Conformational flexibility driving charge-selective substrate translocation across a bacterial transporter.

Bacterial membrane porins facilitate the translocation of small molecules while restricting large molecules, and this mechanism remains elusive at the molecular level. Here, we investigate the selective uptake of large cyclic sugars across an unusual passive membrane transporter, CymA, comprising a charged zone and a constricting N terminus segment. Using a combination of electrical recordings, protein mutagenesis and molecular dynamics simulations, we establish substrate translocation across CymA governed by the electrostatic pore properties and conformational dynamics of the constriction segment. Notably, we show that the variation in pH of the environment resulted in reversible modulation of the substrate binding site in the pore, thereby regulating charge-selective transport of cationic, anionic and neutral cyclic sugars. The quantitative kinetics of cyclic sugar translocation across CymA obtained in electrical recordings at different pHs are comparable with molecular dynamics simulations that revealed the transport pathway, energetics and favorable affinity sites in the pore for substrate binding. We further define the molecular basis of cyclic sugar translocation and establish that the constriction segment is flexible and can reside inside or outside the pore, regulating substrate translocation distinct from the ligand-gated transport mechanism. Our study provides novel insights into energy-independent large molecular membrane transport for targeted drug design strategies.

Recent advancements of hydrogels in immunotherapy: Breast cancer treatment.

Breast cancer is the most prevalent cancer among women and the leading cause of cancer-related deaths in this population. Recent advances in Immunotherapy, or combined immunotherapy, offering a more targeted and less toxic approach, expand the survival rate of patients more than conventional treatment. Notably, hydrogels, a versatile platform provided promising avenues to combat breast cancer in preclinical studies and extended to clinical practices. With advantages such as the alternation of tumor microenvironment, immunomodulation, targeted delivery of therapeutic agents, and their sustained release at specific sites of interest, hydrogels can potentially be used for the treatment of breast cancer. This review highlights the advantages, mechanisms of action, stimuli-responsiveness properties, and recent advancements of hydrogels for treating breast cancer immunotherapy. Moreover, post-treatment and its clinical translations are discussed in this review. The integration of hydrogels in immunotherapy strategies may pave the way for more effective, personalized, and patient-friendly approaches to combat breast cancer, ultimately contributing to a brighter future for breast cancer patients.

Understanding electrochemical interfaces through comparing experimental and computational charge density-potential curves.

Electrode-electrolyte interfaces play a decisive role in electrochemical charge accumulation and transfer processes. Theoretical modelling of these interfaces is critical to decipher the microscopic details of such phenomena. Different force field-based molecular dynamics protocols are compared here in a view to connect calculated and experimental charge density-potential relationships. Platinum-aqueous electrolyte interfaces are taken as a model. The potential of using experimental charge density-potential curves to transform cell voltage into electrode potential in force-field molecular dynamics simulations, and the need for that purpose of developing simulation protocols that can accurately calculate the double-layer capacitance, are discussed.

Elucidation of Spatial Positioning of Ribosomes around Chromosome in Escherichia coli Cytoplasm via a Data-Informed Polymer-Based Model.

The spatial arrangement of ribosomes and chromosome in Escherichia coli's cytoplasm challenges conventional wisdom. Contrary to the notion of ribosomes acting as inert crowders to the chromosome in the cytoplasm, here we propose a nuanced view by integrating a wide array of experimental data sets into a polymer-based computer model. A set of data-informed computer simulations determines that a delicate balance of attractive and repulsive interactions between ribosomes and the chromosome is required in order to reproduce experimentally obtained linear densities and brings forth the view that ribosomes are not mere inert crowders in the cytoplasm. The model finds that the ribosomes represent themselves as a poor solvent for the chromosome with a 50 nm mesh size, consistent with previous experimental analysis. Our multidimensional analysis of ribosome distribution, both free (30S and 50S) and bound (70S polysome), uncovers a relatively less pronounced segregation pattern than previously thought. Notably, we identify a ribosome-rich central region within the innermost core of the nucleoid. Moreover, our exploration of the chromosome mesh size and the conformation of bound ribosomes suggests that these ribosomes maintain elongated shapes, enabling them to navigate through the chromosome mesh and access the central core. This dynamic localization challenges the static segregation model and underscores the pivotal role of ribosome-chromosome interactions in cellular media.

A Halogen-Bond-Driven Artificial Chloride-Selective Channel Constructed from 5-Iodoisophthalamide-based Molecules.

Despite considerable emphasis on advancing artificial ion channels, progress is constrained by the limited availability of small molecules with the necessary attributes of self-assembly and ion selectivity. In this study, a library of small molecules based on 5-haloisophthalamide and a non-halogenated isophthalamide were examined for their ion transport properties across the lipid bilayer membranes, and the finding demonstrates that the di-hexyl-substituted 5-iodoisophthalamide derivative exhibits the highest level of activity. Furthermore, it was established that the highest active compound facilitates the selective chloride transport that occurs via an antiport-mediated mechanism. The crystal structure of the compound unveils a distinctive self-assembly of molecules, forming a zig-zag channel pore that is well-suited for the permeation of anions. Planar bilayer conductance measurements proved the formation of chloride selective channels. A molecular dynamics simulation study, relying on the self-assembled component derived from the crystal structure, affirmed the paramount significance of intermolecular hydrogen bonding in the formation of supramolecular barrel-rosette structures that span the bilayer. Furthermore, it was demonstrated that the transport of chloride across the lipid bilayer membrane is facilitated by the synergistic effects of halogen bonding and hydrogen bonding within the channel.

Development of a Data-Driven Integrative Model of a Bacterial Chromosome.

The chromosome of archetypal bacteria E. coli is known for a complex topology with a 4.6 × 106 base pairs (bp) long sequence of nucleotides packed within a micrometer-sized cellular confinement. The inherent organization underlying this chromosome eludes general consensus due to the lack of a high-resolution picture of its conformation. Here we present our development of an integrative model of E. coli at a 500 bp resolution (https://github.com/JMLab-tifrh/ecoli_finer), which optimally combines a set of multiresolution genome-wide experimentally measured data within a framework of polymer based architecture. In particular the model is informed with an intragenome contact probability map at 5000 bp resolution derived via the Hi-C experiment and RNA-sequencing data at 500 bp resolution. Via dynamical simulations, this data-driven polymer based model generates an appropriate conformational ensemble commensurate with chromosome architectures that E. coli adopts. As a key hallmark of the E. coli chromosome the model spontaneously self-organizes into a set of nonoverlapping macrodomains and suitably locates plectonemic loops near the cell membrane. As novel extensions, it predicts a contact probability map simulated at a higher resolution than precedent experiments and can demonstrate segregation of chromosomes in a partially replicating cell. Finally, the modular nature of the model helps us devise control simulations to quantify the individual role of key features in hierarchical organization of the bacterial chromosome.

A Synergistic View on Osmolyte's Role against Salt and Cold Stress in Biointerfaces.

We present our perspective on the role of osmolytes in mitigating abiotic stresses such as hypersalinity and sudden temperature changes. While the stabilizing effect of osmolytes on protein tertiary structures has been extensively studied, their direct impact on abiotic stress factors has eluded mainstream attention. Via highlighting a set of recent success stories of a joint venture of computer simulations and experimental measurements, we summarize the mechanistic insights into osmolytic action, particularly in the context of salt stress and combined cold-salt stress at the interface of biomolecular surfaces and saline environments. We stress the importance of chemical specificity in osmolytic activity, the interplay of differential osmolytic behaviors against heterogeneous salt stress, and the capability of osmolytes to adopt combined actions. Additionally, we discuss the potential of incorporating nanomaterial-based systems to enrich our understanding of osmolyte bioactions and facilitate their practical applications. We anticipate that this discourse will inspire interdisciplinary collaborations and motivate further investigations on osmolytes, ultimately broadening their applications in the fields of health and disease.

3site Multisubstrate-Bound State of Cytochrome P450cam.

We identified a multisubstrate-bound state, hereby referred as a 3site state, in cytochrome P450cam via integrating molecular dynamics simulation with nuclear magnetic resonance (NMR) pseudocontact shift measurements. The 3site state is a result of simultaneous binding of three camphor molecules in three locations around P450cam: (a) in a well-established "catalytic" site near heme, (b) in a kink-separated "waiting" site along channel-1, and (c) in a previously reported "allosteric" site at E, F, G, and H helical junctions. These three spatially distinct binding modes in the 3site state mutually communicate with each other via homotropic allostery and act cooperatively to render P450cam functional. The 3site state shows a significantly superior fit with NMR pseudo contact shift (PCS) data with a Q-score of 0.045 than previously known bound states and consists of D251 free of salt-bridges with K178 and R186, rendering the enzyme functionally primed. To date, none of the reported cocomplex of P450cam with its redox partner putidaredoxin (pdx) has been able to match solution NMR data and controversial pdx-induced opening of P450cam's channel-1 remains a matter of recurrent discourse. In this regard, inclusion of pdx to the 3site state is able to perfectly fit the NMR PCS measurement with a Q-score of 0.08 and disfavors the pdx-induced opening of channel-1, reconciling previously unexplained remarkably fast hydroxylation kinetics with a koff of 10.2 s-1. Together, our findings hint that previous experimental observations may have inadvertently captured the 3site state as an in vitro solution state, instead of the catalytic state alone, and provided a distinct departure from the conventional understanding of cytochrome P450.

Polyphenolic Carbon Quantum Dots with Intrinsic Reactive Oxygen Species Amplification for Two-Photon Bioimaging and In Vivo Tumor Therapy.

Recent studies indicate that mitochondrial dysfunctions and DNA damage have a critical influence on cell survival, which is considered one of the therapeutic targets for cancer therapy. In this study, we demonstrated a comparative study of the effect of polyphenolic carbon quantum dots (CQDs) on in vitro and in vivo antitumor efficacy. Dual emissive (green and yellow) shape specific polyphenolic CQDs (G-CQDs and Y-CQDs) were synthesized from easily available nontoxic precursors (phloroglucinol), and the antitumor property of the as-synthesized probe was investigated as compared to round-shaped blue emissive CQDs (B-CQDs) derived from well-reported precursor citric acid and urea. The B-CQDs had a nuclei-targeting property, and G-CQDs and Y-CQDs had mitochondria-targeting properties. We have found that the polyphenol containing CQDs (at a dose of 100 µg mL-1) specifically attack mitochondria by excess accumulation, altering the metabolism, inhibiting branching pattern, imbalanced Bax/Bcl-2 homeostasis, and ultimately generating oxidative stress levels, leading to oxidative stress-induced cell death in cancer cells in vitro. We show that G-CQDs are the main cause of oxidative stress in cancer cells because of their ability to produce sufficient •OH- and 1O2 radicals, evidenced by electron paramagnetic resonance spectroscopy and a terephthalic acid test. Moreover, the near-infrared absorption properties of the CQDs were exhibited in two-photon (TP) emission, which was utilized for TP cellular imaging of cancer cells without photobleaching. The in vivo antitumor test further discloses that intratumoral injection of G-CQDs can significantly augment the treatment efficacy of subcutaneous tumors without any adverse effects on BalB/c nude mice. We believe that shape-specific polyphenolic CQD-based nanotheranostic agents have a potential role in tumor therapy, thus proving an insight on treatment of malignant cancers.

Machine Learning Subtle Conformational Change due to Phosphorylation in Intrinsically Disordered Proteins.

Phosphorylation of intrinsically disordered proteins/regions (IDPs/IDRs) has a profound effect in biological functions such as cell signaling, protein folding or unfolding, and long-range allosteric effects. However, here we focus on two IDPs, namely 83-residue IDR transcription factor Ash1 and 92-residue long N-terminal region of CDK inhibitor Sic1 protein, found in Saccharomyces cerevisiae, for which experimental measurements of average conformational properties, namely, radius of gyration and structure factor, indicate negligible changes upon phosphorylation. Here, we show that a judicious dissection of conformational ensemble via combination of unsupervised machine learning and extensive molecular dynamics (MD) trajectories can highlight key differences and similarities among the phosphorylated and wild-type IDP. In particular, we develop Markov state model (MSM) using the latent-space dimensions of an autoencoder, trained using multi-microsecond long MD simulation trajectories. Examination of structural changes among the states, prior to and upon phosphorylation, captured several similarities and differences in their backbone contact maps, secondary structure, and torsion angles. Hydrogen bonding analysis revealed that phosphorylation not only increases the number of hydrogen bonds but also switches the pattern of hydrogen bonding between the backbone and side chain atoms with the phosphorylated residues. We also observe that although phosphorylation introduces salt bridges, there is a loss of the cation-π interaction. Phosphorylation also improved the probability for long-range hydrophobic contacts and also enhanced interaction with water molecules and improved the local structure of water as evident from the geometric order parameters. The observations on these machine-learnt states gave important insights, as it would otherwise be difficult to determine experimentally which is important, if we were to understand the role of phosphorylation of IDPs in their biological functions.

Identifying Selectivity Filters in Protein Biosensor for Ligand Screening.

Specialized sensing mechanisms in bacteria enable the identification of cognate ligands with remarkable selectivity in highly xenobiotic-polluted environments where these ligands are utilized as energy sources. Here, via integrating all-atom computer simulation, biochemical assay, and isothermal titration calorimetry measurements, we determine the molecular basis of MopR, a phenol biosensor's complex selection process of ligand entry. Our results reveal a set of strategically placed selectivity filters along the ligand entry pathway of MopR. These filters act as checkpoints, screening diverse aromatic ligands at the protein surface based on their chemical features and sizes. Ligands meeting specific criteria are allowed to enter the sensing site in an orientation-dependent manner. Sequence and structural analyses demonstrate the conservation of this ligand entry mechanism across the sensor class, with individual amino acids along the selectivity filter path playing a critical role in ligand selection. Together, this investigation highlights the importance of interactions with the ligand entry pathway, in addition to interactions within the binding pocket, in achieving ligand selectivity in biological sensing. The findings enhance our understanding of ligand selectivity in bacterial phenol biosensors and provide insights for rational expansion of the biosensor repertoire, particularly for the biotechnologically relevant class of aromatic pollutants.

Osmolytes as Cryoprotectants under Salt Stress.

Cryoprotecting agent (CPA)-guided preservation is essential for effective protection of cells from cryoinjuries. However, current cryoprotecting technologies practiced to cryopreserve cells for biomedical applications are met with extreme challenges due to the associated toxicity of CPAs. Because of these limitations of present CPAs, the quest for nontoxic alternatives for useful application in cell-based biomedicines has been attracting growing interest. Toward this end, here, we investigate naturally occurring osmolytes' scope as biocompatible cryoprotectants under cold stress conditions in high-saline medium. Via a combination of the simulation and experiment on charged silica nanostructures, we render first-hand evidence that a pair of archetypal osmolytes, glycine and betaine, would act as a cryoprotectant by restoring the indigenous intersurface electrostatic interaction, which had been a priori screened due to the cold effect under salt stress. While these osmolytes' individual modes of action are sensitive to subtle chemical variation, a uniform augmentation in the extent of osmolytic activity is observed with an increase in temperature to counter the proportionately enhanced salt screening. The trend as noted in inorganic nanostructures is found to be recurrent and robustly transferable in a charged protein interface. In hindsight, our observation justifies the sufficiency of the reduced requirement of osmolytes in cells during critical cold conditions and encourages their direct usage and biomimicry for cryopreservation.

Nonmonotonic Modulation of the Protein-Ligand Recognition Event by Inert Crowders.

The ubiquitous event of a protein recognizing small molecules or ligands at its native binding site is crucial for initiating major biological processes. However, how a crowded environment, as is typically represented by a cellular interior, would modulate the protein-ligand search process is largely debated. Excluded volume-based theory suggests that the presence of an inert crowder would reinforce a steady stabilization and enhancement of the protein-ligand recognition process. Here, we counter this long-held perspective via the molecular dynamics simulation and Markov state model of the protein-ligand recognition event in the presence of inert crowders. Specifically, we demonstrate that, depending on concentration, even purely inert crowders can exert a nonmonotonic effect via either stabilizing or destabilizing the protein-ligand binding event. Analysis of the kinetic network of binding pathways reveals that the crowders would either modulate precedent non-native on-pathway intermediates or would devise additional ones in a multistate recognition event across a wide range of concentrations. As an important insight, crowders gradually shift the relative transitional preference of these intermediates toward a native-bound state, with ligand residence time at the binding pocket dictating the trend of nonmonotonic concentration dependence by simple inert crowders.

Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event.

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

Emergence of Catalytic Triad by Short Peptide Based Nanofibrillar Assemblies.

Through millions of years of the evolutionary journey, contemporary enzymes observed in extant metabolic pathways have evolved to become specialized, in contrast to their ancestors, which displayed promiscuous activities with wider substrate specificities. However, there remain critical gaps in our understanding of how these early enzymes could show such catalytic versatility despite lacking the complex three-dimensional folds of the existing modern-day enzymes. Herein, we report the emergence of a promiscuous catalytic triad by short amyloid peptide based nanofibers that access paracrystalline folds of ß-sheets to expose three residues (lysine, imidazole, and tyrosine) toward solvent. The ordered folded nanostructures could simultaneously catalyze two metabolically relevant chemical transformations via C-O and C-C bond manipulations, displaying both hydrolase and retro-aldolase-like activities. Further, the latent catalytic capabilities of the short peptide based promiscuous folds also helped in processing a cascade transformation, suggesting the important role they might have played in protometabolism and early evolutionary processes.

A deep encoder-decoder framework for identifying distinct ligand binding pathways.

The pathway(s) that a ligand would adopt en route to its trajectory to the native pocket of the receptor protein act as a key determinant of its biological activity. While Molecular Dynamics (MD) simulations have emerged as the method of choice for modeling protein-ligand binding events, the high dimensional nature of the MD-derived trajectories often remains a barrier in the statistical elucidation of distinct ligand binding pathways due to the stochasticity inherent in the ligand's fluctuation in the solution and around the receptor. Here, we demonstrate that an autoencoder based deep neural network, trained using an objective input feature of a large matrix of residue-ligand distances, can efficiently produce an optimal low-dimensional latent space that stores necessary information on the ligand-binding event. In particular, for a system of L99A mutant of T4 lysozyme interacting with its native ligand, benzene, this deep encoder-decoder framework automatically identifies multiple distinct recognition pathways, without requiring user intervention. The intermediates involve the spatially discrete location of the ligand in different helices of the protein before its eventual recognition of native pose. The compressed subspace derived from the autoencoder provides a quantitatively accurate measure of the free energy and kinetics of ligand binding to the native pocket. The investigation also recommends that while a linear dimensional reduction technique, such as time-structured independent component analysis, can do a decent job of state-space decomposition in cases where the intermediates are long-lived, autoencoder is the method of choice in systems where transient, low-populated intermediates can lead to multiple ligand-binding pathways.

Conformational Plasticity in α-Synuclein and How Crowded Environment Modulates It.

A 140-residue intrinsically disordered protein (IDP), α-synuclein (αS), is known to adopt conformations that are vastly plastic and susceptible to environmental cues and crowders. However, the inherently heterogeneous nature of αS has precluded a clear demarcation of its monomeric precursor between aggregation-prone and functionally relevant aggregation-resistant states and how a crowded environment could modulate their mutual dynamic equilibrium. Here, we identify an optimal set of distinct metastable states of αS in aqueous media by dissecting a 73 µs-long molecular dynamics ensemble via building a comprehensive Markov state model (MSM). Notably, the most populated metastable state corroborates with the dimension obtained from PRE-NMR studies of αS monomer, and it undergoes kinetic transition at diverse time scales with a weakly populated random-coil-like ensemble and a globular protein-like state. However, subjecting αS to a crowded environment results in a nonmonotonic compaction of these metastable conformations, thereby skewing the ensemble by either introducing new tertiary contacts or by reinforcing the innate contacts. The early stage of dimerization process is found to be considerably expedited in the presence of crowders, albeit promoting nonspecific interactions. Together with this, using an extensively sampled ensemble of αS, this exposition demonstrates that crowded environments can potentially modulate the conformational preferences of IDP that can either promote or inhibit aggregation events.

A Benzohydrazide-Based Artificial Ion Channel that Modulates Chloride Ion Concentration in Cancer Cells and Induces Apoptosis by Disruption of Autophagy.

Fluctuations in the intracellular chloride ion concentration, mediated by synthetic ion transporters, have been known to induce cytotoxicity in cells by disrupting ionic homeostasis. However, the activity of these transporters in modulating autophagy remains largely unexplored. Here, we report a benzoylbenzohydrazide (1c) that self-assembles to form a supramolecular nanochannel lumen that allows selective and efficient transport of chloride ions across the cell membranes, disrupts ion homeostasis, and thus leads to the induction of apoptosis in cancer cells. It is important to note that the transporter was relatively nontoxic to cells of noncancerous origin. 1c was also shown to induce the deacidification of lysosomes, thereby disrupting autophagy in cancer cells. Taken together, these findings provide a rare example of an artificial ion channel that specifically targets cancer cells by induction of apoptosis via disruption of autophagy.

Chemically routed interpore molecular diffusion in metal-organic framework thin films.

Transport diffusivity of molecules in a porous solid is constricted by the rate at which molecules move from one pore to the other, along the concentration gradient, i.e. by following Fickian diffusion. In heterogeneous porous materials, i.e. in the presence of pores of different sizes and chemical environments, diffusion rate and directionality remain tricky to estimate and adjust. In such a porous system, we have realized that molecular diffusion direction can be orthogonal to the concentration gradient. To experimentally determine this complex diffusion rate dependency and get insight of the microscopic diffusion pathway, we have designed a model nanoporous structure, metal-organic framework (MOF). In this model two chemically and geometrically distinct pore windows are spatially oriented by an epitaxial, layer-by-layer growth method. The specific design of the nanoporous channels and quantitative mass uptake rate measurements have indicated that the mass uptake is governed by the interpore diffusion along the direction orthogonal to the concentration gradient. This revelation allows chemically carving the nanopores, and accelerating the interpore diffusion and kinetic diffusion selectivity.

Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning.

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. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.