. Using molecular dynamics (MD) simulations, we present evidence that a MXene based nanopore is able to detect the different types of DNA bases. We have successfully identified features to differentiate the translocation of different types of DNA bases across the nanopore.

Deciphering the structural basis of the broad substrate specificity of myo-inositol monophosphatase (IMP) from Cicer arietinum.

Myo-inositol monophosphatase (IMP) is a crucial enzyme in the inositol biosynthetic pathway that dephosphorylates myo-inositol 1-phosphate and other inositol phosphate derivative compounds to maintain the homeostasis of cellular inositol pool. In our previous research, we have biochemically and functionally characterized IMP enzyme from chickpea (CaIMP), which was able to catalyze diverse substrates. We cloned, overexpressed recombinant CaIMP protein and purified it and further characterized the CaIMP with its three main substrates viz. galactose 1-P, inositol 6-P and fructose 1,6-bisP. Homology model of CaIMP was generated to elucidate the factors contributing to the broad substrate specificity of the protein. The active site of the CaIMP protein was analysed with respect to its interactions with the proposed substrates. Structural features such as, high B-factor and flexible loop regions in the active site, inspired further investigation into the static and dynamic behaviour of the active site of CaIMP protein. The electrostatic biding of each of the key substrates was assessed through molecular docking. Furthermore, molecular dynamics simulations showed that these interactions indeed were stable for extended periods of time under physiological conditions. These experiments conclusively allowed us to establish the primary factors contributing to the promiscuity in substrate binding by CaIMP protein.

Structure model of ferrochelatase from Salmonella Typhi elucidating metalation mechanism.

A homology model of ferrochelatase (HemH), the heme biosynthesis terminal step enzyme from Salmonella Typhi was generated to understand the mechanism of metal insertion into protoporphyrin IX for heme biosynthesis. The overall fold of membrane associated ferrochelatase (StFc) from S. Typhi is similar to human and Yeast ferrochelatase than Bacillus subtilis, and Bacillus anthracis. An insertion of 16 amino acid residues in helical switch having hydrophobic patch proposed to interact with membrane lipids and in opening and closing of heme binding cleft. The sequence analysis and the docking study revealed that the protoporphyrin binding site in StFc has a crucial replacement of Tyr/Met to Leu13 unique in comparison to other known structures, where Tyr13 observed in B. subtilis/B. anthracis while Met76 in human/yeast play important role in holding protoporphyrin in optimal orientation for metalation. A sitting-a-top (SAT) complex mechanism for metalation is proposed with His194 and Glu264 lie at the bottom and Leu13 on the top of the porphyrin ring. In addition, an entry and exit mechanism is also proposed for protoporphyrin binding into cavity by opening and closing of helical switch using molecular dynamics simulation studies of Apo and heme complexed model structure of S. Typhi HemH.

Estimation of structure and stability of MurE ligase from Salmonella enterica serovar Typhi.

MurE ligase catalyzes the assembly of peptide moiety, an essential component of bacterial cell wall. We have explored the conformational stability and unfolding equilibrium behaviour of the protein MurE ligase by determining the conformational free energy, entropy and enthalpy parameters under stress conditions. MurE from Salmonella enterica Serovar Typhi was cloned, expressed and purified. Conformational changes associated with increasing concentration of GdmCl- and urea-induced denaturation of MurE were monitored using Circular Dichroism (CD) and fluorescence spectroscopies. The secondary structural content of protein estimated by CD experiment is in close agreement with the predicted MurE ligase structure by homology modeling. Denaturant-induced transition curve was analyzed for thermodynamic parameters. Average values for MurE ligase of ΔGD0 = 3.13 kcal mol-1, m = 1.52 kcal mol-1 M-1 and Cm (=ΔGD0/m) = 2.05 M were calculated in the presence of GdmCl whereas in the case of urea these were ΔGD0 = 3.04 kcal mol-1, m = 1.20 kcal mol-1 M-1 and Cm (=ΔGD0/m) = 2.53 M. The observed superposition of normalized transition curve of two independent optical properties suggested that GdmCl- and urea-induced denaturation follow a two-state process.