Underground pipelines serve as critical infrastructure for gas transmission, strategically buried for safety, environmental, and economic considerations. Despite their importance, operational challenges and external interferences can lead to underground gas leaks with potentially catastrophic consequences for both human safety and the environment. The presence of a protective soil bed introduces complexities in understanding subsurface transport phenomena and quantifying gas releases accurately. Herein, this review presents a systematic analysis of published research in the field of underground gas releases, with an emphasis on interdisciplinary approaches that connect the lithosphere and atmosphere. The analysis highlights the broad spectrum of employed methods, including theoretical models based on fundamental principles, empirical formulations derived from experimental data, and sophisticated computational tools. A clear fundamental understanding and computational analysis, and to a lesser extent experimental, have been established to describe the migration regime. In contrast, more empirical research has addressed the crater formation regime, though focus was given to the far-field modelling following the soil ejection rather than the transient phenomena leading to the formation of the crater. Additionally, this review touches upon practical and conceptual topics, such as detection and localization techniques, and flow regimes in other gaseous flows through soil and powder beds, putting into question the applicability of some presumed granulated concepts to the flowing behavior expected beyond migration. The research landscape predominantly focuses on investigating the influence of release parameters on the release phenomena only from the atmospheric or soil domain perspective. This work provides insights that aim to first transcend both domains and then bridge the three distinct flow regimes-migration, uplift, and crater formation-despite the limited acknowledgment of the necessity of addressing all regimes concurrently through a universal approach. This review serves as a valuable resource for engineers to develop innovative solutions for the management of risks associated with underground gas leaks.

Proton-coupled oligopeptide transporters (POTs) are of great pharmaceutical interest owing to their promiscuous substrate binding site that has been linked to improved oral bioavailability of several classes of drugs. Members of the POT family are conserved across all phylogenetic kingdoms and function by coupling peptide uptake to the proton electrochemical gradient. Cryo-EM structures and alphafold models have recently provided new insights into different conformational states of two mammalian POTs, SLC15A1, and SLC15A2. Nevertheless, these studies leave open important questions regarding the mechanism of proton and substrate coupling, while simultaneously providing a unique opportunity to investigate these processes using molecular dynamics (MD) simulations. Here, we employ extensive unbiased and enhanced-sampling MD to map out the full SLC15A2 conformational cycle and its thermodynamic driving forces. By computing conformational free energy landscapes in different protonation states and in the absence or presence of peptide substrate, we identify a likely sequence of intermediate protonation steps that drive inward-directed alternating access. These simulations identify key differences in the extracellular gate between mammalian and bacterial POTs, which we validate experimentally in cell-based transport assays. Our results from constant-PH MD and absolute binding free energy (ABFE) calculations also establish a mechanistic link between proton binding and peptide recognition, revealing key details underpining secondary active transport in POTs. This study provides a vital step forward in understanding proton-coupled peptide and drug transport in mammals and pave the way to integrate knowledge of solute carrier structural biology with enhanced drug design to target tissue and organ bioavailability.
The cells in our body are sealed by a surrounding membrane that allows them to control which molecules can enter or leave. Desired molecules are often imported via transport proteins that require a source of energy. One way that transporter proteins achieve this is by simultaneously moving positively charged particles called protons across the membrane. Proteins called POTs (short for proton-coupled oligopeptide transporters) use this mechanism to import small peptides and drugsin to the cells of the kidney and small intestine. Sitting in the centre of these transporters is a pocket that binds to the imported peptide which has a gate on either side: an outer gate that opens towards the outside of the cell, and an inner gate that opens towards the cell’s interior. The movement of protons from the outer to the inner gate is thought to shift the shape of the transporter from an outwards to an inwards-facing state. However, the molecular details of this energetic coupling are not well understood. To explore this, Lichtinger et al. used computer simulations to pinpoint where protons bind on POTs to trigger the gates to open. The simulations proposed that two sites together make up the outward-facing gate, which opens upon proton binding. Lichtinger et al. then validated these sites experimentally in cultured human cells that produce mutant POTs. After the desired peptide/drug has attached to the binding pocket, the protons then move to two more sites further down the transporter. This triggers the inner gate to open, which ultimately allows the small molecule to move into the cell. These findings represent a significant step towards understanding how POTs transport their cargo. Since POTs can transport a range of drugs from the digestive tract into the body, these results could help researchers design molecules that are better absorbed. This could lead to more orally available medications, making it easier for patients to adhere to their treatment regimen.

As the world recovers from the COVID-19 pandemic, a resurgence in MPXV cases is causing serious concern. The early clinical similarity of MPXV to common ailments like the flu and cold, coupled with the resemblances of its progressing rash to other infections, underscores the importance of prompt and accurate diagnosis. Among the infections, smallpox is clinically closest to MPXV, and rashes similar to MPXV stages also appear in syphilis and varicella zoster. A comprehensive review of MPXV, herpes, and syphilis was carried out, including structural and morphological features, origins, transmission modes, and computational studies. PubMed literature search on MPXV, using MeSH key terms, yielded 1904 results, with the analysis revealing prominent links to sexually transmitted diseases. More in-depth exploration of MPXV, Herpes Simplex Virus (HSV), and Syphilis revealed further disease interconnections and geographical correlations. These findings emphasize the need for a holistic understanding of these interconnected infectious agents for better control and management.

The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep-learning-based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ-conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide-rich peptides by these complementary methods.

The novelty and the essential purpose of this research is the preparation of new anti-inflammatory iron complexes in water green solvent using critical micelle concentration of anionic surface active agent (SAA). Three new anti-inflammatory iron complexes have been prepared. Thiophene-electron (es) donor (D) Schiff base (2-(2-OH-benzylidene)-amino)-4, 5, 6, 7-tetrah ydrobenzo[b] thiophene-3-carbonitrile) has been prepared. Molecular structures of all samples were confirmed based on CNH analysis, 1H NMR and 13C NMR spectra. The molecular structure of Schiff base is further confirmed by computational chemistry using the DFT-B3LYP method, 6-31G (d) basis set. Observed and simulated 1H NMR, UV-Vis. IR/Raman spectra confirmed the molecular structure of D. This Schiff base is intercalated to ferric chloride (FeCl3) giving pure iron charge transfer complex (CTCs). In vitro and kinetic studies confirmed Fe-CTC complexes had (concentration-dependent) potent antimicrobial-, good anti-inflammatory activities. Free radical scavenging activity nitrous oxide (NO.) of Fe (III)CTCs is attributed to geometry Fe(III) ions as distorted octahedral (either monoclinic or triclinic single crystals) via functional groups (-C]N-O, NH2). Elemental analysis and EDS spectra confirmed strong binding between iron and hetero atoms (N, S, O) of D molecules.

Dialysis membranes are not hemocompatible with human blood, as the patients are suffering from the blood-membrane interactions\' side effects. Zwitterionic structures have shown improved hemocompatibility; however, their complicated synthesis hinders their commercialization. The goal of the study is to achieve fast functionalization for carboxybetaine and sulfobetaine zwitterionic immobilization on PES membranes while comparing the stability and the targeted hemocompatibility. The chemical modification approach is based on an aminolysis reaction. Characterization, computational simulations, and clinical analysis were conducted to study the modified membranes. Atomic force microscopy (AFM) patterns showed a lower mean roughness for carboxybetaine-modified (6.3 nm) and sulfobetaine-modified (7.7 nm) membranes compared to the neat membrane (52.61 nm). The pore size of the membranes was reduced from values above 50 nm for the neat PES to values between 2 and 50 nm for zwitterionized membranes, using Brunauer-Emmett-Teller (BET) analysis. More hydrophilic surfaces led to a growth equilibrium water content (EWC) of nearly 6% for carboxybetaine and 10% for sulfobetaine-modified membranes. Differential scanning calorimetry (DSC) measurements were 12% and 16% stable water for carboxybetaine- and sulfobetaine-modified membranes, respectively. Sulfobetaine membranes showed better compatibility with blood with respect to C5a, IL-1a, and IL-6 biomarkers. Aminolysis-based zwitterionization was found to be suitable for the improvement of hemodialysis membranes. The approach introduced in this paper could be used to modify the current dialysis membranes with minimal change in the production facilities.

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the \'humanness\' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Multiple sclerosis (MS), an intricate neurological disorder, continues to challenge our understanding of the pivotal interplay between the immune system and the central nervous system (CNS). This condition arises from the immune system\'s misdirected attack on nerve fiber protection, known as myelin sheath, alongside nerve fibers themselves. This enigmatic condition, characterized by demyelination and varied clinical manifestations, prompts exploration into its multifaceted etiology and potential therapeutic avenues. Research has revealed a potential connection between Epstein Barr virus (EBV), specifically Epstein Barr Nuclear Antigen 1 (EBNA-1), and MS. The immune response to EBNA-1 antigen triggers the production of anti-EBNA-1 molecules, including IgG that identify a similar amino acid sequence to EBNA-1 in myelin, inadvertently targeting myelin sheath and contributing to MS progression. Currently, no treatment exists for EBNA-1-induced MS apart from symptom management. Addressing this, a novel potential therapeutic avenue utilizing small interference RNAs (siRNA) has been designed. By targeting the conserved EBNA-1 gene sequences in EBV types 1 and 2, five potential siRNAs were identified in our analysis. Thorough evaluations encompassing off-target binding, thermodynamics and secondary structure elucidation, efficacy prediction, siRNA-mRNA sequence binding affinity exploration, melting temperature, and docking of siRNAs with human argonaute protein 2 (AGO2) were conducted to elucidate the siRNAs efficiency. These designed siRNA molecules harnessed promising silencing activity in the EBNA-1 gene encoding the EBNA-1 antigen protein and thus have the potential to mitigate the severity of this dangerous virus.

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.