Permeability of TB drugs through the mycolic acid monolayer: a tale of two force fields.

Tuberculosis (TB) treatment becomes challenging due to the unique cell wall structure of Mycobacterium tuberculosis (M. tb). Among various components of the M.tb cell wall, mycolic acid (MA) is of particular interest because it is speculated to exhibit extremely low permeability for most of the drug molecules, thus helping M.tb to survive against medical treatment. However, no quantitative assessment of the thermodynamic barrier encountered by various well-known TB drugs in the mycolic acid monolayer has been performed so far using computational tools. On this premise, our present work aims to probe the permeability of some first and second line TB drugs, namely ethambutol, ethionamide, and isoniazid, through the modelled mycolic acid monolayer, using molecular dynamics (MD) simulation with two sets of force field (FF) parameters, namely GROMOS 54A7-ATB (GROMOS) and CHARMM36 (CHARMM) FFs. Our findings indicate that both FFs provide consistent results in terms of the mode of drug-monolayer interactions but significantly differ in the drug permeability through the monolayer. The mycolic acid monolayer generally exhibited a higher free energy barrier of crossing with CHARMM FF, while with GROMOS FF, better stability of drug molecules on the monolayer surface was observed, which can be attributed to the greater electrostatic potential at the monolayer-water interface, found for the later. Although both the FF parameters predicted the highest resistance against ethambutol (permeability values of 8.40 × 10-34 cm s-1 and 9.61 × 10-31 cm s-1 for the CHARMM FF and the GROMOS FF, respectively), results obtained using GROMOS were found to be consistent with the water solubility of drugs, suggesting it to be a slightly better FF for modelling drug-mycolic acid interactions. Therefore, this study enhances our understanding of TB drug permeability and highlights the potential of the GROMOS FF in simulating drug-mycolic acid interactions.

Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox.

Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein-Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2-5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein-Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox .

MSO-GP: 3-D segmentation of large and complex conjoined tree structures.

Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.

Reversing the Trend: Deciphering Self-Assembly of Unconventional Amphiphiles Having Both Alkyl-Chain and PEG.

In the field of molecular self-assembly, the core of an assembly is always made up of hydrophobic moiety like a long alkyl chain, whereas the outer part has always been a hydrophilic moiety such as poly(ethylene glycol) (PEG), or charged species. Hence, reversing the trend to manifest self-assembled structures with a PEG core and a surface consisting of alkyl chains in aqueous system is incredibly challenging. Herein, we architected a unique class of cationic bolaamphiphiles containing low molecular weight PEG and alkyl chains of different lengths. The bolaamphiphiles spontaneously form vesicles without external stimuli. These vesicles are unprecedented because PEG makes up the vesicle core, while the alkyl chains appear on the vesicles' exterior. Hence, this particular design reverses the usual trend of self-assembly formation. The vesicle size increases with the increase in alkyl chain-length. To our great surprise, we obtained large micelles for longest alkyl-chain amphiphile, which in turn act as a gemini amphiphile. The shift from a particular bolaamphiphile to gemini amphiphile with the variation of alkyl chain is also unexplored. Therefore, this specific class of self-assembled structure would compound a new paradigm in molecular self-assembly and supramolecular chemistry.

EPI-SF: essential protein identification in protein interaction networks using sequence features.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox.

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

RUBic: rapid unsupervised biclustering.

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

Correction: Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

[This corrects the article DOI: 10.1371/journal.pone.0286862.].

C-4-Modified Isotetrones Prevent Biofilm Growth and Persister Cell Resuscitation in Mycobacterium smegmatis.

Hyperphosphorylated nucleotide (p)ppGpp, synthesized by Rel protein, regulates the stringent response pathway responsible for biofilm and persister cell growth in mycobacteria. The discovery of vitamin C as an inhibitor of Rel protein activities raises the prospect of tetrone lactones to prevent such pathways. The closely related isotetrone lactone derivatives are identified herein as inhibitors of the above processes in a mycobacterium. Synthesis and biochemical evaluations show that an isotetrone possessing phenyl substituent at C-4 inhibit the biofilm formation at 400 µg mL-1, 84 h post-exposure, followed by moderate inhibition by the isotetrone possessing the p-hydroxyphenyl substituent. The latter isotetrone inhibits the growth of persister cells at 400 µg mL-1 f.c. when monitored for 2 weeks, under PBS starvation. Isotetrones also potentiate the inhibition of antibiotic-tolerant regrowth of cells by ciprofloxacin (0.75 µg mL-1) and thus act as bioenhancers. Molecular dynamics studies show that isotetrone derivatives bind to the RelMsm protein more efficiently than vitamin C at a binding site possessing serine, threonine, lysine, and arginine.

Utilization of DNA and 2D metal oxide interaction for an optical biosensor.

The efficient monitoring and early detection of viruses may provide essential information about diseases. In this work, we have highlighted the interaction between DNA and a two-dimensional (2D) metal oxide for developing biosensors for further detection of viral infections. Spectroscopic measurements have been used to probe the efficient interactions between single-stranded DNA (ssDNA) and the 2D metal oxide and make them ideal candidates for detecting viral infections. We have also used fully atomistic molecular dynamics (MD) simulation to give a microscopic understanding of the experimentally observed ssDNA-metal oxide interaction. The adsorption of ssDNA on the inorganic surface was found to be driven by favourable enthalpy change, and 5'-guanine was identified as the interacting nucleotide base. Additionally, the in silico assessment of the conformational changes of the ssDNA chain during the adsorption process was also performed in a quantitative manner. Finally, we comment on the practical implications of these developments for sensing that could help design advanced systems for preventing virus-related pandemics.

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output. Our two stage synergistic segmentation method, Deep-Fuzz, works especially well for overlapping objects, and achieves state-of-the-art performance in four public cell nuclei segmentation datasets. We also show through visual examples how our final output is better aligned with pathological insights, and thus more clinically interpretable.

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

A computational study on strontium ion modified hydroxyapatite-fibronectin interactions.

Protein adsorption is the first key step in cell-material interactions. The initial phase of such an adsorption process can only be probed using modelling approaches like molecular dynamics (MD) simulations. Despite a large number of studies on the adsorption behaviour of proteins on different biomaterials including calcium phosphates (CaP), little attention has been paid towards the quantitative assessment of the effects of various physicochemical influencers like surface modification, pH, and ionic strength. In the case of doped CaPs, surface modification through isomorphic substitution of foreign ions inside the apatite structure is of particular interest in the context of protein-HA interactions, as it is widely used to tailor the biological response of HA. Given this background, we present here the molecular-level understanding of the fibronectin (FN) adsorption mechanism and kinetics on a Sr2+-doped hydroxyapatite, HA, (001) surface at 300 K by means of all-atom molecular dynamics simulations. Electrostatic interactions involved in the adsorption of FN on HA were found to be significantly modified due to Sr2+ doping into the apatite lattice. In harmony with the published experimental observations, the Sr-doped surfaces were found to better support FN adhesion compared to pure HA, with 10 mol% Sr-doped HA exhibiting the best FN adsorption. The observed altered adsorption behaviour of FN on Sr-doped HA was correlated with the Hofmeister effect. Moreover, the non-monotonous trend of the FN-material interaction energy can be attributed to the spatial rearrangement of the functional groups (PO43-, OH-) in the apatite crystal. Sr2+ ions also influence the stability of the secondary structure of FN, as observed from the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analysis. The presence of Sr2+ enhances the flexibility of specific residues (residue nos. 20-44, 74-88) of the FN module. Rupture forces to disentangle FN from the biomaterial surface, obtained from steered molecular dynamics (SMD) simulations, were found to corroborate well with the results of equilibrium MD simulations. One particular observation is that the availability of an RGD motif (Arginine-Glycine-aspartate sequence, which interacts with cell surface receptor integrin to form a focal adhesion complex) for the interaction with cell surface receptor integrin is not significantly influenced by Sr2+ substitution.

In silico study on probing atomistic insights into structural stability and tensile properties of Fe-doped hydroxyapatite single crystals.

Hydroxyapatite (HA, Ca10PO4(OH)2) is a widely explored material in the experimental domain of biomaterials science, because of its resemblance with natural bone minerals. Specifically, in the bioceramic community, HA doped with multivalent cations (e.g., Mg2+, Fe2+, Sr2+, etc.) has been extensively investigated in the last few decades. Experimental research largely established the critical role of dopant content on mechanical and biocompatibility properties. The plethora of experimental measurements of mechanical response on doped HA is based on compression or indentation testing of polycrystalline materials. Such measurements, and more importantly the computational predictions of mechanical properties of single crystalline (doped) HA are scarce. On that premise, the present study aims to build atomistic models of Fe2+-doped HA with varying Fe content (10, 20, 30, and 40 mol%) and to explore their uniaxial tensile response, by means of molecular dynamics (MD) simulation. In the equilibrated unit cell structures, Ca(1) sites were found to be energetically favourable for Fe2+ substitution. The local distribution of Fe2+ ions significantly affects the atomic partial charge distribution and chemical symmetry surrounding the functional groups, and such signatures are found in the MD analyzed IR spectra. The significant decrease in the intensity of the IR bands found in the Fe-doped HA together with band splitting, because of the symmetry changes in the crystal structure. Another important objective of this work is to computationally predict the mechanical response of doped HA in their single crystal format. An interesting observation is that the elastic anisotropy of undoped HA was not compromised with Fe-doping. Tensile strength (TS) is systematically reduced in doped HA with Fe2+ dopant content and a decrease in TS with temperature can be attributed to the increased thermal agitation of atoms at elevated temperatures. The physics of the tensile response was rationalized in terms of the strain dependent changes in covalent/ionic bond framework (Ca-P distance, P-O bond strain, O-P-O angular strain, O-H bond distance). Further, the dynamic changes in covalent bond network were energetically analyzed by calculating the changes in O-H and P-O bond vibrational energy. Summarizing, the current work establishes our foundational understanding of the atomistic phenomena involved in the structural stability and tensile response of Fe-doped HA single crystals.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN.

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein-protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.